eo-mcp

<div align="center"> # `eo-mcp` ### The Open Source Model Context Protocol (MCP) for Planetary Earth Observation [](https://opensource.org/licenses/Apache-2.0) [](https://www.python.org/downloads/) [](https://modelcontextprotocol.io) [](https://stacspec.org) [](#free-government-catalogs) [](https://www.linkedin.com/company/eo-mcp/) **Empower AI agents to autonomously discover, stream, and compute satellite analytics from free government archives.** Plugs directly into **Claude Desktop**, **Cursor**, **Codex**, **Antigravity**, and autonomous Python agent loops with zero vendor lock-in and zero proprietary API costs. [Website & Live Interactive Demo](https://eo-mcp.github.io/) • [LinkedIn Page](https://www.linkedin.com/company/eo-mcp/) • [Quickstart](#quickstart) • [Tools Reference](#tools-reference) • [Architecture](#architecture) <p align="center"> <img src="assets/hero-earth.gif" alt="eo-mcp : Autonomous Planetary Earth Observation Protocol" width="720" style="max-width: 100%; border-radius: 8px;" /> </p> </div> --- ## Why `eo-mcp`? Proprietary platforms (like Planet Labs' agentic dashboard or Google Earth Engine) build walled gardens that trap users in closed user interfaces and expensive recurring subscription paywalls. `eo-mcp` takes the opposite philosophy, modeled after open-source community standards: - **100% Open Standards**: Speaks standard **Model Context Protocol (JSON-RPC 2.0)**. Works in Claude Desktop, Cursor IDE, VS Code, Open WebUI, and custom agent frameworks (LangChain, AutoGen, CrewAI). - **Zero-Config Free Government Data**: Instant out-of-the-box queries to AWS Earth Search, NASA CMR, and Copernicus public STAC endpoints for Sentinel-2, Landsat 8/9, and Copernicus DEM without needing API keys. - **Cloud-Native COG Streaming**: Never download a 1GB satellite granule again. `eo-mcp` leverages HTTP range requests (`/vsicurl/`) to stream and compute band math on only the exact bounding box of your city, farm, or river in seconds (saving >98% bandwidth). - **Deterministic Scientific Visualization (Zero AI Hallucinations)**: 100% script-driven visual outputs (NumPy arrays, 256-color scientific LUT rasters, GeoJSON vectors, georeferenced GeoTIFFs, and self-contained interactive Leaflet web maps) computed directly from real satellite pixels and coordinates. Generative AI imagery tools (Midjourney, DALL-E, Nano Banana) are strictly prohibited. - **Autonomous Agentic Script Runner**: When standard tools aren't enough, agents can generate and run custom `rasterio`, `xarray`, and `geopandas` scripts inside an isolated geospatial sandbox. --- ## Quickstart > [!IMPORTANT] > **100% Free & Zero-Config:** You **DO NOT** need a Copernicus API key, ESA account, or paid subscription. Baseline satellite streams (Sentinel-2, Landsat, and Copernicus DEM) stream directly from open government cloud archives out-of-the-box. ### 0. Prerequisite: Install `uv` `eo-mcp` is powered by Astral's `uv` for instant isolated execution: - **macOS / Linux**: ```bash curl -LsSf https://astral.sh/uv/install.sh | sh ``` - **Windows (PowerShell)**: ```powershell powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex" ``` --- ### 1. Instant Run with `uvx` (Recommended) Run directly from GitHub with zero installation or virtualenv setup: ```bash uvx --from git+https://github.com/eo-mcp/eo-mcp eo-mcp ``` Or run diagnostics to verify your environment and open STAC feeds: ```bash uvx --from git+https://github.com/eo-mcp/eo-mcp eo-mcp doctor ``` Or install locally from source: ```bash git clone https://github.com/eo-mcp/eo-mcp.git cd eo-mcp uv run eo-mcp ``` --- ### 2. Configure Your AI Agent #### Claude Desktop Add this to your `claude_desktop_config.json`: - **macOS**: `~/Library/Application Support/Claude/claude_desktop_config.json` - **Windows**: `%APPDATA%\Claude\claude_desktop_config.json` ```json { "mcpServers": { "eo-mcp": { "command": "uvx", "args": ["--from", "git+https://github.com/eo-mcp/eo-mcp", "eo-mcp"] } } } ``` #### Cursor IDE 1. Open **Cursor Settings** (`Ctrl+Shift+J` or `Cmd+Shift+J`). 2. Navigate to **Features** > **MCP**. 3. Click **+ Add New MCP Server**: - **Name**: `eo-mcp` - **Type**: `command` - **Command**: `uvx --from git+https://github.com/eo-mcp/eo-mcp eo-mcp` #### Codex Register `eo-mcp` into your Codex environment with a single command: ```bash codex mcp add eo-mcp -- uvx --from git+https://github.com/eo-mcp/eo-mcp eo-mcp ``` #### Google Antigravity Add to your workspace or global `.antigravity/mcp.json`: ```json { "mcpServers": { "eo-mcp": { "command": "uvx", "args": ["--from", "git+https://github.com/eo-mcp/eo-mcp", "eo-mcp"] } } } ``` --- ### ❓ Troubleshooting: Why is my AI asking for a Copernicus API key? > [!WARNING] > If Claude, Cursor, or ChatGPT asks you: *"Please provide your Copernicus API key or credentials"*, **that means `eo-mcp` is not yet running in your client!