Add MCP prompts, README, LICENSE, and PyPI metadata
Five new prompts guide LLMs through multi-tool workflows: satellite_snapshot, climate_monitor, layer_deep_dive, multi_layer_story, polar_watch. README documents all 11 tools, 5 resources, and 7 prompts with example conversations. MIT license, project URLs, and updated classifiers for PyPI.
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LICENSE
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MIT License
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Copyright (c) 2026 Ryan Malloy
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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168
README.md
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# mcgibs
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NASA Earth science visualizations for LLMs.
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An [MCP](https://modelcontextprotocol.io/) server that connects language models to [NASA GIBS](https://www.earthdata.nasa.gov/engage/open-data-services-software/earthdata-developer-portal/gibs-api) (Global Imagery Browse Services) — 1000+ visualization layers covering satellite imagery, scientific data products, and derived Earth observations, updated daily.
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**Three pillars:**
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- **Discovery** — search layers by keyword, browse measurement categories, check date availability
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- **Visualization** — fetch imagery and data products by place name and date, compare dates side-by-side, composite multiple layers
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- **Interpretation** — natural-language colormap explanations, legend graphics, scientific context
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No API key required. All data is freely available from NASA.
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## Quick Start
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### From PyPI
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```bash
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uvx mcgibs
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```
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### Add to Claude Code
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```bash
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claude mcp add mcgibs -- uvx mcgibs
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```
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### Local development
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```bash
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git clone https://git.supported.systems/rpm/mcgibs.git
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cd mcgibs
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uv sync --all-extras
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uv run mcgibs
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```
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Or add a local dev server to Claude Code:
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```bash
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claude mcp add mcgibs-local -- uv run --directory /path/to/mcgibs mcgibs
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```
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## Tools
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| Tool | Description |
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|------|-------------|
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| `search_gibs_layers` | Search 1000+ layers by keyword, measurement, period, or status |
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| `get_layer_info` | Full metadata for a layer — instrument, platform, resolution, dates |
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| `list_measurements` | All measurement categories with layer counts |
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| `check_layer_dates` | Available date range for a layer (capabilities + live DescribeDomains) |
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| `get_imagery` | Fetch a visualization by layer, date, and place name or bbox |
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| `compare_dates` | Side-by-side comparison of two dates for change detection |
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| `get_imagery_composite` | Overlay up to 5 layers into a single composite image |
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| `explain_layer_colormap` | Natural-language explanation of what colors represent |
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| `get_legend` | Pre-rendered legend graphic for a layer |
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| `resolve_place` | Geocode a place name to coordinates and bounding box |
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| `build_tile_url` | Construct a direct WMTS tile URL for embedding |
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## Resources
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| URI | Description |
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|-----|-------------|
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| `gibs://catalog` | Full layer catalog grouped by measurement category |
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| `gibs://layer/{layer_id}` | Individual layer metadata as JSON |
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| `gibs://colormap/{layer_id}` | Colormap explanation for a layer |
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| `gibs://dates/{layer_id}` | Available date range for a layer |
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| `gibs://projections` | Supported GIBS projections and endpoints |
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## Prompts
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| Prompt | Parameters | Description |
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|--------|------------|-------------|
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| `earth_overview` | *(none)* | Introduction to GIBS with suggested explorations |
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| `investigate_event` | `event_type`, `location`, `date` | Guided workflow for investigating natural events |
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| `satellite_snapshot` | `place`, `date` | Quick satellite view of any location |
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| `climate_monitor` | `indicator`, `location`, `start_date`, `end_date` | Track climate changes over time |
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| `layer_deep_dive` | `layer_id`, `location`, `date` | Full scientific analysis of a single layer |
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| `multi_layer_story` | `topic`, `location`, `date` | Data journalism — composite layers to tell a story |
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| `polar_watch` | `pole`, `date`, `compare_date` | Arctic/Antarctic ice and snow monitoring |
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## Example Workflows
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### Investigating a wildfire
