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feat: expose protocol analysis, OOT export tools; harden for release

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Wire up protocol analysis (parse_protocol_spec, generate_decoder_chain,
get_missing_oot_modules), signal analysis (analyze_iq_file), and OOT
export (generate_oot_skeleton, export_block_to_oot, export_from_flowgraph)
as MCP tools with integration tests.

Security fixes from Hamilton review:
- Remove `from __future__ import annotations` from tool registration
  files (breaks FastMCP schema generation)
- Add blocklist guard to evaluate_expression (was unsandboxed eval)
- Replace string interpolation with base64 encoding in Docker test
  harness (prevents code injection)
- Add try/finally cleanup for temp files and Docker containers
- Replace assert with proper ValueError in flowgraph block creation
- Log OOT auto-discovery failures instead of swallowing silently

Packaging:
- Move entry point to src/gnuradio_mcp/server.py with script entry
  point (uv run gnuradio-mcp)
- Add PyPI metadata (authors, license, classifiers, urls)
- Add MIT LICENSE file
- Rewrite README for current feature set (80+ tools)
- Document single-session limitation
This commit is contained in:
Ryan Malloy 2026-02-20 13:17:11 -07:00
parent d7fda8babb
commit 212832e7e4
14 changed files with 1729 additions and 826 deletions
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This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it
under certain conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands
might be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary.
For more information on this, and how to apply and follow the GNU GPL, see
<https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you
may consider it more useful to permit linking proprietary applications with
the library. If this is what you want to do, use the GNU Lesser General
Public License instead of this License. But first, please read
<https://www.gnu.org/licenses/why-not-lgpl.html>.
MIT License
Copyright (c) 2026 Ryan Malloy
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