** > > When an MCP server has not been configured or failed to start, LLMs fall back to generic training data and mistakenly assume you need a paid or registered API key. > > **How to fix in 30 seconds:** > 1. Ensure `uv` is installed and accessible in your terminal (`uv --version`). > 2. In **Cursor**, make sure `eo-mcp` displays a **green status indicator** under **Settings > Features > MCP**. > 3. In **Claude Desktop**, look for the **hammer icon (🔨)** in the chat window to confirm `eo-mcp` tools are active. > 4. Test connectivity in your terminal with `uvx --from git+https://github.com/eo-mcp/eo-mcp eo-mcp doctor`. --- ## Supported Satellite Collections | Mission / Sensor | Resolution | Spectral Domain | Primary Applications | Free STAC Source | | :--- | :--- | :--- | :--- | :--- | | **Sentinel-2 L2A** | 10m - 20m | Optical (12 Bands: VNIR/SWIR) | Vegetation Health (NDVI), Water Bodies (NDWI), Crops | AWS Earth Search / CDSE | | **Landsat 8 & 9 (C2 L2)** | 30m | Optical & Thermal (OLI-2/TIRS-2) | Burn Severity (NBR), Land Surface Temp (LST) | AWS Earth Search / USGS | | **Copernicus DEM GLO-30** | 30m | Elevation (DSM) | Elevation Profiles, Slope, Aspect, Flood Basins | AWS Earth Search / CDSE | | **Sentinel-1 GRD** | 10m | C-Band SAR (VV/VH Radar) | All-Weather Flood Inundation & Soil Moisture | CDSE / Planetary Computer | | **Sentinel-5P TROPOMI** | 5.5km × 3.5km | Atmospheric Absorption | NO₂, SO₂, Carbon Monoxide, Methane, Aerosols | CDSE / Planetary Computer | | **MODIS / VIIRS** | 250m - 1km | Optical, Thermal & Fire | Active Wildfires, Global Daily Phenology | NASA CMR STAC | --- --- ## Tools Reference & Architecture Patterns `eo-mcp` adopts the modern **Ergonomic Workflow** and **Progressive Tool Discovery** architecture (pioneered by Neon Postgres and modern agentic coding clients like Codex, Claude Code, Cursor, and Antigravity): 1. **Ergonomic Workflow Tools ("The Create with Compute Pattern")**: Bundles multi-step satellite chains (geocoding → STAC discovery → cloud filtering → band math → hazard modeling) into single-turn calls. Eliminates 4–6 round trips and prevents LLM chaining errors. 2. **Category Scoping & Lean Context Windows**: Scope tools via `EO_MCP_PROFILE` or `--profile` (`workflows`, `hazards`, `climate`, `maritime`, `minimal`) to reduce prompt bloat by up to 70%. 3. **Progressive Tool Discovery**: Clients dynamically search and inspect tools via `discover_eo_tools`. 4. **Code Mode Ergonomics**: Pythonic imports (`from eo_mcp import assess_location_hazard, environmental_site_audit`) allow autonomous agents in code execution sandboxes to run multi-step pipelines directly with zero JSON-RPC overhead. --- ### 🌟 Ergonomic Workflow Tools (High-Level Turnkey Operations) #### 1. `assess_location_hazard(location: str, hazard_type: str, ...)` **All-in-one disaster & climate hazard assessment.** Accepts a natural language place name (`"Valencia, Spain"`, `"Rhodes, Greece"`) or bounding box. Automatically geocodes, queries open STAC catalogs, and runs specialized hazard modeling: - `hazard_type="flood_inundation"`: Sea-level rise and storm surge inundation (EU Floods Directive). - `hazard_type="wildfire"`: Active thermal hotspots, FRP (MW), and EFFIS clustered perimeters. - `hazard_type="burn_severity"`: Multi-temporal dNBR and post-fire scar classification. - `hazard_type="coastal_erosion"`: Multi-year shoreline retreat rates (m/year) via perpendicular transects. - `hazard_type="drought"`: Freshwater reservoir depletion and desiccated dry margins. - `hazard_type="urban_heat"`: Land Surface Temperature (LST) and thermal hotspot gradients. - `hazard_type="dark_vessels"`: Sentinel-1 SAR ship detection and AIS correlation. **Outputs:** Formatted summary with ASCII preview maps, GIS-ready GeoJSON (`format="geojson"`), or CSV (`format="csv"`). #### 2. `environmental_site_audit(location: str, datetime_range: str = "2024-06-01/2024-08-31", format: str = "summary")` **Comprehensive Regional Environmental Scorecard.** In a single call, synthesizes: - **Vegetation Vigor & Biomass**: Sentinel-2 NDVI distribution and canopy vigor status. - **Surface Water & Wetness**: NDWI distribution and moisture index. - **Topography & Terrain**: Copernicus DEM GLO-30 elevation (min/mean/max) and slope gradients. - **Vulnerability Indices**: Integrated flood susceptibility and vegetative stress risk classification. **Outputs:** Executive JSON scorecard or GIS polygon GeoJSON. #### 3. `run_pipeline(spec: str, location: str = None, format: str = "summary", parameters: str = None)` **Zero-Infrastructure User-Defined Pipeline Orchestrator.** Enables researchers, environmental agencies, and autonomous AI agents to build, configure, and execute custom multi-step Earth Observation processing pipelines: - **Turnkey Compound Recipes**: 1. `compound_wildfire_runoff_risk`: High-severity dNBR burn scars + Copernicus DEM slope gradient $\rightarrow$ Post-fire debris flow and runoff risk. 2. `coastal_storm_surge_infrastructure_exposure`: Copernicus DEM + IPCC AR6 Sea Level Rise + storm surge $\rightarrow$ Submerged transport networks & isolated medical facilities. 