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```
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User: Use the investigate_event prompt for the 2025 LA wildfires
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→ search_gibs_layers("fire")
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→ search_gibs_layers("true color")
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→ check_layer_dates("MODIS_Terra_CorrectedReflectance_TrueColor")
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→ get_imagery("MODIS_Terra_CorrectedReflectance_TrueColor", "2025-01-08", place="Los Angeles")
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→ get_imagery("MODIS_Fires_Terra", "2025-01-08", place="Los Angeles")
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→ compare_dates("MODIS_Terra_CorrectedReflectance_TrueColor", "2025-01-01", "2025-01-10", place="Los Angeles")
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→ explain_layer_colormap("MODIS_Fires_Terra")
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```
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### Monitoring sea ice decline
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```
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User: Use climate_monitor to track Arctic sea ice from March to September
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→ search_gibs_layers("sea ice")
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→ check_layer_dates("AMSR2_Sea_Ice_Concentration_12km")
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→ explain_layer_colormap("AMSR2_Sea_Ice_Concentration_12km")
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→ compare_dates("AMSR2_Sea_Ice_Concentration_12km", "2025-03-01", "2025-09-01", bbox=[-180,60,180,90])
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→ get_legend("AMSR2_Sea_Ice_Concentration_12km")
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```
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### Quick look at anywhere on Earth
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```
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User: Use satellite_snapshot for the Great Barrier Reef
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→ resolve_place("Great Barrier Reef")
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→ get_imagery("MODIS_Terra_CorrectedReflectance_TrueColor", "2025-06-01", place="Great Barrier Reef")
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→ search_gibs_layers("chlorophyll")
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→ get_imagery_composite(["MODIS_Terra_CorrectedReflectance_TrueColor", "MODIS_Terra_Chlorophyll_A"], "2025-06-01", place="Great Barrier Reef")
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```
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## Projections
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| EPSG | Description | Use case |
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|------|-------------|----------|
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| 4326 | Geographic (WGS84) | Default — global coverage, most layers |
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| 3857 | Web Mercator | Web map tiles, Leaflet/Mapbox integration |
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| 3413 | Arctic Polar Stereographic | Arctic-focused imagery |
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| 3031 | Antarctic Polar Stereographic | Antarctic-focused imagery |
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## Development
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```bash
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uv sync --all-extras
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# Lint
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uv run ruff check src/ tests/
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# Tests
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uv run pytest
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# Build
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uv build
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```
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## Architecture
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```
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src/mcgibs/
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server.py MCP server — tools, resources, prompts, middleware
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client.py GIBS HTTP client — WMS, WMTS, colormaps, geocoding
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capabilities.py WMTS GetCapabilities parser and layer search
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colormaps.py Colormap XML parser and natural-language interpreter
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models.py Pydantic models — Layer, BBox, GeoResult, ColormapEntry
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constants.py API endpoints, projections, tile matrix definitions
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geo.py Bounding box math and geocoding helpers
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```
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## License
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[MIT](LICENSE)
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## Links
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- [NASA GIBS](https://www.earthdata.nasa.gov/engage/open-data-services-software/earthdata-developer-portal/gibs-api)
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- [GIBS API Documentation](https://nasa-gibs.github.io/gibs-api-docs/)
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- [Worldview](https://worldview.earthdata.nasa.gov/) — NASA's browser-based GIBS viewer
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- [FastMCP](https://gofastmcp.com/) — the MCP framework powering this server
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- [Source](https://git.supported.systems/rpm/mcgibs)
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name = "mcgibs"
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name = "mcgibs"
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version = "2026.02.18"
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version = "2026.02.18"
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description = "FastMCP server for NASA Global Imagery Browse Services (GIBS)"
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description = "FastMCP server for NASA Global Imagery Browse Services (GIBS)"
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readme = "CLAUDE.md"
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readme = "README.md"
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requires-python = ">=3.12"
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requires-python = ">=3.12"
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license = {text = "MIT"}
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authors = [{name = "Ryan Malloy", email = "ryan@supported.systems"}]
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authors = [{name = "Ryan Malloy", email = "ryan@supported.systems"}]