319
README.md
View File

@ -1,58 +1,23 @@
# GR-MCP: GNU Radio MCP Server
[![Python Version](https://img.shields.io/badge/python-3.14%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
**GR-MCP** is a FastMCP server for [GNU Radio](https://www.gnuradio.org/) that enables programmatic, automated, and AI-driven creation and control of GNU Radio flowgraphs. It exposes 36 MCP tools for building, modifying, validating, running, and monitoring `.grc` files.
**GR-MCP** is a [FastMCP](https://gofastmcp.com) server for [GNU Radio](https://www.gnuradio.org/) that enables programmatic, automated, and AI-driven creation of GNU Radio flowgraphs. It exposes 80+ MCP tools for building, validating, running, and exporting `.grc` files — plus block development, protocol analysis, and OOT module management.
> **Why GR-MCP?**
> **What can you do with it?**
> - Build and validate flowgraphs programmatically
> - Run flowgraphs in Docker containers with XML-RPC control
> - Adjust variables in real-time without restarting
> - Collect Python code coverage from containerized flowgraphs
> - Integrate with LLMs, automation frameworks, and custom tools
## Features
### Flowgraph Building (15 tools)
Build, edit, and validate `.grc` files programmatically:
- `get_blocks` / `make_block` / `remove_block` - Block management
- `get_block_params` / `set_block_params` - Parameter control
- `get_block_sources` / `get_block_sinks` - Port inspection
- `get_connections` / `connect_blocks` / `disconnect_blocks` - Wiring
- `validate_block` / `validate_flowgraph` / `get_all_errors` - Validation
- `save_flowgraph` - Save to `.grc` file
- `get_all_available_blocks` - List available block types
### Runtime Control (11 tools)
Run flowgraphs in Docker containers with headless QT rendering:
- `launch_flowgraph` - Start a flowgraph in a container (Xvfb + optional VNC)
- `list_containers` / `stop_flowgraph` / `remove_flowgraph` - Container lifecycle
- `connect` / `connect_to_container` / `disconnect` - XML-RPC connection
- `list_variables` / `get_variable` / `set_variable` - Real-time variable control
- `start` / `stop` / `lock` / `unlock` - Flowgraph execution control
- `capture_screenshot` / `get_container_logs` - Visual feedback
- `get_status` - Connection and container status
### Coverage Collection (4 tools)
Collect Python code coverage from containerized flowgraphs:
- `collect_coverage` - Gather coverage data after flowgraph stops
- `generate_coverage_report` - Generate HTML/XML/JSON reports
- `combine_coverage` - Aggregate coverage across multiple runs
- `delete_coverage` - Clean up coverage data
## Requirements
- Python >= 3.14
- GNU Radio (tested with GRC v3.10.12.0)
- Docker (optional, for runtime control features)
- UV package manager
> - Generate custom GNU Radio blocks from natural language descriptions
> - Parse protocol specifications into decoder pipelines
> - Analyze IQ recordings to detect signal characteristics
> - Export blocks to distributable OOT modules
> - Run flowgraphs in Docker containers with real-time variable control
> - Install and manage OOT modules via Docker
## Quickstart
### 1. Clone and setup
### 1. Install
```bash
git clone https://github.com/rsp2k/gr-mcp
@ -63,30 +28,99 @@ uv venv --system-site-packages --python 3.14
uv sync
```
### 2. Configure your MCP client
### 2. Run
Add to Claude Desktop, Cursor, or other MCP client config:
```bash
uv run gnuradio-mcp
```
### 3. Add to your MCP client
**Claude Code:**
```bash
claude mcp add gnuradio-mcp -- uv run --directory /path/to/gr-mcp gnuradio-mcp
```
**Claude Desktop / Cursor / other MCP clients:**
```json
{
"mcpServers": {
"gr-mcp": {
"gnuradio-mcp": {
"command": "uv",
"args": ["--directory", "/path/to/gr-mcp", "run", "main.py"]
"args": ["run", "--directory", "/path/to/gr-mcp", "gnuradio-mcp"]
}
}
}
```
### 3. (Optional) Build Docker images for runtime control
### Requirements
```bash
# Build the runtime image (Xvfb + VNC + ImageMagick)
docker build -f docker/Dockerfile.gnuradio-runtime -t gnuradio-runtime:latest docker/
- Python >= 3.14
- GNU Radio (tested with GRC v3.10.12.0)
- Docker (optional — for runtime control, block testing, OOT builds)
- [uv](https://docs.astral.sh/uv/) package manager
# Build the coverage image (adds python3-coverage)
docker build -f docker/Dockerfile.gnuradio-coverage -t gnuradio-coverage:latest docker/
```
> **Note:** GR-MCP is designed for single-session use. All connected MCP clients share the same flowgraph state. Run one server instance per concurrent session.
## Features
### Flowgraph Building (30 tools)
Build, edit, validate, and export `.grc` files:
| Category | Tools |
|----------|-------|
| Blocks | `make_block`, `remove_block`, `get_blocks` |
| Parameters | `get_block_params`, `set_block_params` |
| Ports | `get_block_sources`, `get_block_sinks` |
| Connections | `connect_blocks`, `disconnect_blocks`, `get_connections` |
| Validation | `validate_block`, `validate_flowgraph`, `get_all_errors` |
| Persistence | `save_flowgraph`, `load_flowgraph` |
| Code Gen | `generate_code` |
| Discovery | `get_all_available_blocks`, `search_blocks`, `get_block_categories` |
| Options | `get_flowgraph_options`, `set_flowgraph_options` |
| Python | `create_embedded_python_block`, `evaluate_expression` |
| Bypass | `bypass_block`, `unbypass_block` |
| Import/Export | `export_flowgraph_data`, `import_flowgraph_data` |
| OOT Paths | `load_oot_blocks`, `add_block_path`, `get_block_paths` |
### Block Development (18 tools, dynamically registered)
Generate, validate, test, and export custom blocks. These tools are registered on-demand via `enable_block_dev_mode` to minimize context usage:
| Category | Tools |
|----------|-------|
| Generation | `generate_sync_block`, `generate_basic_block`, `generate_interp_block`, `generate_decim_block` |
| Validation | `validate_block_code`, `parse_block_prompt` |
| Testing | `test_block_in_docker` |
| Integration | `inject_generated_block` |
| Protocol | `parse_protocol_spec`, `generate_decoder_chain`, `get_missing_oot_modules` |
| Signal | `analyze_iq_file` |
| OOT Export | `generate_oot_skeleton`, `export_block_to_oot`, `export_from_flowgraph` |
| Mode | `enable_block_dev_mode`, `disable_block_dev_mode`, `get_block_dev_mode` |
### Runtime Control (36 tools)
Run flowgraphs in Docker containers with real-time control:
| Category | Tools |
|----------|-------|
| XML-RPC | `connect`, `disconnect`, `get_status`, `list_variables`, `get_variable`, `set_variable` |
| Execution | `start`, `stop`, `lock`, `unlock` |
| ControlPort | `connect_controlport`, `disconnect_controlport`, `get_knobs`, `set_knobs`, `get_knob_properties`, `get_performance_counters`, `post_message` |
| Docker | `launch_flowgraph`, `list_containers`, `stop_flowgraph`, `remove_flowgraph`, `connect_to_container`, `capture_screenshot`, `get_container_logs` |
| Coverage | `collect_coverage`, `generate_coverage_report`, `combine_coverage`, `delete_coverage` |
| OOT Mgmt | `detect_oot_modules`, `install_oot_module`, `list_oot_images`, `remove_oot_image`, `build_multi_oot_image`, `list_combo_images`, `remove_combo_image` |
### MCP Resources
| Resource URI | Description |
|-------------|-------------|
| `oot://directory` | Curated directory of 20 OOT modules (12 preinstalled) |
| `oot://directory/{module}` | Details for a specific OOT module |
| `prompts://block-generation/*` | Block generation patterns and templates |
| `prompts://protocol-analysis/*` | Decoder pipeline guidance |
## Usage Examples
@ -94,111 +128,158 @@ docker build -f docker/Dockerfile.gnuradio-coverage -t gnuradio-coverage:latest
### Building a flowgraph
```python
# Create a signal generator block
# Create blocks
make_block(block_type="analog_sig_source_x", name="sig_source")
make_block(block_type="audio_sink", name="speaker")
# Set parameters
# Configure
set_block_params(block_name="sig_source", params={
"freq": "1000",
"amplitude": "0.5",
"waveform": "analog.GR_COS_WAVE"
})
# Connect blocks
# Wire and save
connect_blocks(
source_block="sig_source", source_port="0",
sink_block="audio_sink", sink_port="0"
sink_block="speaker", sink_port="0"
)
# Validate and save
validate_flowgraph()
save_flowgraph(path="/tmp/my_flowgraph.grc")
```
### Running a flowgraph with runtime control
### Generating a custom block
```python
# Launch in Docker container
enable_block_dev_mode()
generate_sync_block(
name="pm_demod",
description="Phase modulation demodulator",
inputs=[{"dtype": "complex", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
parameters=[{"name": "sensitivity", "dtype": "float", "default": 1.0}],
work_logic="Extract instantaneous phase from complex samples"
)
```
### Protocol analysis to decoder chain
```python
enable_block_dev_mode()
# Parse a protocol spec
protocol = parse_protocol_spec(
"GFSK at 250k baud, deviation: 25khz, preamble 0xAA, sync 0x2DD4"
)
# Generate the decoder pipeline
chain = generate_decoder_chain(protocol=protocol, sample_rate=2000000.0)
# Returns: blocks, connections, variables, missing_oot_modules
```
### Exporting to an OOT module
```python
enable_block_dev_mode()
# Generate block
block = generate_sync_block(name="my_filter", ...)
# Export to distributable OOT module
export_block_to_oot(
generated=block,
module_name="mymodule",
output_dir="/path/to/gr-mymodule",
author="Your Name"
)
# Creates: CMakeLists.txt, python/mymodule/my_filter.py, grc/mymodule_my_filter.block.yml
```
### Runtime control (Docker)
```python
# Launch flowgraph in container
launch_flowgraph(
flowgraph_path="/path/to/flowgraph.py",
name="my-sdr",
xmlrpc_port=8080,
enable_vnc=True # Optional: VNC on port 5900
enable_vnc=True
)
# Connect and control
# Tune in real-time
connect_to_container(name="my-sdr")
list_variables() # See available variables
set_variable(name="freq", value=2.4e9) # Tune in real-time
set_variable(name="freq", value=2.4e9)
# Visual feedback
capture_screenshot(name="my-sdr") # Get QT GUI screenshot
get_container_logs(name="my-sdr") # Check for errors
# Clean up
# Inspect and clean up
capture_screenshot(name="my-sdr")
stop_flowgraph(name="my-sdr")
remove_flowgraph(name="my-sdr")
```
### Collecting code coverage
```python
# Launch with coverage enabled
launch_flowgraph(
flowgraph_path="/path/to/flowgraph.py",
name="coverage-test",
enable_coverage=True
)
# Run your test scenario...
# Then stop (graceful shutdown required for coverage data)
stop_flowgraph(name="coverage-test")
# Collect and report
collect_coverage(name="coverage-test")
generate_coverage_report(name="coverage-test", format="html")
```
## Development
```bash
# Install dev dependencies
uv sync --all-extras
# Run tests
pytest
# Run with coverage
pytest --cov=gnuradio_mcp --cov-report=term-missing
# Pre-commit hooks
pre-commit run --all-files
```
## Architecture
```
main.py # FastMCP app entry point
src/gnuradio_mcp/
├── server.py # FastMCP app entry point
├── models.py # Pydantic models for all tools
├── utils.py # Unique IDs, error formatting
├── oot_catalog.py # Curated OOT module directory
├── middlewares/
│ ├── platform.py # GNU Radio Platform wrapper
│ ├── flowgraph.py # Flowgraph block/connection management
│ ├── block.py # Block parameter/port access
│ ├── flowgraph.py # Block/connection management
│ ├── block.py # Parameter/port access
│ ├── ports.py # Port resolution utilities
│ ├── docker.py # Docker container lifecycle
│ └── xmlrpc.py # XML-RPC variable control
│ ├── xmlrpc.py # XML-RPC variable control
│ ├── thrift.py # ControlPort/Thrift client
│ ├── oot.py # OOT module Docker builds
│ ├── block_generator.py # Code generation for custom blocks
│ ├── oot_exporter.py # Export blocks to OOT modules
│ └── protocol_analyzer.py # Protocol parsing, decoder chains, IQ analysis
└── providers/
├── base.py # PlatformProvider (flowgraph tools)
├── mcp.py # McpPlatformProvider (registers tools)
├── runtime.py # RuntimeProvider (Docker/XML-RPC)
└── mcp_runtime.py # McpRuntimeProvider (registers tools)
├── runtime.py # RuntimeProvider (Docker/XML-RPC/Thrift)
├── mcp_runtime.py # McpRuntimeProvider (registers tools)
├── block_dev.py # BlockDevProvider (generation/analysis)
└── mcp_block_dev.py # McpBlockDevProvider (dynamic registration)
```
**Data flow:** GNU Radio objects → Middlewares (validation/rewrite) → Pydantic Models (serialization) → MCP Tools
## Development
```bash
# Install all dependencies
uv sync --all-extras
# Run tests
pytest
# Run specific test suite
pytest tests/unit/
pytest tests/integration/
# Pre-commit hooks (black, flake8, isort, mypy)
pre-commit run --all-files
```
## Project Status
## Docker Images (Optional)
**Active development.** Core flowgraph building is stable. Runtime control (Docker + XML-RPC) is Phase 1 complete. Coverage collection is functional.
For runtime control and block testing:
Contributions and feedback welcome!
```bash
# Runtime image (Xvfb + VNC + ImageMagick)
docker build -f docker/Dockerfile.gnuradio-runtime -t gnuradio-runtime:latest docker/
# Coverage image (adds python3-coverage)
docker build -f docker/Dockerfile.gnuradio-coverage -t gnuradio-coverage:latest docker/
```
## License
[MIT](LICENSE)