3. `agricultural_drought_thermal_stress`: Sentinel-2 NDVI canopy vigor + Landsat LST surface thermal anomalies + reservoir surface water shrinkage. 4. `maritime_environmental_patrol`: Sentinel-1 SAR CFAR target detection + Live Baltic AIS transponders + low-backscatter oil slick screening. - **Custom Declarative Pipelines**: Accepts custom JSON specifications chaining primitives (`fetch_raster`, `spectral_index`, `terrain_analysis`, `inundation_model`, `wildfire_activity`, `burn_severity`, `maritime_sar_ais`, `exposure_overlay`, `compound_risk_synthesis`). - **OpenStreetMap Critical Infrastructure Overlay**: Automatically intersects hazard footprints with public roads, bridges, hospitals, fire stations, and ports via the public Overpass API with local spatial fallback. - **Outputs**: Comprehensive JSON report with ASCII hazard map, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or CSV (`format="csv"`). #### 4. `list_pipeline_recipes()` & `describe_pipeline_recipe(recipe_name: str)` Inspect and introspect available compound hazard and multi-spectral processing recipes, their default parameters, and execution step definitions. #### 5. `discover_eo_tools(category: str = None, query: str = None)` **Progressive Tool Discovery.** Allows agents to inspect available tool capabilities on demand without loading all 25 tool schemas upfront: - Categories: `workflows`, `core`, `spectral`, `hazards`, `climate`, `maritime`, `advanced`. --- ### 🛠️ Primitives & Specialized Planetary Tools #### 4. `eo_geocode(query: str) -> dict` Translates natural language place names (e.g. `"Valencia, Spain"`, `"Imperial Valley, CA"`, `"Lake Chad"`) into standard WGS84 bounding boxes `[min_lon, min_lat, max_lon, max_lat]`. ### 2. `stac_search(bbox: list, datetime_range: str, collections: list, max_cloud_cover: float = 20.0) -> list` Searches free STAC catalogs for available satellite scenes matching the spatial area, cloud thresholds, and date window. Returns scene IDs, acquisition dates, cloud percentages, and direct asset URLs. ### 3. `calculate_spectral_index(collection: str, index: str, bbox: list, date: str) -> dict` Streams the required bands over HTTP range requests and computes the requested index: - **NDVI**: `(B08 - B04) / (B08 + B04)` (Vegetation vigor & biomass) - **NDWI**: `(B03 - B08) / (B03 + B08)` (Water body delineation & surface wetness) - **NBR**: `(B08 - B12) / (B08 + B12)` (Wildfire burn severity & scar mapping) - **EVI**: Enhanced Vegetation Index (Atmospherically corrected canopy structure) Returns summary statistics (mean, min, max, std), histogram, and optional GeoTIFF/PNG export path. ### 4. `get_elevation_profile(bbox: list, calculate_slope: bool = True) -> dict` Queries the gold-standard Copernicus DEM GLO-30 (30m) dataset. Extracts minimum/maximum/mean elevation, terrain slope gradients, and aspect orientation without downloading the global raster. ### 5. `detect_water_sar(bbox: list, date: str, threshold_db: float = -16.0) -> dict` Extracts Sentinel-1 C-Band SAR radar backscatter (sigma0 in dB). Radar signals scatter away from calm surface water, producing distinct dark backscatter signatures unaffected by optical cloud cover. ### 6. `run_geospatial_script(script_code: str) -> dict` Allows autonomous coding agents to write and execute arbitrary Python geospatial workflows in an isolated sandbox pre-loaded with `rasterio`, `numpy`, `shapely`, and `scipy`. ### 7. `detect_dark_vessels(bbox: list, datetime_range: str, ais_source: str = "open_baltic_api", pfa_factor: float = 3.2, sea_state: str = "auto", format: str = "summary") -> str` Detects radar-reflective metallic ship hulls in Sentinel-1 SAR imagery using **Sea-State Adaptive CA-CFAR** (Cell-Averaging Constant False Alarm Rate) with local annular clutter sliding windows (guard and training rings) and automatic ocean surface roughness compensation (suppressing wave-crest false alarms in rough seas). Correlates radar targets with open public AIS feeds (Digitraffic Baltic Sea open API or user-supplied AIS records) to isolate uncooperative **Dark Vessels** (`DARK_VESSEL`), verified ships (`TRUSTED`), and spoofed signals (`SPOOF_OR_ABSENT`), with Signal-to-Clutter Ratio (SCR in dB) and bilge/oil slick discharge alerts (MSFD Descriptor 8). **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 8. `analyze_coastal_erosion(bbox: list, historical_date_range: str, recent_date_range: str, transect_sample_step: int = 5, format: str = "summary") -> str` Quantifies multi-temporal coastal shoreline retreat and erosion rates ($\text{m/year}$) between two satellite observation dates using automated MNDWI water indexing on Sentinel-2 / Landsat scenes and perpendicular baseline transects (USGS DSAS methodology). Classifies coastal segments into