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keywords = ["nasa", "gibs", "mcp", "satellite", "imagery", "earth-science"]
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keywords = ["nasa", "gibs", "mcp", "satellite", "imagery", "earth-science"]
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classifiers = [
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classifiers = [
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"Development Status :: 4 - Beta",
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"Development Status :: 4 - Beta",
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"Intended Audience :: Developers",
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"Intended Audience :: Developers",
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"Intended Audience :: Science/Research",
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"Intended Audience :: Science/Research",
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"License :: OSI Approved :: MIT License",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.12",
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"Programming Language :: Python :: 3.13",
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"Programming Language :: Python :: 3.13",
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"Programming Language :: Python :: 3.14",
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"Programming Language :: Python :: 3.14",
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"defusedxml>=0.7.1",
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"defusedxml>=0.7.1",
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]
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]
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[project.urls]
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Homepage = "https://git.supported.systems/rpm/mcgibs"
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Documentation = "https://nasa-gibs.github.io/gibs-api-docs/"
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"Bug Tracker" = "https://git.supported.systems/rpm/mcgibs/issues"
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[project.scripts]
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[project.scripts]
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mcgibs = "mcgibs.server:main"
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mcgibs = "mcgibs.server:main"
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return "\n".join(lines)
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return "\n".join(lines)
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@mcp.prompt
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def satellite_snapshot(
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place: str,
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date: str = "today",
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) -> str:
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"""Quick satellite view of any place on Earth.
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Args:
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place: Location to view (e.g. "Tokyo", "Amazon Rainforest", "Sahara Desert").
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date: Date to view (YYYY-MM-DD), or "today" for the most recent imagery.
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"""
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lines = [
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f"Show me a satellite view of {place} on {date}.",
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"",
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"Follow this workflow:",
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"",
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f'1. **Geocode**: Use resolve_place to get coordinates for "{place}".',
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"",
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f"2. **True color first**: Fetch imagery for "
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f'"MODIS_Terra_CorrectedReflectance_TrueColor" '
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f"on {date} at that location. This gives a natural-looking view.",
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"",
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f"3. **Find science layers**: Search for layers relevant to "
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f'"{place}" — consider what would be scientifically interesting '
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f"for this region (vegetation for forests, sea surface temperature "
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f"for coastal areas, snow cover for mountains, etc.).",
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"",
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"4. **Check dates**: Verify the science layers have data for "
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f"{date} using check_layer_dates.",
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"",
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"5. **Overlay**: If a relevant science layer is available, "
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"use get_imagery_composite to overlay it on the true color base.",
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"",
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"6. **Interpret**: If the overlaid layer has a colormap, "
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"use explain_layer_colormap to describe what the colors mean.",
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"",
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f"7. **Describe**: Summarize what's visible in the satellite view "
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f"of {place} — notable features, weather patterns, land use, "
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f"or any phenomena visible from orbit.",
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]
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return "\n".join(lines)
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@mcp.prompt
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def climate_monitor(
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indicator: str,
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location: str,
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start_date: str,
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end_date: str,
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) -> str:
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"""Track a climate indicator over time at a specific location.
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Args:
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indicator: What to monitor (e.g. "sea ice", "vegetation", "temperature", "snow cover").
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location: Region to monitor (e.g. "Arctic", "Amazon Basin", "Greenland").
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start_date: Beginning of monitoring period (YYYY-MM-DD).
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end_date: End of monitoring period (YYYY-MM-DD).