524
docs/block-dev-workflow.md Normal file
View File

@ -0,0 +1,524 @@
# AI-Assisted Block Development Workflow
This document describes the complete workflow for developing custom GNU Radio blocks using GR-MCP's block development tools. The workflow enables rapid iteration from concept to distributable OOT module.
## Overview
GR-MCP provides an AI-assisted development workflow that transforms signal processing requirements into working GNU Radio blocks:
```
Protocol Spec / IQ Recording
│
▼
┌─────────────────────────┐
│ Protocol Analysis │ parse_protocol_spec()
│ Signal Detection │ analyze_iq_file()
└─────────────────────────┘
│
▼
┌─────────────────────────┐
│ Block Generation │ generate_sync_block()
│ Decoder Chains │ generate_decoder_chain()
└─────────────────────────┘
│
▼
┌─────────────────────────┐
│ Validation & Test │ validate_block_code()
│ Docker Testing │ test_block_in_docker()
└─────────────────────────┘
│
▼
┌─────────────────────────┐
│ OOT Export │ export_block_to_oot()
│ Distribution │ generate_oot_skeleton()
└─────────────────────────┘
```
## Enabling Block Dev Mode
Block development tools are dynamically registered to minimize context usage. Enable them when needed:
```python
# Check if enabled
get_block_dev_mode()
# Enable block development tools
enable_block_dev_mode()
# Disable when done
disable_block_dev_mode()
```
---
## Phase 1: Protocol Analysis
Parse natural language protocol specifications into structured models.
### parse_protocol_spec
Extracts modulation, framing, and encoding parameters from protocol descriptions.
```python
result = parse_protocol_spec(
spec_text="""
GFSK signal at 250k baud, deviation: 25khz
Preamble: 0xAA (8 bytes)
Sync word: 0x2D, 0xD4
CRC-16 at end of frame
"""
)
# Returns ProtocolModel with:
# - modulation.scheme = "GFSK"
# - modulation.symbol_rate = 250000.0
# - modulation.deviation = 25000.0
# - framing.preamble = "0xAA"
# - framing.sync_word = "0x2D, 0xD4"
# - encoding.crc = "CRC-16"
```
**Supported Parameters:**
| Category | Parameters |
|----------|------------|
| Modulation | scheme (FSK, GFSK, BPSK, QPSK, OFDM, CSS), symbol_rate, deviation, order |
| Framing | preamble, sync_word, header_format, frame_length |
| Encoding | fec_type, interleaving, whitening, crc |
### generate_decoder_chain
Creates a complete decoder pipeline from a parsed protocol specification.
```python
# Parse protocol first
protocol = parse_protocol_spec("GFSK at 50k baud, deviation: 25khz")
# Generate decoder chain
chain = generate_decoder_chain(
protocol=protocol,
sample_rate=2000000.0
)
# Returns DecoderPipelineModel with:
# - blocks: list of DecoderBlock with block_type, parameters
# - connections: list of (src_block, src_port, dst_block, dst_port)
# - variables: dict of flowgraph variables
# - missing_oot_modules: list of required OOT modules
```
**Generated Blocks by Modulation:**
| Modulation | Blocks Generated |
|------------|------------------|
| FSK/GFSK | low_pass_filter → analog_quadrature_demod_cf → clock_recovery |
| BPSK | costas_loop_cc → constellation_decoder_cb |
| LoRa/CSS | freq_xlating_fir_filter → lora_demod (requires gr-lora_sdr) |
### get_missing_oot_modules
Check which OOT modules are required for a decoder chain.
```python
# Parse a LoRa protocol
protocol = parse_protocol_spec("CSS/LoRa at SF7, 125kHz bandwidth")
# Check missing modules
missing = get_missing_oot_modules(protocol)
# Returns: ["gr-lora_sdr"]
```
---
## Phase 2: Signal Analysis
Analyze IQ recordings to identify signal characteristics.
### analyze_iq_file
Performs FFT-based spectral analysis and signal detection.
```python
result = analyze_iq_file(
file_path="/path/to/recording.cf32",
sample_rate=2000000.0,
dtype="complex64" # or "complex128", "int16"
)
# Returns IQAnalysisResult with:
# - sample_count: int
# - duration_seconds: float
# - power_stats: {min_db, max_db, mean_db, std_db}
# - spectral_features: {peak_frequency, bandwidth_3db, ...}
# - signals_detected: list of detected signal regions
```
**Supported Data Types:**
| Format | dtype Parameter |
|--------|-----------------|
| Complex float32 (GNU Radio default) | `complex64` |
| Complex float64 | `complex128` |
| Interleaved int16 (RTL-SDR) | `int16` |
---
## Phase 3: Block Generation
Generate GNU Radio block code from specifications.
### generate_sync_block
Creates a `gr.sync_block` with 1:1 input/output sample relationship.
```python
result = generate_sync_block(
name="pm_demod",
description="Phase modulation demodulator",
inputs=[{"dtype": "complex", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
parameters=[
{"name": "sensitivity", "dtype": "float", "default": 1.0}
],
work_logic="Extract instantaneous phase from complex samples"
)
# Returns GeneratedBlockCode with:
# - source_code: complete Python block implementation
# - block_name: "pm_demod"
# - block_class: "sync_block"
# - is_valid: bool
# - validation_errors: list[str]
```
**Work Templates:**
Pre-built templates for common operations:
| Template | Description |
|----------|-------------|
| `gain` | Multiply samples by gain factor |
| `add` | Add constant to samples |
| `threshold` | Binary threshold comparison |
```python
# Using a work template
result = generate_sync_block(
name="my_gain",
description="Variable gain",
inputs=[{"dtype": "float", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
parameters=[{"name": "gain", "dtype": "float", "default": 1.0}],
work_template="gain"
)
```
### generate_basic_block
Creates a `gr.basic_block` with custom input/output ratios.
```python
result = generate_basic_block(
name="frame_sync",
description="Frame synchronizer with variable output",
inputs=[{"dtype": "byte", "vlen": 1}],
outputs=[{"dtype": "byte", "vlen": 1}],
parameters=[
{"name": "sync_word", "dtype": "int", "default": 0x2DD4}
],
work_logic="Search for sync word and output aligned frames",
forecast_logic="noutput_items + len(self.buffer)"
)
```
### generate_interp_block / generate_decim_block
Create blocks with fixed interpolation or decimation ratios.
```python
# Interpolating block (2x output samples per input)
interp = generate_interp_block(
name="upsample_2x",
description="2x upsampler with zero-stuffing",
inputs=[{"dtype": "float", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
interpolation=2,
work_logic="Zero-stuff between samples"
)
# Decimating block (4x fewer output samples)
decim = generate_decim_block(
name="downsample_4x",
description="4x downsampler",
inputs=[{"dtype": "float", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
decimation=4,
work_logic="Output every 4th sample"
)
```
### validate_block_code
Static analysis without execution.
```python
result = validate_block_code(source_code=my_block_code)
# Returns ValidationResult with:
# - is_valid: bool
# - errors: list[str] (syntax errors, missing imports)
# - warnings: list[str] (style issues, potential bugs)
```
### test_block_in_docker
Test generated blocks in an isolated container.
```python
result = test_block_in_docker(
source_code=my_block_code,
test_input=[1.0, 2.0, 3.0, 4.0],
expected_output=[2.0, 4.0, 6.0, 8.0], # optional
timeout_seconds=30
)
# Returns BlockTestResult with:
# - passed: bool
# - actual_output: list[float]
# - error_message: str (if failed)
# - execution_time_ms: float
```
---
## Phase 4: OOT Export
Export generated blocks to distributable OOT modules.
### generate_oot_skeleton
Create an empty gr_modtool-compatible module structure.
```python
result = generate_oot_skeleton(
module_name="mymodule",
output_dir="/path/to/gr-mymodule",
author="Your Name",
description="My custom GNU Radio blocks"
)
# Creates:
# gr-mymodule/
# ├── CMakeLists.txt
# ├── python/
# │ └── mymodule/
# │ └── __init__.py
# └── grc/
# └── (empty, for .block.yml files)
```
### export_block_to_oot
Export a generated block to an existing or new OOT module.
```python
# First generate a block
block = generate_sync_block(
name="pm_demod",
description="Phase modulation demodulator",
inputs=[{"dtype": "complex", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
parameters=[{"name": "sensitivity", "dtype": "float", "default": 1.0}]
)
# Export to OOT module
result = export_block_to_oot(
generated=block,
module_name="apollo",
output_dir="/path/to/gr-apollo",
author="Ryan Malloy"
)
# Creates:
# gr-apollo/
# ├── CMakeLists.txt
# ├── python/apollo/
# │ ├── __init__.py
# │ └── pm_demod.py ← Block implementation
# └── grc/
# └── apollo_pm_demod.block.yml ← GRC block definition
```
### export_from_flowgraph
Export an embedded Python block from the current flowgraph.
```python
# After creating an embedded block with create_embedded_python_block()
result = export_from_flowgraph(
block_name="epy_block_0",
module_name="custom",
output_dir="/path/to/gr-custom",
author="Your Name"
)
```
---
## Complete Workflow Example
### Example: Apollo USB PCM Decoder
This example demonstrates the full workflow for creating a decoder for Apollo mission telemetry.
```python
# 1. Enable block dev mode
enable_block_dev_mode()
# 2. Parse the protocol specification
protocol = parse_protocol_spec("""
Apollo Unified S-Band PCM telemetry:
- BPSK subcarrier at 1.024 MHz
- 51.2 kbps bit rate
- Manchester encoding
- Frame: 128 words × 8 bits @ 50 fps
- Frame sync pattern: 0xEB9000
""")
# 3. Generate decoder chain
chain = generate_decoder_chain(
protocol=protocol,
sample_rate=2048000.0 # 2x subcarrier for Nyquist
)
# 4. Generate custom phase demodulator
pm_demod = generate_sync_block(
name="pm_demod",
description="Apollo PM demodulator for 0.133 rad deviation",
inputs=[{"dtype": "complex", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
parameters=[
{"name": "deviation", "dtype": "float", "default": 0.133}
],
work_logic="""
# Extract instantaneous phase
phase = numpy.angle(input_items[0])
# Differentiate to get PM signal
output_items[0][:] = numpy.diff(phase, prepend=phase[0]) * self.deviation
"""
)
# 5. Validate the generated block
validation = validate_block_code(pm_demod.source_code)
if not validation.is_valid:
print(f"Errors: {validation.errors}")
# 6. Test in Docker
test = test_block_in_docker(
source_code=pm_demod.source_code,
test_input=[1+0j, 0+1j, -1+0j, 0-1j], # 90° phase steps
timeout_seconds=30
)
# 7. Export to OOT module
export_block_to_oot(
generated=pm_demod,
module_name="apollo",
output_dir="/home/user/gr-apollo",
author="Ryan Malloy"
)
# 8. Build and install the module
install_oot_module(
git_url="file:///home/user/gr-apollo",
branch="main"
)
```
---
## Three-Tier Development Model
GR-MCP supports three levels of block persistence:
| Tier | Mechanism | Persistence | Use Case |
|------|-----------|-------------|----------|
| 1 | `create_embedded_python_block()` | In .grc file | Rapid iteration |
| 2 | `validate_block_code()` + flowgraph | Memory only | Session testing |
| 3 | `export_block_to_oot()` | File system | Distribution |
**Workflow Progression:**
```
Tier 1: Rapid Iteration
create_embedded_python_block() → modify → test → iterate
│
▼ (satisfied with block)
Tier 2: Validation
validate_block_code() → test_block_in_docker()
│
▼ (ready for distribution)
Tier 3: Export
export_block_to_oot() → install_oot_module()
```
---
## Resources
Block dev mode provides prompt and template resources:
```python
# Access via MCP resources
"prompts://block-generation/sync-block" # gr.sync_block patterns
"prompts://block-generation/basic-block" # gr.basic_block patterns
"prompts://protocol-analysis/decoder-chain" # Decoder pipeline guidance
"templates://block/sync-block" # Python code template
"templates://oot/cmake" # CMakeLists.txt template
"templates://oot/block-yaml" # .block.yml template
```
---
## Tool Reference
### Protocol Analysis Tools
| Tool | Description |
|------|-------------|
| `parse_protocol_spec` | Parse natural language protocol spec → ProtocolModel |
| `generate_decoder_chain` | ProtocolModel → complete decoder pipeline |
| `get_missing_oot_modules` | Check which OOT modules are required |
### Signal Analysis Tools
| Tool | Description |
|------|-------------|
| `analyze_iq_file` | FFT analysis of IQ recordings |
### Block Generation Tools
| Tool | Description |
|------|-------------|
| `generate_sync_block` | Create 1:1 sample processing block |
| `generate_basic_block` | Create variable-ratio block |
| `generate_interp_block` | Create interpolating block |
| `generate_decim_block` | Create decimating block |
| `validate_block_code` | Static code analysis |
| `test_block_in_docker` | Isolated container testing |
| `parse_block_prompt` | Parse natural language → generation params |
### OOT Export Tools
| Tool | Description |
|------|-------------|
| `generate_oot_skeleton` | Create empty module structure |
| `export_block_to_oot` | Export generated block to OOT |
| `export_from_flowgraph` | Export embedded block to OOT |
---
## Related Documentation
- [GRC Runtime Communication](grc-runtime-communication.md) - XML-RPC and ControlPort
- [OOT Catalog](../src/gnuradio_mcp/oot_catalog.py) - Curated OOT module directory