critical erosion, stable, and accretion zones aligned with the EU Climate Adaptation Strategy. **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 9. `simulate_sea_level_rise(bbox: list, water_level_rise_m: float = 1.0, storm_surge_m: float = 0.0, scenario: str = None, format: str = "summary") -> str` Simulates coastal inundation and storm surge flood risk using Copernicus DEM GLO-30 (30m) elevation and an 8-connected morphological flood-fill algorithm (hydro-connected bathtub model) preventing inland depression artifacts. Supports IPCC AR6 climate projections (`SSP1-2.6`, `SSP2-4.5`, `SSP5-8.5`) and EU Floods Directive hazard zoning. **Outputs:** JSON summary with ASCII flood distribution map, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 10. `detect_active_wildfires(bbox: list, days: int = 2, source: str = "VIIRS_NOAA20_NRT", format: str = "summary") -> str` Queries open NASA FIRMS active fire feeds (VIIRS / MODIS) to monitor thermal anomalies, calculate Fire Radiative Power (FRP in MW), and cluster contiguous fire perimeters with EFFIS / Copernicus Emergency Management fire danger classifications (`EXTREME`, `HIGH`, `MODERATE`). **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 11. `monitor_atmospheric_emissions(bbox: list, gas: str = "NO2", datetime_range: str = "2024-06-01/2024-06-30", format: str = "summary") -> str` Monitors tropospheric trace gases ($\text{NO}_2$, $\text{CH}_4$ Methane, $\text{SO}_2$, $\text{CO}$) from Sentinel-5P TROPOMI column densities and cross-references them against in-situ ground monitoring stations from the open OpenAQ public REST API for EU Ambient Air Quality Directive compliance. **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 12. `analyze_reservoir_drought(bbox: list, historical_year: int = 2019, recent_year: int = 2024, format: str = "summary") -> str` Analyzes reservoir shrinkage and freshwater deficit using the EC Joint Research Centre (JRC) Global Surface Water archive and multi-temporal Sentinel-2 imagery. Quantifies surface water depletion ($\text{ha}$, $\text{km}^2$), transition breakdown (permanent water vs. desiccated dry margin), and drought severity classes for the EU Water Framework Directive. **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 13. `calculate_burn_severity(bbox: list, pre_fire_date_range: str, post_fire_date_range: str, format: str = "summary") -> str` Quantifies post-fire damage using difference Normalized Burn Ratio ($dNBR$) and Relativized Burn Ratio ($RBR$) computed from pre- and post-fire Sentinel-2 L2A scenes (NIR Band 8 and SWIR22 Band 12). Maps spatial burn perimeters classified into USGS and European Forest Fire Information System (EFFIS) severity grades (`HIGH_SEVERITY`, `MODERATE_HIGH`, `MODERATE_LOW`, `LOW_SEVERITY`, `UNBURNED`). **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 14. `analyze_urban_heat_island(bbox: list, datetime_range: str, format: str = "summary") -> str` Calculates Land Surface Temperature ($LST$ in °C) and Urban Heat Island ($UHI$) intensity gradients using Landsat-8/9 Thermal Infrared Sensor (TIRS Band 10: $10.895\ \mu\text{m}$) combined with NDVI Fractional Vegetation Cover (FVC) and Sobrino surface emissivity modeling. Detects extreme heat risk anomalies, thermal hotspots, and cool-island vegetative buffers. **Outputs:** JSON summary with ASCII temperature distribution map, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 15. `monitor_crop_phenology(bbox: list, year: int = 2024, crop_type: str = None, format: str = "summary") -> str` Tracks multi-temporal agricultural canopy growth trajectories across the seasonal cycle using cloud-free Sentinel-2 observations. Extracts key phenological inflection milestones: Start of Season (SOS / greenup), Peak of Season (POS / maximum photosynthetic capacity), and End of Season (EOS / senescence). Compares seasonal canopy amplitude and vigor anomaly percentages against regional crop baselines. **Outputs:** JSON summary, GIS-ready GeoJSON FeatureCollection (`format="geojson"`), or tabular CSV (`format="csv"`). ### 16. `configure_credentials(provider: str, username: str = None, password: str = None, token: str = None) -> dict` Dynamically configures or updates credentials for external satellite and data providers in-session (`cdse`, `earthdata`, `planetary_computer`, `nasa_firms`). Allows users and agents to unlock advanced authenticated features (such as direct granule downloads) on demand without restarting the server. ### 17. `get_credential_status() -> dict` Inspects configured authentication status across all supported satellite catalog providers. Verifies whether zero-config public mode is active and safely displays masked status indicators. ### 18. `download_copernicus_granule(product_id: str, output_dir: str = None, username: str = None, password: str = None) -> dict` Generates authenticated download manifests, OData API links, and curl commands for official