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"""
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lines = [
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f"Monitor {indicator} changes near {location} "
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f"from {start_date} to {end_date}.",
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"",
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"Follow this workflow:",
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"",
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f'1. **Find layers**: Search for GIBS layers related to "{indicator}". '
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f"Look for layers with colormaps — these encode quantitative data.",
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"",
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"2. **Check availability**: Use check_layer_dates on the best "
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f"matching layer to confirm data exists for both {start_date} "
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f"and {end_date}.",
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"",
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"3. **Understand the scale**: Use explain_layer_colormap to learn "
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"what the color values represent (units, range). "
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"Also fetch the legend with get_legend for visual reference.",
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"",
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f"4. **Compare endpoints**: Use compare_dates to create a "
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f"side-by-side view of {start_date} vs {end_date} "
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f'for "{location}". This immediately shows the magnitude of change.',
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"",
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"5. **Intermediate snapshots**: Fetch 1-2 additional dates between "
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"start and end using get_imagery to show the progression "
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"(e.g. mid-point of the range).",
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"",
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"6. **Synthesize**: Describe the observed trend, referencing "
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"specific colormap values where possible. Is the indicator "
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"increasing, decreasing, or showing seasonal variation? "
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"What does the spatial pattern reveal?",
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]
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return "\n".join(lines)
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@mcp.prompt
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def layer_deep_dive(
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layer_id: str,
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location: str = "global",
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date: str = "",
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) -> str:
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"""Full scientific analysis of a single GIBS layer.
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Args:
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layer_id: The GIBS layer identifier (e.g. "AIRS_L3_Surface_Air_Temperature_Daily_Day").
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location: Region to focus on, or "global" for worldwide view.
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date: Specific date (YYYY-MM-DD), or empty for the most recent available.
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"""
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location_desc = f'for "{location}"' if location != "global" else "at global scale"
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date_desc = f"on {date}" if date else "using the most recent available date"
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lines = [
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f"Do a deep scientific analysis of the {layer_id} layer "
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f"{location_desc} {date_desc}.",
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"",
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"Follow this workflow:",
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"",
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"1. **Layer metadata**: Use get_layer_info to retrieve full metadata "
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f'for "{layer_id}" — instrument, platform, resolution, date range, '
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"and scientific description.",
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"",
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||||||
|
"2. **Color interpretation**: Use explain_layer_colormap to get a "
|
||||||
|
"detailed description of what each color represents, including "
|
||||||
|
"physical units and value ranges. Fetch the legend with get_legend.",
|
||||||
|
"",
|
||||||
|
f"3. **Sample imagery**: Use get_imagery to fetch the layer "
|
||||||
|
f"{location_desc} {date_desc}. If no date was specified, "
|
||||||
|
"use check_layer_dates to find the latest available date first.",
|
||||||
|
"",
|
||||||
|
"4. **Image interpretation**: Walk through what's visible in the "
|
||||||
|
"image, referencing specific colormap values. Identify notable "
|
||||||
|
"spatial patterns, anomalies, or features.",
|
||||||
|
"",
|
||||||
|
"5. **Scientific context**: Explain what this layer measures, "
|
||||||
|
"why it matters, what instrument collects the data, and how "
|
||||||
|
"scientists use it. Connect the visualization to the underlying "
|
||||||
|
"Earth science.",
|
||||||
|
]
|
||||||
|
return "\n".join(lines)
|
||||||
|
|
||||||
|
|
||||||
|
@mcp.prompt
|
||||||
|
def multi_layer_story(
|
||||||
|
topic: str,
|
||||||
|
location: str,
|
||||||
|
date: str,
|
||||||
|
) -> str:
|
||||||
|
"""Compose a data-driven narrative by combining multiple GIBS layers.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
topic: Story theme (e.g. "wildfire smoke", "ocean productivity", "dust storm").
|
||||||
|
location: Region of interest.
|
||||||
|
date: Date for the story (YYYY-MM-DD).
