Ryan Malloy
e087a3b7a0
Implement proper MCP resource protocol for image access, eliminating the need
for clients to handle local file paths and enabling seamless image integration.
Key Features:
• MCP Resource Endpoint: pdf-image://{image_id} for direct image access
• extract_images(): Returns resource_uri field with MCP resource links
• pdf_to_markdown(): Embeds resource URIs in markdown image references
• Automatic MIME type detection (image/png, image/jpeg)
• Seamless client integration without file path handling
Benefits:
✅ Direct image access via MCP resource protocol
✅ No local file path dependencies for MCP clients
✅ Proper MIME type handling for image display
✅ Clean markdown with working image links
✅ Standards-compliant MCP resource implementation
Response Format Enhancement:
+ "resource_uri": "pdf-image://page_1_image_0"
+ Works in markdown: \
+ MIME Type: image/png or image/jpeg
+ Direct client access without file system dependencies
This resolves the limitation where extracted images were only available
as local file paths, making them truly accessible to MCP clients
through the standardized resource protocol.
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
137 lines
4.5 KiB
Markdown
137 lines
4.5 KiB
Markdown
# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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MCP PDF Tools is a FastMCP server that provides comprehensive PDF processing capabilities including text extraction, table extraction, OCR, image extraction, and format conversion. The server is built on the FastMCP framework and provides intelligent method selection with automatic fallbacks.
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## Development Commands
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### Environment Setup
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```bash
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# Install with development dependencies
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uv sync --dev
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# Install system dependencies (Ubuntu/Debian)
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sudo apt-get install tesseract-ocr tesseract-ocr-eng poppler-utils ghostscript python3-tk default-jre-headless
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```
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### Testing
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```bash
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# Run all tests
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uv run pytest
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# Run with coverage
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uv run pytest --cov=mcp_pdf_tools
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# Run specific test file
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uv run pytest tests/test_server.py
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# Run specific test
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uv run pytest tests/test_server.py::TestTextExtraction::test_extract_text_success
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```
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### Code Quality
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```bash
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# Format code
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uv run black src/ tests/ examples/
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# Lint code
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uv run ruff check src/ tests/ examples/
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# Type checking
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uv run mypy src/
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```
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### Running the Server
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```bash
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# Run MCP server directly
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uv run mcp-pdf-tools
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# Verify installation
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uv run python examples/verify_installation.py
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# Test with sample PDF
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uv run python examples/test_pdf_tools.py /path/to/test.pdf
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```
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### Building and Distribution
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```bash
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# Build package
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uv build
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# Upload to PyPI (requires credentials)
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uv publish
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```
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## Architecture
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### Core Components
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- **`src/mcp_pdf_tools/server.py`**: Main server implementation with all PDF processing tools
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- **FastMCP Framework**: Uses FastMCP for MCP protocol implementation
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- **Multi-library approach**: Integrates PyMuPDF, pdfplumber, pypdf, Camelot, Tabula, and Tesseract
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### Tool Categories
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1. **Text Extraction**: `extract_text` - Intelligent method selection (PyMuPDF, pdfplumber, pypdf)
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2. **Table Extraction**: `extract_tables` - Auto-fallback through Camelot → pdfplumber → Tabula
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3. **OCR Processing**: `ocr_pdf` - Tesseract with preprocessing options
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4. **Document Analysis**: `is_scanned_pdf`, `get_document_structure`, `extract_metadata`
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5. **Format Conversion**: `pdf_to_markdown` - Clean markdown with MCP resource URIs for images
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6. **Image Processing**: `extract_images` - Extract images with MCP resource URIs for direct client access
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### MCP Client-Friendly Design
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**Optimized for MCP Context Management:**
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- **Image Processing**: `extract_images` and `pdf_to_markdown` return MCP resource URIs for direct image access
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- **Resource URIs**: Images accessible via `pdf-image://{image_id}` protocol for seamless client integration
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- **Prevents Context Overflow**: Avoids verbose base64 output that fills client message windows
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- **File-Based Storage**: Images saved to cache with metadata including file paths and human-readable sizes
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- **Direct Access**: MCP clients can fetch images directly using resource URIs
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### Intelligent Fallbacks
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The server implements smart fallback mechanisms:
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- Text extraction automatically detects scanned PDFs and suggests OCR
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- Table extraction tries multiple methods until tables are found
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- All operations include comprehensive error handling with helpful hints
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### Dependencies Management
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Critical system dependencies:
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- **Tesseract OCR**: Required for `ocr_pdf` functionality
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- **Java**: Required for Tabula table extraction
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- **Ghostscript**: Required for Camelot table extraction
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- **Poppler**: Required for PDF to image conversion
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### Configuration
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Environment variables (optional):
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- `TESSDATA_PREFIX`: Tesseract language data location
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- `PDF_TEMP_DIR`: Temporary file processing directory
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- `DEBUG`: Enable debug logging
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## Development Notes
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### Testing Strategy
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- Comprehensive unit tests with mocked PDF libraries
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- Test fixtures for consistent PDF document simulation
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- Error handling tests for all major failure modes
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- Server initialization and tool registration validation
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### Tool Implementation Pattern
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All tools follow this pattern:
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1. Validate PDF path using `validate_pdf_path()`
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2. Try primary method with intelligent selection
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3. Implement fallbacks where applicable
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4. Return structured results with metadata
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5. Include timing information and method used
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6. Provide helpful error messages with troubleshooting hints
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### Docker Support
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The project includes Docker support with all system dependencies pre-installed, useful for consistent cross-platform development and deployment.
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### MCP Integration
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Tools are registered using FastMCP decorators and follow MCP protocol standards for tool descriptions and parameter validation. |