242
docs/grc-runtime-communication.md Normal file
View File

@ -0,0 +1,242 @@
# GRC Runtime Communication with Flowgraph Processes
This document explains how GNU Radio Companion (GRC) communicates with running flowgraph processes and the two mechanisms available for runtime control.
## Key Insight: GRC is a Code Generator, Not a Runtime Controller
GRC runs flowgraphs as **completely separate subprocesses** via `subprocess.Popen()`. It does not have built-in runtime control capabilities.
```
+--------------------+ subprocess.Popen() +---------------------+
| GNU Radio | -----------------------------------> | Generated Python |
| Companion (GRC) | | Flowgraph Script |
| | <----------------------------------- | |
| (Qt/GTK GUI) | stdout/stderr pipe | (gr.top_block) |
+--------------------+ +---------------------+
```
The generated Python script runs independently. To control parameters at runtime, you must use one of the two communication mechanisms described below.
## GRC Execution Flow
```
.grc file (YAML)
|
v Platform.load_and_generate_flow_graph()
Generator (Mako templates)
|
v generator.write()
Python script (with set_*/get_* methods)
|
v ExecFlowGraphThread -> subprocess.Popen()
Running flowgraph process
|
v stdout/stderr piped back to GRC console
```
### Key GRC Execution Files
| File | Purpose |
|------|---------|
| `grc/main.py` | Entry point |
| `grc/gui_qt/components/executor.py` | ExecFlowGraphThread subprocess launcher |
| `grc/core/platform.py` | Block registry, flowgraph loading |
| `grc/core/generator/Generator.py` | Generator factory |
| `grc/workflows/common.py` | Base generator classes |
| `grc/workflows/python_nogui/flow_graph_nogui.py.mako` | Mako template for Python |
---
## Two Runtime Control Mechanisms
### 1. XML-RPC Server (Simple, HTTP-based)
A **block-based approach** - add the `xmlrpc_server` block to your flowgraph to expose GRC variables over HTTP.
| Aspect | Details |
|--------|---------|
| Protocol | HTTP (XML-RPC) |
| Default Port | 8080 |
| Setup | Add `XMLRPC Server` block to flowgraph |
| Naming | `set_varname()` / `get_varname()` |
| Type Support | Basic Python types |
#### How It Works
1. Add `XMLRPC Server` block to flowgraph
2. GRC variables automatically become `set_X()` / `get_X()` methods
3. Connect with any XML-RPC client (Python, C++, curl, etc.)
#### Client Example
```python
import xmlrpc.client
# Connect to running flowgraph
server = xmlrpc.client.ServerProxy('http://localhost:8080')
# Read and write variables
print(server.get_freq()) # Read a variable
server.set_freq(145.5e6) # Set a variable
# Flowgraph control
server.stop() # Stop flowgraph
server.start() # Start flowgraph
server.lock() # Lock flowgraph for modifications
server.unlock() # Unlock flowgraph
```
#### Key Files
| File | Purpose |
|------|---------|
| `gr-blocks/grc/xmlrpc_server.block.yml` | Server block definition |
| `gr-blocks/grc/xmlrpc_client.block.yml` | Client block definition |
| `gr-blocks/examples/xmlrpc/` | Example flowgraphs |
---
### 2. ControlPort/Thrift (Advanced, Binary)
A **configuration-based approach** - blocks register their parameters via `setup_rpc()` in C++ code. See `docs/doxygen/other/ctrlport.dox` for detailed block implementation.
| Aspect | Details |
|--------|---------|
| Protocol | Thrift Binary TCP |
| Default Port | 9090 |
| Setup | Enable in config, blocks call `setup_rpc()` |
| Naming | `block_alias::varname` |
| Type Support | Rich (complex, vectors, PMT types) |
| Metadata | Units, min/max, display hints |
#### Architecture
```
+------------------------------------------------------------------+
| Running Flowgraph Process |
+-----------------------------------------------------------------+
| Block A Block B |
| +------------------+ +------------------+ |
| | setup_rpc() { | | setup_rpc() { | |
| | add_rpc_var( | | add_rpc_var( | |
| | "gain", | | "freq", | |
| | &get_gain, | | &get_freq, | |
| | &set_gain); | | &set_freq); | |
| | } | | } | |
| +--------+---------+ +--------+---------+ |
| | | |
| v v |
| +----------------------------------------------------------+ |
| | rpcserver_thrift (port 9090) | |
| | +-----------------+ +-----------------+ | |
| | | setcallbackmap | | getcallbackmap | | |
| | | "blockA::gain" | | "blockA::gain" | | |
| | | "blockB::freq" | | "blockB::freq" | | |
| | +-----------------+ +-----------------+ | |
| +----------------------------------------------------------+ |
+------------------------------------------------------------------+
^
| Thrift Binary Protocol (TCP)
v
+------------------------------------------------------------------+
| Python Client |
| from gnuradio.ctrlport import GNURadioControlPortClient |
| |
| client = GNURadioControlPortClient(host='localhost', port=9090) |
| knobs = client.getKnobs(['blockA::gain', 'blockB::freq']) |
| client.setKnobs({'blockA::gain': 2.5}) |
+------------------------------------------------------------------+
```
#### Enabling ControlPort
**~/.gnuradio/config.conf:**
```ini
[ControlPort]
on = True
edges_list = True
[thrift]
port = 9090
nthreads = 2
```
#### Client Example
```python
from gnuradio.ctrlport.GNURadioControlPortClient import GNURadioControlPortClient
# Connect to running flowgraph
client = GNURadioControlPortClient(host='localhost', port=9090)
# Get knobs (read values)
knobs = client.getKnobs(['analog_sig_source_0::frequency'])
print(knobs)
# Set knobs (write values)
client.setKnobs({'analog_sig_source_0::frequency': 1500.0})
# Regex-based retrieval - get all frequency knobs
all_freq_knobs = client.getRe(['.*::frequency'])
# Get metadata (units, min, max, description)
props = client.properties(['analog_sig_source_0::frequency'])
print(props['analog_sig_source_0::frequency'].units)
print(props['analog_sig_source_0::frequency'].min)
```
#### GUI Monitoring Tools
- **gr-ctrlport-monitor** - Real-time variable inspection
- **gr-perf-monitorx** - Performance profiling visualization
```bash
gr-ctrlport-monitor localhost 9090
gr-perf-monitorx localhost 9090
```
#### Key Files
| File | Purpose |
|------|---------|
| `gnuradio-runtime/lib/controlport/thrift/gnuradio.thrift` | Thrift IDL definition |
| `gnuradio-runtime/include/gnuradio/rpcserver_thrift.h` | Server implementation |
| `gnuradio-runtime/include/gnuradio/rpcregisterhelpers.h` | Registration templates |
| `gnuradio-runtime/python/gnuradio/ctrlport/GNURadioControlPortClient.py` | Python client |
| `gnuradio-runtime/python/gnuradio/ctrlport/RPCConnectionThrift.py` | Thrift connection |
---
## Comparison: XML-RPC vs ControlPort
| Feature | XML-RPC | ControlPort/Thrift |
|---------|---------|-------------------|
| Setup | Add block to flowgraph | Enable in config.conf |
| Protocol | HTTP | Binary TCP |
| Performance | Slower (text-based) | Faster (binary) |
| Type support | Basic Python types | Complex, vectors, PMT |
| Metadata | None | Units, min/max, hints |
| Tooling | Any HTTP client | Specialized monitors |
| Use case | Simple control | Performance monitoring |
### When to Use Each
**Use XML-RPC when:**
- You need quick, simple remote control
- Integration with web applications
- Language-agnostic client access
- Minimal configuration
**Use ControlPort when:**
- You need performance monitoring
- Working with complex data types
- Block-level control granularity
- Need metadata about parameters
---
## Related Documentation
- `docs/doxygen/other/ctrlport.dox` - Detailed ControlPort block implementation guide
- `gr-blocks/examples/xmlrpc/` - XML-RPC usage examples
- `docs/usage-manual/(exported from wiki) Performance Counters.txt` - Performance monitoring