|
||||||
|
"""
|
||||||
|
lines = [
|
||||||
|
f'Tell the story of "{topic}" near {location} on {date} '
|
||||||
|
f"by compositing multiple satellite layers.",
|
||||||
|
"",
|
||||||
|
"Follow this workflow:",
|
||||||
|
"",
|
||||||
|
f'1. **Find related layers**: Search for 2-4 layers related to "{topic}". '
|
||||||
|
f"Choose layers that complement each other — for example, "
|
||||||
|
f"fire detections + aerosol optical depth + true color for wildfires, "
|
||||||
|
f"or SST + chlorophyll + true color for ocean events.",
|
||||||
|
"",
|
||||||
|
"2. **Verify dates**: Use check_layer_dates on each candidate "
|
||||||
|
f"to confirm data is available for {date}.",
|
||||||
|
"",
|
||||||
|
"3. **Understand each layer**: For each layer with a colormap, "
|
||||||
|
"use explain_layer_colormap to understand the value encoding.",
|
||||||
|
"",
|
||||||
|
"4. **Composite view**: Use get_imagery_composite to overlay "
|
||||||
|
"the layers into a single image. Put the base layer "
|
||||||
|
"(true color or reference) first in the list.",
|
||||||
|
"",
|
||||||
|
"5. **Individual layers**: Also fetch get_imagery for each layer "
|
||||||
|
"separately so you can describe what each one contributes.",
|
||||||
|
"",
|
||||||
|
"6. **Narrative**: Write a data-journalism style narrative "
|
||||||
|
"describing how the layers interact and what story they tell "
|
||||||
|
"together. Reference specific colormap values and spatial "
|
||||||
|
"patterns. Explain cause and effect where the data supports it.",
|
||||||
|
]
|
||||||
|
return "\n".join(lines)
|
||||||
|
|
||||||
|
|
||||||
|
@mcp.prompt
|
||||||
|
def polar_watch(
|
||||||
|
pole: str,
|
||||||
|
date: str,
|
||||||
|
compare_date: str = "",
|
||||||
|
) -> str:
|
||||||
|
"""Monitor Arctic or Antarctic ice and snow conditions.
|
||||||
|
|
||||||
|
Args:
|
||||||
|
pole: Which pole — "arctic" or "antarctic".
|
||||||
|
date: Primary observation date (YYYY-MM-DD).
|
||||||
|
compare_date: Optional second date for before/after comparison (YYYY-MM-DD).
|
||||||
|
"""
|
||||||
|
if pole.lower().startswith("ant"):
|
||||||
|
region = "Antarctic"
|
||||||
|
bbox = "[-180, -90, 180, -60]"
|
||||||
|
else:
|
||||||
|
region = "Arctic"
|
||||||
|
bbox = "[-180, 60, 180, 90]"
|
||||||
|
|
||||||
|
lines = [
|
||||||
|
f"Monitor {region} ice and snow conditions on {date}.",
|
||||||
|
"",
|
||||||
|
"Follow this workflow:",
|
||||||
|
"",
|
||||||
|
f'1. **Find polar layers**: Search for "sea ice", "snow", '
|
||||||
|
f'and "ice sheet" layers. Focus on layers that cover the '
|
||||||
|
f"{region} region.",
|
||||||
|
"",
|
||||||
|
f"2. **Check availability**: Use check_layer_dates to verify "
|
||||||
|
f"data exists for {date} on the selected layers.",
|
||||||
|
"",
|
||||||
|
f"3. **Polar imagery**: Fetch imagery using the {region} "
|
||||||
|
f"bounding box {bbox}. Get true color first, then ice/snow "
|
||||||
|
f"concentration layers.",
|
||||||
|
"",
|
||||||
|
"4. **Color interpretation**: Use explain_layer_colormap on each "
|
||||||
|
"science layer and fetch legends with get_legend to understand "
|
||||||
|
"concentration values and classifications.",
|
||||||
|
"",
|
||||||
|
"5. **Embeddable tiles**: Use build_tile_url to generate a "
|
||||||
|
"WMTS URL for the primary ice layer — useful for embedding "
|
||||||
|
"in reports or dashboards.",
|
||||||
|
]
|
||||||
|
|
||||||
|
if compare_date:
|
||||||
|
lines.extend([
|
||||||
|
"",
|
||||||
|
f"6. **Temporal comparison**: Use compare_dates to show "
|
||||||
|
f"{compare_date} vs {date} side-by-side. Describe the "
|
||||||
|
f"change in ice extent and concentration between these dates.",
|
||||||
|
])
|
||||||
|
else:
|
||||||
|
lines.extend([
|
||||||
|
"",
|
||||||
|
f"6. **Summarize**: Describe the current {region} ice/snow "
|
||||||
|
f"state visible in the imagery, referencing colormap values "
|
||||||
|
f"for concentration percentages.",
|
||||||
|
])
|
||||||
|
|
||||||
|
return "\n".join(lines)
|
||||||
|
|
||||||
|
|
||||||
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||||
# Entry point
|
# Entry point
|
||||||
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
# ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
|
||||||
|
|||||||
@ -277,6 +277,65 @@ async def test_list_resources(capabilities_xml):
|
|||||||
server_module._client = None
|
server_module._client = None
|
||||||
|
|
||||||
|
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
# Prompt tests
|
||||||
|
# ---------------------------------------------------------------------------
|
||||||
|
|
||||||
|
|
||||||
|
@respx.mock
|
||||||
|
async def test_list_prompts(capabilities_xml):
|
||||||
|
"""All expected prompts are registered on the MCP server."""