Copernicus Data Space Ecosystem (CDSE) full-granule archive products (Sentinel-1, Sentinel-2, Sentinel-3, Sentinel-5P). ### 19. `query_nasa_opera(bbox: list, datetime_range: str, product_type: str = "dswx", max_cloud_cover: float = 20.0, limit: int = 5, format: str = "summary") -> str` Discovers and inspects NASA JPL OPERA (Observational Products for End-Users from Remote Sensing Analysis) datasets via NASA CMR STAC: - `dswx`: 30m Dynamic Surface Water Extent from HLS (open water, partial water, inundated vegetation). - `dist`: 30m Surface Disturbance alerts from HLS (vegetation loss, wildfire scars, deforestation). - `rtc`: 30m Radiometric Terrain Corrected Sentinel-1 SAR (all-weather radar backscatter). **Outputs:** JSON summary with direct COG asset URLs, or GIS-ready GeoJSON (`format="geojson"`). ### 20. `export_interactive_map(title: str, bbox: list, geojson: str = None, hazard_type: str = None, output_html_path: str = None) -> str` Generates a standalone, self-contained interactive MapLibre GL JS web application from hazard results. Features dark titanium glassmorphic styling, interactive feature popups, responsive bounding box fitting, and 3D perspective pitch toggling. Bridges headless agentic compute with interactive GeoLibre-compatible visualization. ### 21. `export_geolibre_project(title: str, bbox: list, layers: str = None, output_json_path: str = None, basemap_theme: str = "dark") -> str` Exports a native `.geolibre` project configuration file. Enables one-click importing of `eo-mcp` hazard layers directly into GeoLibre Desktop (Tauri) or GeoLibre Web (`geolibre.app`). ### 22. `query_spatial_sql(sql: str, geojson: str, table_name: str = "features") -> str` Executes spatial SQL queries against GeoJSON FeatureCollections and geospatial metadata. Modeled after GeoLibre's DuckDB Spatial engine. Supports standard SQL (`SELECT`, `WHERE`, `GROUP BY`, `ORDER BY`) and spatial functions (`ST_Area`, `ST_Centroid`, `ST_Length`, `ST_Intersects`). ### 23. `analyze_coastal_water_quality(bbox: list, datetime_range: str = "2024-06-01/2024-06-30", collection: str = "sentinel-2-l2a", format: str = "summary") -> str` Assesses coastal water quality, eutrophication, harmful algal blooms (HABs), sedimentation, and industrial thermal effluent plumes. Computes: - **Normalized Difference Chlorophyll Index (NDCI)**: $(B05 - B04) / (B05 + B04)$ for chlorophyll-a estimation and HAB risk alerts (`CRITICAL`, `ELEVATED`, `MODERATE`, `LOW`) using Mishra & Mishra (2012). - **Normalized Difference Turbidity Index (NDTI)**: $(B04 - B03) / (B04 + B03)$ for water clarity using Lacaux et al. (2007). - **Suspended Particulate Matter (SPM / TSS) Proxy**: Single-band calibrated red reflectance model using Nechad et al. (2010). - **Thermal Plume Anomaly Detection**: Sea Surface Temperature (SST) elevation gradients ($\Delta T \ge 1.5^\circ\text{C}$). **Outputs:** JSON summary report, GIS-ready RFC 7946 GeoJSON (`format="geojson"`), or tabular CSV (`format="csv"`). ### 24. `generate_script(task_type: str, prompt: str = None, bbox: list = None, datetime_range: str = None, mode: str = "standalone") -> str` Empowers AI agents and researchers to autonomously synthesize complete, runnable Python geospatial scripts: - **`mode="standalone"`**: Generates self-contained, zero-dependency Python code that queries public STAC endpoints (`pystac_client`, `rasterio`, `numpy`, `shapely`) without requiring `eo-mcp` installed. - **`mode="sdk"`**: Generates modular Python scripts leveraging `eo_mcp` core analytical engines. **Supported Task Types:** `coastal_water_quality`, `maritime_patrol`, `coastal_erosion`, `inundation_model`, `spectral_indices`, `wildfire_dnbr`. ### 25. `validate_script(script_code: str) -> str` Performs Abstract Syntax Tree (AST) static security analysis on agent-generated Python code. Validates syntax and strictly blocks prohibited modules (`subprocess`, `os`, `shutil`, `socket`, `pty`, `ctypes`) and dangerous executions (`eval`, `exec`, `compile`, `__import__`). ### 26. `run_script(script_code: str, custom_context: str = None) -> str` Safely executes validated geospatial Python scripts inside an in-memory execution sandbox pre-loaded with `numpy`, `rasterio`, and `shapely`. Captures execution runtime, standard output, standard error, and returns structured result variables. ### 27. `synthesize_pipeline_code(recipe_name: str, bbox: list, datetime_range: str = None, output_format: str = "standalone") -> str` Transpiles declarative compound hazard recipes into standalone, executable Python scripts ready for Google Colab, Jupyter Notebooks, or headless serverless pipelines. --- ## Planetary Google Earth Engine (GEE) Tools `eo-mcp` bridges client-side cloud-native COG streaming with server-side planetary Google Earth Engine compute, supporting 50+ years of satellite archives (Landsat 1 MSS in 1972 through Sentinel-2 today): ### 28. `gee_init(project_id: str = None, service_account_key: str = None) -> str` Initializes the Earth Engine Python API session. Honors standard GCP credential resolution: direct parameters, service account JSON key, `GEE_PROJECT` or `GOOGLE_APPLICATION_CREDENTIALS` environment variables, or cached credentials from `earthengine authenticate`. ### 29. `gee_catalog_search(query: str, limit: int = 10) -> str` Instant multi-term keyword search across 880+ GEE public datasets using a cached index. Searches titles, tags, IDs, and providers without network crawling latency. ### 30. `gee_build_composite(year: int, location: str = None, bbox: list = None, aoi_geojson: str = None, season_start_month: int = 1, season_end_month: int = 12, method: str = "median", min_scenes: int = 3, max_cloud_cover: float = None) -> str` Builds a cloud-masked, harmonized multi-sensor composite using an automated fallback ladder across 50 years of archives. Automatically selects sensor priority by era (MSS, TM, ETM+, OLI, S2), unpacks QA bitmasks, harmonizes bands to standard names (`Blue`, `Green`, `Red`, `NIR`, `SWIR1`, `SWIR2`), and caches the result in an in-memory session registry returning a lightweight `composite_id` handle. ### 31. `gee_compute_indices(composite_id: str, indices: list = None, expression: str = None, output_band_name: str = "custom_index") -> str` Computes spectral indices (`NDVI`, `SAVI`, `EVI`, `NDMI`, `NBR`, `NDWI`, `NDBI`, `NDRE`, `CIre`, `GreenRed`, `BlueGreenNIR`) or evaluates custom band-math expressions. Automatically validates sensor era band feasibility and reports skipped indices. ### 32. `gee_thumbnail(composite_id: str, bands: list = None, min_val: float = 0.0, max_val: float = 0.3, palette: list = None, dimensions: int = 720) -> str` Generates an authentic high-resolution PNG thumbnail URL directly from Google Earth Engine. Follows the Deterministic Scientific Visual Mandate (Zero AI Hallucinations), rendering genuine satellite pixels. ### 33. `gee_zonal_stats(composite_id: str, reducers: list = None, bands: list = None, scale: int = None) -> str` Computes multi-reducer summary statistics (`mean`, `median`, `min`, `max`, `stdDev`, `sum`, `count`) over the composite region in a single server-side reduction call. ### 34. `gee_threshold_area(composite_id: str, band_name: str, operator: str, threshold: float, scale: int = None) -> str` Quantifies geodesic surface area in square kilometers ($km^2$) and square meters ($m^2$) meeting threshold criteria (e.g. `NDWI > 0.1` for water or `NDVI < 0.2` for barren soil) using `ee.Image.pixelArea()`, reporting coverage percentage of total region. ### 35. `gee_mask_by_raster(composite_id: str, mask_dataset_id: str, mask_band: str, mask_min: float = None, mask_max: float = None) -> str` Applies an ancillary raster mask (Copernicus DEM elevation/slope, SRTM, or ESA WorldCover classes) to an active composite. ### 36. `gee_sample_polygons(dataset_id: str = "ESA/WorldCover/v200", band: str = "Map", class_values: list = None, class_labels: list = None, location: str = None, bbox: list = None, points_per_class: int = 6, polygon_size_m: float = 180.0) -> str` Auto-generates labeled training polygons from categorical land cover products (e.g. ESA WorldCover) for machine learning classifiers, returning a GeoJSON FeatureCollection. ### 37. `gee_audit_factuality(composite_id: str = None, sensor: str = None, year: int = None, reducer: str = None, indices: list = None) -> str` Audits Earth Observation workflows for critical scientific assumptions (TOA vs SR calibration, cross-sensor spectral offsets, temporal smoothing bias) and synthesizes a declarative Mermaid flowchart of the processing pipeline. ### 38. `gee_execute_code(code: str) -> str` Execution escape hatch running arbitrary Python code with an initialized `ee` context for custom algorithms, returning stdout and structured result variables. --- ## Rich Interactive CLI Tools `eo-mcp` is also a full-featured operator CLI with Rich terminal tables, status badges, and GIS piping: ```bash # Coastal water quality, eutrophication & harmful algal bloom (HAB) analysis eo-mcp water-quality --bbox 22.70 38.80 22.95 38.95 --format summary eo-mcp water-quality --bbox 22.70 38.80 22.95 38.95 --format geojson > water_quality.geojson # Agentic script synthesis (generate standalone open-source Python code) eo-mcp script generate --task coastal_water_quality --mode standalone -o water_script.py eo-mcp script generate --task inundation_model --mode standalone -o flood_model.py # AST security validation & sandboxed execution eo-mcp script validate water_script.py eo-mcp script run water_script.py # Maritime surveillance & dark vessel tracking eo-mcp dark-vessels --bbox 24.5 59.8 25.2 60.2 --format summary eo-mcp dark-vessels --bbox 24.5 59.8 25.2 60.2 --format geojson > dark_vessels.geojson # Shoreline erosion transects (m/year) eo-mcp coastal-erosion --bbox -2.85 56.32 -2.75 56.38 --hist-date 2019-05-01/2019-08-31 --recent-date 2024-05-01/2024-08-31 # IPCC Sea Level Rise & Storm Surge Simulation with ASCII depth map eo-mcp sea-level-rise --bbox 22.8 38.6 23.2 38.9 --scenario SSP5-8.5 --surge 0.5 # Active wildfires & FRP thermal perimeter clustering eo-mcp wildfires --bbox -120.5 38.5 -120.0 39.0 --days 2 # Post-fire burn severity assessment (dNBR & EFFIS classification) eo-mcp burn-severity --bbox -120.5 38.5 -120.0 38.8 --pre-date 2024-05-01 --post-date 2024-07-01 # Land Surface Temperature & Urban Heat Island analysis (LST in °C) eo-mcp urban-heat --bbox 2.2 48.7 2.5 49.0 --date 2024-07-15 # Agricultural crop phenology & seasonal growth milestones (SOS/POS/EOS) eo-mcp crop-phenology --bbox -119.5 36.2 -119.4 36.3 --year 2024 --crop Almonds # Atmospheric gas emissions & OpenAQ ground stations eo-mcp emissions --bbox 2.2 48.7 2.5 49.0 --gas NO2 # Reservoir drought depletion & water loss dynamics eo-mcp drought --bbox -4.5 37.5 -4.0 38.0 --hist-year 2019 --recent-year 2024 # Compound hazard pipeline orchestration eo-mcp pipeline list eo-mcp pipeline run --recipe coastal_water_quality_eutrophication --location "Fthiotida, Greece" --format summary # Inspect or configure provider credentials eo-mcp auth eo-mcp auth set --provider cdse --username user@example.com --password secret ``` --- ## Architecture ```mermaid flowchart TD subgraph Clients["AI Agent Clients"] Claude["Claude Desktop"] Cursor["Cursor IDE"] Antigravity["Antigravity / Local LLMs"] CLI_User["Operator CLI (`eo-mcp`)"] end subgraph Server["eo-mcp Server (FastMCP / JSON-RPC 2.0)"] CLI["Rich Terminal CLI Engine"] Router["Catalog Router & Geocoder"] subgraph WorkflowTools["Ergonomic & Pipeline Orchestrator Tools"] W1["run_pipeline"] W2["list_pipeline_recipes"] W3["assess_location_hazard"] W4["environmental_site_audit"] end subgraph StandardTools["Standard Satellite Tools"] T1["stac_search"] T2["calculate_spectral_index"] T3["get_elevation_profile"] T4["detect_water_sar"] T5["run_geospatial_script"] end subgraph PlanetaryTools["Planetary Hazard & Climate Tools"] P1["detect_dark_vessels"] P2["analyze_coastal_erosion"] P3["simulate_sea_level_rise"] P4["detect_active_wildfires"] P5["monitor_atmospheric_emissions"] P6["analyze_reservoir_drought"] end subgraph Core["Zero-Infrastructure Analysis Engines"] COG["Windowed COG Streamer (/vsicurl/)"] CFAR["CA-CFAR Radar & AIS Correlator"] DSAS["MNDWI & DSAS Transect Engine"] Bathtub["8-Connected Bathtub Flood Filler"] FIRMS["Thermal Anomaly & FRP Clusterer"] TROPOMI["S5P Column & OpenAQ Correlator"] GSW["Surface Water Dynamics Engine"] PipeEng["Declarative In-Memory Pipeline Engine"] end end subgraph Archives["Free Zero-Config Public APIs & STAC Servers"] AWS["AWS Earth Search STAC (S2, Landsat, Cop-DEM)"] CDSE["Copernicus Dataspace (Sentinel-1, Sentinel-5P)"] AIS_API["Digitraffic Baltic Live AIS API"] NASA_API["NASA FIRMS Open Fire API"] OPENAQ_API["OpenAQ Air Quality REST API"] JRC_API["EC JRC Global Surface Water COGs"] OSM_API["OpenStreetMap Overpass API (Public Infrastructure)"] end Clients -->|stdio / SSE / CLI| Server CLI --> Router Router --> WorkflowTools Router --> StandardTools Router --> PlanetaryTools WorkflowTools --> Core StandardTools --> Core PlanetaryTools --> Core Core --> Archives ``` --- ### 💻 User-Defined Pipeline CLI Quickstart ```bash # 1. List available compound hazard recipes eo-mcp pipeline list # 2. Inspect recipe steps, parameters, and input/output contracts eo-mcp pipeline describe coastal_storm_surge_infrastructure_exposure # 3. Execute pre-built compound hazard recipe eo-mcp pipeline run --recipe coastal_storm_surge_infrastructure_exposure --location "Valencia, Spain" --format summary # 4. Export compound multi-hazard GIS layers (RFC 7946 GeoJSON) eo-mcp pipeline run --recipe compound_wildfire_runoff_risk --location "Rhodes, Greece" --format geojson > wildfire_debris_flow.geojson # 5. Execute custom user-defined JSON pipeline specification eo-mcp pipeline run --spec my_pipeline.json --location "Venice, Italy" --format summary ``` --- ## Cloud-Native COG Streaming (How it works) Traditional GIS downloads entire satellite scenes (often 800MB to 1.5GB) before clipping to the study area. When interacting with an AI agent over the internet, downloading gigabytes of data per query creates latency and exhausts disk space. `eo-mcp` uses **Cloud Optimized GeoTIFF (COG)** range streaming: 1. The server reads the COG header over HTTP (~10KB) to discover internal tile offsets and coordinate projection. 2. It translates the user's bounding box into pixel window coordinates. 3. It performs targeted HTTP `Range: bytes=...` requests to pull **only the pixels covering the bounding box**. 4. A typical query transfers **2 MB to 5 MB** of data and completes in under 2 seconds. --- ## Flexible Credential Management `eo-mcp` is designed with a **zero-friction, open-first philosophy**: 1. **Zero-Config Default (100% Free)**: Out of the box, `eo-mcp` streams satellite data from free open government cloud archives (AWS Earth Search, NASA CMR, Copernicus public STAC). No user registration, API keys, or credit cards are needed. 