23
pyproject.toml
View File

@ -5,8 +5,21 @@ build-backend = "setuptools.build_meta"
[project]
name = "gnuradio-mcp"
version = "0.2.0"
description = "A FastMCP server for gnuradio."
description = "MCP server for GNU Radio — build, validate, run, and export flowgraphs programmatically."
readme = "README.md"
license = "MIT"
requires-python = ">=3.14"
authors = [
{name = "Ryan Malloy", email = "ryan@supported.systems"},
]
classifiers = [
"Development Status :: 4 - Beta",
"Intended Audience :: Developers",
"Intended Audience :: Science/Research",
"Programming Language :: Python :: 3.14",
"Topic :: Scientific/Engineering",
"Topic :: Communications :: Ham Radio",
]
dependencies = [
"pydantic>=2.12",
"fastmcp>=3.0.0b1",
@ -25,6 +38,14 @@ dev = [
"pre-commit>=4.5",
]
[project.urls]
Homepage = "https://github.com/rsp2k/gr-mcp"
Repository = "https://github.com/rsp2k/gr-mcp"
Issues = "https://github.com/rsp2k/gr-mcp/issues"
[project.scripts]
gnuradio-mcp = "gnuradio_mcp.server:main"
[tool.pytest.ini_options]
# Tell pytest where to find the package
pythonpath = ["src", "."]

46
src/gnuradio_mcp/middlewares/flowgraph.py
View File

@ -46,7 +46,8 @@ class FlowGraphMiddleware(ElementMiddleware):
) -> BlockModel:
block_name = block_name or get_unique_id(self._flowgraph.blocks, block_type)
block = self._flowgraph.new_block(block_type)
assert block is not None, f"Failed to create block: {block_type}"
if block is None:
raise ValueError(f"Failed to create block: {block_type!r} — unknown block type or internal GRC error")
set_block_name(block, block_name)
return BlockModel.from_block(block)
@ -213,7 +214,8 @@ class FlowGraphMiddleware(ElementMiddleware):
"""
block_name = block_name or get_unique_id(self._flowgraph.blocks, "epy_block")
block = self._flowgraph.new_block("epy_block")
assert block is not None, "Failed to create epy_block"
if block is None:
raise ValueError("Failed to create epy_block — embedded Python block type not available")
set_block_name(block, block_name)
block.params["_source_code"].set_value(source_code)
block.rewrite()
@ -223,12 +225,50 @@ class FlowGraphMiddleware(ElementMiddleware):
# Gap 6: Expression Evaluation
# ──────────────────────────────────────────
# Patterns that indicate code execution attempts rather than expressions
_BLOCKED_PATTERNS = (
"__import__",
"exec(",
"eval(",
"compile(",
"open(",
"subprocess",
"os.system",
"os.popen",
"os.exec",
"os.spawn",
"os.remove",
"os.unlink",
"os.rmdir",
"shutil.",
"importlib",
"builtins",
"globals()",
"locals()",
"getattr(",
"setattr(",
"delattr(",
"breakpoint(",
)
def evaluate_expression(self, expr: str) -> Any:
"""Evaluate a Python expression in the flowgraph's namespace.
The namespace includes all imports, variables, parameters, and
modules defined in the flowgraph.
modules defined in the flowgraph. Intended for arithmetic, variable
lookups, and GRC expressions (e.g. "samp_rate / 2", "2 ** sf").
WARNING: This delegates to GRC's built-in evaluator which ultimately
calls Python eval(). A blocklist rejects obviously dangerous patterns,
but this is NOT a sandbox. Do not expose to untrusted inputs.
"""
expr_lower = expr.lower().replace(" ", "")
for pattern in self._BLOCKED_PATTERNS:
if pattern.lower().replace(" ", "") in expr_lower:
raise ValueError(
f"Expression rejected: contains blocked pattern {pattern!r}. "
f"evaluate_expression is for arithmetic and variable lookups only."
)
fg = self._flowgraph
fg.rewrite()
return fg.evaluate(expr)

2
src/gnuradio_mcp/middlewares/protocol_analyzer.py
View File

@ -92,7 +92,7 @@ class ProtocolAnalyzerMiddleware:
def _refresh_available_blocks(self):
"""Update list of available blocks from platform."""
if self._platform_mw:
for block_type in self._platform_mw.block_types:
for block_type in self._platform_mw.blocks:
self._available_blocks.add(block_type.key)
# ──────────────────────────────────────────

6
src/gnuradio_mcp/providers/base.py
View File

@ -213,7 +213,11 @@ class PlatformProvider:
##############################################
def evaluate_expression(self, expr: str) -> Any:
"""Evaluate a Python expression in the flowgraph's namespace."""
"""Evaluate a Python expression in the flowgraph's namespace.
For arithmetic and variable lookups (e.g. "samp_rate / 2", "2 ** sf").
Dangerous patterns (import, exec, open, os, subprocess) are blocked.
"""
return self._flowgraph_mw.evaluate_expression(expr)
##############################################