|
||||||
|
respx.get(url__regex=r".*WMTSCapabilities\.xml").mock(
|
||||||
|
return_value=httpx.Response(200, text=capabilities_xml)
|
||||||
|
)
|
||||||
|
|
||||||
|
server_module._client = await _init_mock_client(capabilities_xml)
|
||||||
|
try:
|
||||||
|
async with Client(mcp) as client:
|
||||||
|
prompts = await client.list_prompts()
|
||||||
|
prompt_names = {p.name for p in prompts}
|
||||||
|
|
||||||
|
expected = {
|
||||||
|
"investigate_event",
|
||||||
|
"earth_overview",
|
||||||
|
"satellite_snapshot",
|
||||||
|
"climate_monitor",
|
||||||
|
"layer_deep_dive",
|
||||||
|
"multi_layer_story",
|
||||||
|
"polar_watch",
|
||||||
|
}
|
||||||
|
|
||||||
|
for name in expected:
|
||||||
|
assert name in prompt_names, f"Missing prompt: {name}"
|
||||||
|
finally:
|
||||||
|
await server_module._client.close()
|
||||||
|
server_module._client = None
|
||||||
|
|
||||||
|
|
||||||
|
@respx.mock
|
||||||
|
async def test_satellite_snapshot_prompt(capabilities_xml):
|
||||||
|
"""satellite_snapshot prompt includes the place name and expected tool references."""
|
||||||
|
respx.get(url__regex=r".*WMTSCapabilities\.xml").mock(
|
||||||
|
return_value=httpx.Response(200, text=capabilities_xml)
|
||||||
|
)
|
||||||
|
|
||||||
|
server_module._client = await _init_mock_client(capabilities_xml)
|
||||||
|
try:
|
||||||
|
async with Client(mcp) as client:
|
||||||
|
result = await client.get_prompt(
|
||||||
|
"satellite_snapshot", {"place": "Tokyo", "date": "2025-06-01"}
|
||||||
|
)
|
||||||
|
text = result.messages[0].content.text
|
||||||
|
|
||||||
|
assert "Tokyo" in text
|
||||||
|
assert "resolve_place" in text
|
||||||
|
assert "get_imagery" in text or "get_imagery_composite" in text
|
||||||
|
assert "MODIS_Terra_CorrectedReflectance_TrueColor" in text
|
||||||
|
finally:
|
||||||
|
await server_module._client.close()
|
||||||
|
server_module._client = None
|
||||||
|
|
||||||
|
|
||||||
@respx.mock
|
@respx.mock
|
||||||
async def test_colormap_resource(capabilities_xml, colormap_xml):
|
async def test_colormap_resource(capabilities_xml, colormap_xml):
|
||||||
"""gibs://colormap/{layer_id} returns colormap explanation text."""
|
"""gibs://colormap/{layer_id} returns colormap explanation text."""
|
||||||
|
|||||||
Loading…
x
Reference in New Issue
Block a user