330
src/gnuradio_mcp/providers/block_dev.py
View File

@ -3,16 +3,21 @@
Orchestrates block generation, validation, testing, and export operations.
"""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any
from gnuradio_mcp.middlewares.block_generator import BlockGeneratorMiddleware
from gnuradio_mcp.middlewares.oot_exporter import OOTExporterMiddleware
from gnuradio_mcp.middlewares.protocol_analyzer import ProtocolAnalyzerMiddleware
from gnuradio_mcp.models import (
BlockParameter,
BlockTestResult,
DecoderPipelineModel,
GeneratedBlockCode,
IQAnalysisResult,
OOTExportResult,
OOTSkeletonResult,
ProtocolModel,
SignatureItem,
ValidationResult,
)
@ -33,18 +38,23 @@ class BlockDevProvider:
def __init__(
self,
flowgraph_mw: FlowGraphMiddleware | None = None,
docker_mw: DockerMiddleware | None = None,
flowgraph_mw: "FlowGraphMiddleware | None" = None,
docker_mw: "DockerMiddleware | None" = None,
platform_mw=None,
):
"""Initialize the block development provider.
Args:
flowgraph_mw: Flowgraph middleware for block injection
docker_mw: Docker middleware for isolated testing
platform_mw: Platform middleware for block availability
"""
self._flowgraph_mw = flowgraph_mw
self._docker_mw = docker_mw
self._platform_mw = platform_mw
self._generator = BlockGeneratorMiddleware(flowgraph_mw)
self._protocol_analyzer = ProtocolAnalyzerMiddleware(platform_mw)
self._oot_exporter = OOTExporterMiddleware(flowgraph_mw)
# ──────────────────────────────────────────
# Block Generation
@ -302,23 +312,26 @@ class BlockDevProvider:
source_code: str,
test_input: list[float],
) -> str:
"""Generate a test flowgraph that exercises the block."""
# Escape the source code for embedding
escaped_code = source_code.replace("\\", "\\\\").replace('"""', '\\"\\"\\"')
"""Generate a test flowgraph that exercises the block.
Uses base64 encoding to safely embed user-supplied source code,
avoiding string interpolation injection risks.
"""
import base64
encoded = base64.b64encode(source_code.encode("utf-8")).decode("ascii")
return f'''#!/usr/bin/env python3
"""Auto-generated test flowgraph for block testing."""
import base64
import json
import numpy as np
from gnuradio import gr, blocks
import json
import sys
# Embedded block source
BLOCK_CODE = """{escaped_code}"""
# Execute the block code to define the class
exec(BLOCK_CODE)
# Decode embedded block source (base64-encoded to prevent injection)
_block_code = base64.b64decode("{encoded}").decode("utf-8")
exec(_block_code)
class test_flowgraph(gr.top_block):
def __init__(self):
@ -357,10 +370,13 @@ if __name__ == "__main__":
) -> BlockTestResult:
"""Execute the test flowgraph in a Docker container."""
import json
import os
import tempfile
import time
start_time = time.time()
script_path = None
container = None
try:
# Write flowgraph to temp file
@ -371,12 +387,13 @@ if __name__ == "__main__":
script_path = f.name
# Run in Docker (use local gnuradio-runtime image)
# remove=False so we can capture logs even on failure
container = self._docker_mw._client.containers.run(
image="gnuradio-runtime:latest",
command=f"python3 /test/script.py",
command="python3 /test/script.py",
volumes={script_path: {"bind": "/test/script.py", "mode": "ro"}},
detach=True,
remove=True,
remove=False,
)
# Wait for completion
@ -418,6 +435,19 @@ if __name__ == "__main__":
error=str(e),
execution_time_ms=(time.time() - start_time) * 1000,
)
finally:
# Clean up temp file
if script_path:
try:
os.unlink(script_path)
except OSError:
pass
# Clean up container
if container:
try:
container.remove(force=True)
except Exception:
pass
def _compare_outputs(
self,
@ -492,3 +522,271 @@ if __name__ == "__main__":
def has_flowgraph(self) -> bool:
"""Check if flowgraph injection is available."""
return self._flowgraph_mw is not None
# ──────────────────────────────────────────
# Protocol Analysis
# ──────────────────────────────────────────
def parse_protocol_spec(self, spec_text: str) -> ProtocolModel:
"""Parse a natural language protocol specification.
Extracts modulation, framing, and encoding parameters from
a text description of a wireless protocol.
Args:
spec_text: Natural language protocol description, e.g.:
"GFSK modulation at 250 kbaud, ±160 kHz deviation,
with 32-bit preamble 0xAAAAAAAA and sync word 0x7E,
CRC-16 for error detection"
Returns:
ProtocolModel with extracted parameters including:
- modulation: scheme, symbol_rate, deviation, bandwidth
- framing: preamble, sync_word, crc_type
- encoding: fec_type, fec_rate, whitening
Example:
protocol = parse_protocol_spec('''
Apollo USB PCM Telemetry:
- BPSK on 1.024 MHz subcarrier
- 51.2 kbps bit rate
- 32-bit frame sync word
- 128-word frames at 50 fps
''')
"""
return self._protocol_analyzer.parse_protocol_spec(spec_text)
def generate_decoder_chain(
self,
protocol: ProtocolModel | dict[str, Any],
sample_rate: float | None = None,
) -> DecoderPipelineModel:
"""Generate a decoder pipeline from a protocol specification.
Creates a chain of GNU Radio blocks appropriate for decoding
the specified protocol, including filtering, demodulation,
symbol recovery, and packet processing.
Args:
protocol: Protocol spec from parse_protocol_spec() or dict
sample_rate: Sample rate override (uses protocol spec if None)
Returns:
DecoderPipelineModel with:
- blocks: List of DecoderBlock with block_type, params
- connections: List of (src, port, dst, port) tuples
- variables: Flowgraph variables to set
- is_complete: True if all blocks are available
- missing_blocks: List of unavailable block types
Example:
protocol = parse_protocol_spec("GFSK at 250 kbaud...")
pipeline = generate_decoder_chain(protocol, sample_rate=2e6)
# Returns blocks: [tuner, demod, timing, slicer, correlator, ...]
"""
# Convert dict to ProtocolModel if needed
if isinstance(protocol, dict):
protocol = ProtocolModel(**protocol)
return self._protocol_analyzer.generate_decoder_chain(
protocol=protocol,
sample_rate=sample_rate,
)
def analyze_iq_file(
self,
file_path: str,
sample_rate: float | None = None,
fft_size: int = 1024,
threshold_db: float = -40,
) -> IQAnalysisResult:
"""Analyze an IQ capture file for signals and modulation.
Performs spectral analysis to detect signals and attempts
automatic modulation classification using statistical features.
Args:
file_path: Path to IQ file (complex64 raw or stereo WAV)
sample_rate: Sample rate if not in file metadata
fft_size: FFT size for spectral analysis (default 1024)
threshold_db: Power threshold for signal detection
Returns:
IQAnalysisResult with:
- signals_detected: List of SignalDetection (center_freq, bandwidth, power)
- modulation_results: ModulationDetectionResult for each signal
- noise_floor_db: Estimated noise floor
- peak_power_db: Maximum signal power
Example:
result = analyze_iq_file(
"/tmp/capture.raw",
sample_rate=2e6,
threshold_db=-30
)
for signal in result.signals_detected:
print(f"Signal at {signal.center_frequency/1e3:.1f} kHz")
"""
return self._protocol_analyzer.analyze_iq_file(
file_path=file_path,
sample_rate=sample_rate,
fft_size=fft_size,
threshold_db=threshold_db,
)
def get_missing_oot_modules(
self,
pipeline: DecoderPipelineModel | dict[str, Any],
) -> list[str]:
"""Identify OOT modules needed for a pipeline.
Maps missing blocks in a decoder pipeline to the OOT modules
that provide them. Useful for determining what to install.
Args:
pipeline: DecoderPipelineModel from generate_decoder_chain()
Returns:
List of OOT module names (e.g., ["gr-lora_sdr", "gr-satellites"])
"""
if isinstance(pipeline, dict):
pipeline = DecoderPipelineModel(**pipeline)
return self._protocol_analyzer.get_missing_oot_modules(pipeline)
# ──────────────────────────────────────────
# OOT Module Export
# ──────────────────────────────────────────
def generate_oot_skeleton(
self,
module_name: str,
output_dir: str,
author: str = "gr-mcp",
description: str = "",
) -> OOTSkeletonResult:
"""Generate an empty OOT module structure.
Creates the directory structure and CMake files for a new
GNU Radio OOT module. Blocks can be added later with
export_block_to_oot().
Args:
module_name: Module name (e.g., "custom" for gr-custom)
output_dir: Base directory for the module
author: Author name for copyright headers
description: Module description
Returns:
OOTSkeletonResult with:
- success: True if skeleton was created
- module_name: Sanitized module name
- output_dir: Absolute path to module
- structure: Dict of created directories and files
- next_steps: Instructions for building
Example:
result = generate_oot_skeleton(
module_name="apollo",
output_dir="/tmp/gr-apollo",
author="Ryan Malloy",
description="Apollo USB signal decoders"
)
"""
return self._oot_exporter.generate_oot_skeleton(
module_name=module_name,
output_dir=output_dir,
author=author,
description=description,
)
def export_block_to_oot(
self,
generated: GeneratedBlockCode | dict[str, Any],
module_name: str,
output_dir: str,
author: str = "gr-mcp",
) -> OOTExportResult:
"""Export a generated block to an OOT module.
Creates or updates an OOT module with the given block.
If the module doesn't exist, creates the skeleton first.
Args:
generated: GeneratedBlockCode from generate_*() or dict
module_name: Module name (e.g., "custom")
output_dir: Base directory for the module
author: Author name for copyright headers
Returns:
OOTExportResult with:
- success: True if export succeeded
- module_name: Final module name
- block_name: Final block name
- files_created: List of created file paths
- build_ready: True if module can be built
Example:
# Generate a block
block = generate_sync_block(
name="pm_demod",
description="Phase demodulator",
inputs=[{"dtype": "complex", "vlen": 1}],
outputs=[{"dtype": "float", "vlen": 1}],
parameters=[{"name": "sensitivity", "dtype": "float", "default": 1.0}]
)
# Export to OOT module
result = export_block_to_oot(
generated=block,
module_name="apollo",
output_dir="/tmp/gr-apollo"
)
# Creates: python/apollo/pm_demod.py, grc/apollo_pm_demod.block.yml
"""
# Convert dict to GeneratedBlockCode if needed
if isinstance(generated, dict):
generated = GeneratedBlockCode(**generated)
return self._oot_exporter.export_block_to_oot(
generated=generated,
module_name=module_name,
output_dir=output_dir,
author=author,
)
def export_from_flowgraph(
self,
block_name: str,
module_name: str,
output_dir: str,
author: str = "gr-mcp",
) -> OOTExportResult:
"""Export an embedded block from the current flowgraph.
Extracts the source code from an epy_block in the flowgraph
and exports it to a full OOT module.
Args:
block_name: Name of the epy_block in the flowgraph
module_name: Target module name
output_dir: Base directory for the module
author: Author name
Returns:
OOTExportResult with status and file paths.
Example:
# After creating an epy_block via create_embedded_python_block()
result = export_from_flowgraph(
block_name="my_gain_0",
module_name="custom",
output_dir="/tmp/gr-custom"
)
"""
return self._oot_exporter.export_from_flowgraph(
block_name=block_name,
module_name=module_name,
output_dir=output_dir,
author=author,
)

28
src/gnuradio_mcp/providers/mcp_block_dev.py
View File

@ -4,8 +4,6 @@ Follows the dynamic registration pattern from McpRuntimeProvider to
minimize context usage when block development features aren't needed.
"""
from __future__ import annotations
import logging
import os
from typing import Any, Callable
@ -106,11 +104,20 @@ class McpBlockDevProvider:
- validate_block_code: Static code analysis
- test_block_in_docker: Isolated testing (if Docker available)
- inject_block: Add generated block to flowgraph
- parse_protocol_spec: Extract protocol params from description
- generate_decoder_chain: Generate block pipeline from protocol
- analyze_iq_file: Detect signals and modulation in IQ captures
- get_missing_oot_modules: Identify OOT modules for pipeline
- generate_oot_skeleton: Create empty OOT module structure
- export_block_to_oot: Export generated block to OOT module
- export_from_flowgraph: Export epy_block to OOT module
Use this when you need to:
- Generate custom signal processing blocks
- Create protocol-specific decoders
- Build and test new DSP algorithms
- Analyze captured signals and auto-generate decoders
- Export blocks to distributable OOT modules
"""
if self._block_dev_enabled:
return BlockDevModeStatus(
@ -190,6 +197,20 @@ class McpBlockDevProvider:
if p.has_flowgraph:
self._add_tool("inject_generated_block", p.inject_block)
# Protocol analysis tools (Phase 3)
self._add_tool("parse_protocol_spec", p.parse_protocol_spec)
self._add_tool("generate_decoder_chain", p.generate_decoder_chain)
self._add_tool("get_missing_oot_modules", p.get_missing_oot_modules)
# Signal analysis tools (Phase 4)
self._add_tool("analyze_iq_file", p.analyze_iq_file)
# OOT export tools (Phase 5)
self._add_tool("generate_oot_skeleton", p.generate_oot_skeleton)
self._add_tool("export_block_to_oot", p.export_block_to_oot)
if p.has_flowgraph:
self._add_tool("export_from_flowgraph", p.export_from_flowgraph)
def _unregister_block_dev_tools(self):
"""Remove all dynamically registered block dev tools."""
for name in list(self._block_dev_tools.keys()):
@ -503,6 +524,7 @@ class McpBlockDevProvider:
cls,
mcp_instance: FastMCP,
flowgraph_mw=None,
platform_mw=None,
auto_enable: bool = False,
) -> McpBlockDevProvider:
"""Factory: create provider with optional Docker support.
@ -510,6 +532,7 @@ class McpBlockDevProvider:
Args:
mcp_instance: FastMCP app instance
flowgraph_mw: Optional FlowGraphMiddleware for block injection
platform_mw: Optional PlatformMiddleware for block availability
auto_enable: Register block dev tools at startup
Returns:
@ -519,5 +542,6 @@ class McpBlockDevProvider:
provider = BlockDevProvider(
flowgraph_mw=flowgraph_mw,
docker_mw=docker_mw,
platform_mw=platform_mw,
)
return cls(mcp_instance, provider, auto_enable=auto_enable)

2
src/gnuradio_mcp/providers/mcp_runtime.py
View File

@ -1,5 +1,3 @@
from __future__ import annotations
import logging
from typing import Any, Callable

33
main.py → src/gnuradio_mcp/server.py
View File

@ -1,4 +1,9 @@
from __future__ import annotations
"""GR-MCP server entry point.
NOTE: This server uses module-level global state (platform, flowgraph).
It is designed for SINGLE-SESSION use only. Concurrent MCP clients sharing
the same server instance will see (and mutate) the same flowgraph state.
"""
import logging
import os
@ -39,13 +44,29 @@ for path in oot_candidates:
try:
result = pmw.add_block_path(path)
if result.blocks_added > 0:
logger.info(f"OOT: +{result.blocks_added} blocks from {path}")
except Exception:
pass
logger.info("OOT: +%d blocks from %s", result.blocks_added, path)
except Exception as e:
logger.warning("Failed to load OOT from %s: %s", path, e)
McpPlatformProvider.from_platform_middleware(app, pmw)
McpRuntimeProvider.create(app)
McpBlockDevProvider.create(app, auto_enable=True) # Tools always available
McpBlockDevProvider.create(app, platform_mw=pmw, auto_enable=True)
def main():
"""Entry point for gnuradio-mcp server."""
import sys
try:
from importlib.metadata import version
package_version = version("gnuradio-mcp")
except Exception:
package_version = "dev"
print(f"gnuradio-mcp v{package_version}", file=sys.stderr)
app.run()
if __name__ == "__main__":
app.run()
main()

303
tests/integration/test_mcp_block_dev.py
View File

@ -270,3 +270,306 @@ class TestToolNotAvailableWhenDisabled:
assert "get_block_dev_mode" in tool_names
assert "enable_block_dev_mode" in tool_names
assert "disable_block_dev_mode" in tool_names
class TestProtocolAnalysisTools:
"""Tests for protocol analysis and signal detection tools."""
@pytest.mark.asyncio
async def test_parse_protocol_spec_gfsk(self, mcp_app):
"""Parse a GFSK protocol specification."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
# Note: Parser expects "Xk baud" and "deviation: Xkhz" format
result = await client.call_tool(
name="parse_protocol_spec",
arguments={
"spec_text": "GFSK signal at 250k baud, deviation: 160khz"
},
)
assert result.data.modulation.scheme == "GFSK"
assert result.data.modulation.symbol_rate == 250000.0
assert result.data.modulation.deviation == 160000.0
@pytest.mark.asyncio
async def test_parse_protocol_spec_lora(self, mcp_app):
"""Parse a LoRa/CSS protocol specification."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
result = await client.call_tool(
name="parse_protocol_spec",
arguments={
"spec_text": """
Protocol: LoRa
Modulation: CSS (Chirp Spread Spectrum)
Bandwidth: 125 kHz
Preamble: 8 upchirps
Sync word: 0x34
"""
},
)
assert result.data.name == "LoRa"
assert result.data.modulation.scheme == "CSS"
assert result.data.modulation.bandwidth == 125000.0
assert result.data.framing is not None
assert result.data.framing.sync_word == "0x34"
assert result.data.framing.preamble_length == 8
@pytest.mark.asyncio
async def test_parse_protocol_spec_with_fec(self, mcp_app):
"""Parse protocol with FEC encoding."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
result = await client.call_tool(
name="parse_protocol_spec",
arguments={
"spec_text": """
FSK at 9600 baud
Hamming FEC with rate 3/4
Data whitening enabled
"""
},
)
assert result.data.modulation.scheme == "FSK"
assert result.data.encoding is not None
assert result.data.encoding.fec_type == "hamming"
assert result.data.encoding.fec_rate == "3/4"
assert result.data.encoding.whitening is True
@pytest.mark.asyncio
async def test_generate_decoder_chain_gfsk(self, mcp_app):
"""Generate decoder chain for GFSK signal."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
# First parse a protocol (using parser's expected format)
parse_result = await client.call_tool(
name="parse_protocol_spec",
arguments={
"spec_text": "GFSK at 50k baud, deviation: 25khz"
},
)
# Generate decoder chain from parsed protocol
# Note: Use structured_content (already a dict) for passing to next tool
result = await client.call_tool(
name="generate_decoder_chain",
arguments={
"protocol": parse_result.structured_content,
"sample_rate": 2000000.0,
},
)
# Should have demodulation blocks
block_types = [b.block_type for b in result.data.blocks]
assert "analog_quadrature_demod_cf" in block_types
assert "digital_symbol_sync_ff" in block_types
assert "digital_binary_slicer_fb" in block_types
# Should have connections
assert len(result.data.connections) >= 2
# Should have sample rate variable
# Note: Access via structured_content since data wraps nested objects
assert result.structured_content["variables"]["samp_rate"] == 2000000.0
@pytest.mark.asyncio
async def test_generate_decoder_chain_with_framing(self, mcp_app):
"""Generate decoder with sync word correlation."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
# Use parser's expected format for baud rate
parse_result = await client.call_tool(
name="parse_protocol_spec",
arguments={
"spec_text": """
FSK at 9.6k baud
Sync word: 0x2DD4
Preamble: 10101010 pattern
"""
},
)
result = await client.call_tool(
name="generate_decoder_chain",
arguments={"protocol": parse_result.structured_content},
)
# Should have correlator for sync word
block_types = [b.block_type for b in result.data.blocks]
assert "digital_correlate_access_code_tag_bb" in block_types
@pytest.mark.asyncio
async def test_get_missing_oot_modules_lora(self, mcp_app):
"""Identify missing OOT modules for LoRa decoder."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
# Parse LoRa protocol (requires gr-lora_sdr)
parse_result = await client.call_tool(
name="parse_protocol_spec",
arguments={
"spec_text": """
Protocol: LoRa
CSS modulation
Bandwidth: 125 kHz
"""
},
)
pipeline_result = await client.call_tool(
name="generate_decoder_chain",
arguments={"protocol": parse_result.structured_content},
)
# Check for missing OOT modules
result = await client.call_tool(
name="get_missing_oot_modules",
arguments={"pipeline": pipeline_result.structured_content},
)
# LoRa blocks require gr-lora_sdr OOT module
# The pipeline should indicate lora_sdr_demod is missing
# which maps to gr-lora_sdr module
# (Only if not installed - test checks the mapping works)
assert isinstance(result.data, list)
@pytest.mark.asyncio
async def test_protocol_analysis_tools_registered(self, mcp_app):
"""Verify protocol analysis tools are registered when enabled."""
async with Client(mcp_app) as client:
result = await client.call_tool(name="enable_block_dev_mode")
tool_names = result.data.tools_registered
# Protocol analysis tools (Phase 3)
assert "parse_protocol_spec" in tool_names
assert "generate_decoder_chain" in tool_names
assert "get_missing_oot_modules" in tool_names
# Signal analysis tools (Phase 4)
assert "analyze_iq_file" in tool_names
# OOT export tools (Phase 5)
assert "generate_oot_skeleton" in tool_names
assert "export_block_to_oot" in tool_names
class TestOOTExportTools:
"""Tests for OOT module export workflow."""
@pytest.mark.asyncio
async def test_generate_oot_skeleton(self, mcp_app, tmp_path):
"""Generate an empty OOT module skeleton."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
output_dir = str(tmp_path / "gr-test")
result = await client.call_tool(
name="generate_oot_skeleton",
arguments={
"module_name": "test",
"output_dir": output_dir,
"author": "Test Author",
"description": "Test module",
},
)
assert result.data.success is True
assert result.data.module_name == "test"
# Check files were created
assert (tmp_path / "gr-test" / "CMakeLists.txt").exists()
assert (tmp_path / "gr-test" / "python" / "test" / "__init__.py").exists()
assert (tmp_path / "gr-test" / "grc" / "CMakeLists.txt").exists()
@pytest.mark.asyncio
async def test_export_block_to_oot(self, mcp_app, tmp_path):
"""Export a generated block to OOT module."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
# First generate a block
block_result = await client.call_tool(
name="generate_sync_block",
arguments={
"name": "my_gain",
"description": "Multiply by gain",
"inputs": [{"dtype": "float", "vlen": 1}],
"outputs": [{"dtype": "float", "vlen": 1}],
"parameters": [{"name": "gain", "dtype": "float", "default": 1.0}],
"work_template": "gain",
},
)
# Export to OOT
output_dir = str(tmp_path / "gr-custom")
result = await client.call_tool(
name="export_block_to_oot",
arguments={
"generated": block_result.structured_content,
"module_name": "custom",
"output_dir": output_dir,
"author": "Test Author",
},
)
assert result.data.success is True
assert result.data.module_name == "custom"
assert result.data.block_name == "my_gain"
# Check block files exist
assert (tmp_path / "gr-custom" / "python" / "custom" / "my_gain.py").exists()
assert (tmp_path / "gr-custom" / "grc" / "custom_my_gain.block.yml").exists()
@pytest.mark.asyncio
async def test_export_full_workflow(self, mcp_app, tmp_path):
"""Full workflow: parse protocol → generate chain → export blocks."""
async with Client(mcp_app) as client:
await client.call_tool(name="enable_block_dev_mode")
# Generate a custom block for the protocol
block_result = await client.call_tool(
name="generate_sync_block",
arguments={
"name": "pm_demod",
"description": "Phase demodulator for Apollo USB",
"inputs": [{"dtype": "complex", "vlen": 1}],
"outputs": [{"dtype": "float", "vlen": 1}],
"parameters": [{"name": "sensitivity", "dtype": "float", "default": 1.0}],
"work_logic": "output_items[0][:] = numpy.angle(input_items[0]) * self.sensitivity",
},
)
assert block_result.data.is_valid is True
# Export to OOT module
output_dir = str(tmp_path / "gr-apollo")
result = await client.call_tool(
name="export_block_to_oot",
arguments={
"generated": block_result.structured_content,
"module_name": "apollo",
"output_dir": output_dir,
},
)
assert result.data.success is True
assert result.data.build_ready is True
# Verify the exported Python source contains our work logic
block_py = tmp_path / "gr-apollo" / "python" / "apollo" / "pm_demod.py"
assert block_py.exists()
content = block_py.read_text()
assert "numpy.angle" in content
assert "sensitivity" in content

2
tests/integration/test_server.py
View File

@ -1,7 +1,7 @@
import pytest
from fastmcp import Client
from main import app as mcp_app
from gnuradio_mcp.server import app as mcp_app
@pytest.fixture