2. **First-Class Support for User Credentials**: When users or enterprise agents need to access authenticated or proprietary features (such as direct Copernicus CDSE full-granule downloads, NASA Earthdata restricted collections, or Microsoft Planetary Computer signed tokens), credentials can be provided at any time: - **Environment Variables (`.env`)**: ```bash export CDSE_USERNAME="your-cdse-email" export CDSE_PASSWORD="your-cdse-password" export EARTHDATA_TOKEN="your-nasa-token" export PC_SDK_SUBSCRIPTION_KEY="your-pc-key" export FIRMS_MAP_KEY="your-firms-key" ``` - **In-Session Dynamic Tool (`configure_credentials`)**: AI agents can invoke `configure_credentials(provider="cdse", username="...", password="...")` dynamically during the conversation without restarting the server. - **CLI Credential Command**: ```bash eo-mcp auth set --provider cdse --username "user@example.com" --password "secret" eo-mcp auth ``` --- ## Scientific Foundation & Literature All algorithms, spectral indices, radar clutter models, and environmental hazard metrics in `eo-mcp` are strictly grounded in canonical peer-reviewed scientific literature, geodetic standards, and European space policy frameworks (such as the EU Floods Directive 2007/60/EC, Marine Strategy Framework Directive MSFD Descriptor 8, and EU Climate Adaptation Strategy). Detailed technical derivations, equations, calibration constants, and validation protocols are documented in [`docs/methodology/`](docs/methodology/overview.md): | Domain & Tool | Methodology Document | Primary Scientific Literature | Policy & Standard Framework | | :--- | :--- | :--- | :--- | | **Optical Spectral Indices**<br>`calculate_spectral_index` | [`docs/methodology/spectral_indices.md`](docs/methodology/spectral_indices.md) | • Rouse et al. (1974) [NASA SP-351]<br>• Tucker (1979) [DOI: 10.1016/0034-4257(79)90013-0](https://doi.org/10.1016/0034-4257(79)90013-0)<br>• McFeeters (1996) [DOI: 10.1080/01431169608948714](https://doi.org/10.1080/01431169608948714)<br>• Gao (1996) [DOI: 10.1016/S0034-4257(96)00067-3](https://doi.org/10.1016/S0034-4257(96)00067-3)<br>• Huete et al. (2002) [DOI: 10.1016/S0034-4257(02)00096-2](https://doi.org/10.1016/S0034-4257(02)00096-2) | CEOS Cal/Val Standards | | **Maritime Radar & Dark Vessels**<br>`detect_dark_vessels` | [`docs/methodology/maritime_radar.md`](docs/methodology/maritime_radar.md) | • Finn & Johnson (1968) CA-CFAR<br>• Novak et al. (1993)<br>• Crisp (2004) [DSTO-RR-0272]<br>• Stasolla & Greidanus (2016) [DOI: 10.1080/2150704X.2016.1226522](https://doi.org/10.1080/2150704X.2016.1226522)<br>• Pelich et al. (2019) [DOI: 10.3390/rs11091078](https://doi.org/10.3390/rs11091078)<br>• Alpers & Hühnerfuss (1988) [DOI: 10.1029/JC093iC04p03642](https://doi.org/10.1029/JC093iC04p03642) | MSFD Descriptor 8<br>European Maritime Safety Agency (EMSA) | | **Coastal Erosion & Shorelines**<br>`analyze_coastal_erosion` | [`docs/methodology/coastal_dynamics.md`](docs/methodology/coastal_dynamics.md) | • Xu (2006) MNDWI [DOI: 10.1080/01431160600589179](https://doi.org/10.1080/01431160600589179)<br>• Otsu (1979) [DOI: 10.1109/TSMC.1979.4310076](https://doi.org/10.1109/TSMC.1979.4310076)<br>• Thieler et al. (2009) [DOI: 10.3133/ofr20081278](https://doi.org/10.3133/ofr20081278)<br>• Himmelstoss et al. (2018) [DOI: 10.3133/ofr20181179](https://doi.org/10.3133/ofr20181179)<br>• Vos et al. (CoastSat, 2019) [DOI: 10.1016/j.envsoft.2019.104528](https://doi.org/10.1016/j.envsoft.2019.104528) | EU Climate Adaptation Strategy<br>USGS DSAS Protocol | | **Sea Level Rise Inundation**<br>`simulate_sea_level_rise` | [`docs/methodology/inundation_dem.md`](docs/methodology/inundation_dem.md) | • Poulter & Halpin (2008) [DOI: 10.1080/13658810701371858](https://doi.org/10.1080/13658810701371858)<br>• Gesch (2009, 2018) [DOI: 10.3389/feart.2018.00230](https://doi.org/10.3389/feart.2018.00230)<br>• Fox-Kemper et al. (IPCC AR6, 2021) [DOI: 10.1017/9781009157896.011](https://doi.org/10.1017/9781009157896.011) | EU Floods Directive (2007/60/EC Art. 6)<br>IPCC SSP Scenarios | | **Active Wildfires & FRP**<br>`detect_active_wildfires` | [`docs/methodology/thermal_wildfires.md`](docs/methodology/thermal_wildfires.md) | • Schroeder et al. (VIIRS 375m, 2014) [DOI: 10.1016/j.rse.2013.12.008](https://doi.org/10.1016/j.rse.2013.12.008)<br>• Giglio et al. (MODIS C6, 2016) [DOI: 10.1016/j.rse.2016.02.054](https://doi.org/10.1016/j.rse.2016.02.054)<br>• Wooster (2003) [DOI: 10.1016/S0034-4257(03)00070-1](https://doi.org/10.1016/S0034-4257(03)00070-1)<br>• Wooster et al. (2005) [DOI: 10.1029/2005JD006318](https://doi.org/10.1029/2005JD006318) | EFFIS European Forest Fire Information System<br>NASA FIRMS | | **Post-Fire Burn Severity**<br>`calculate_burn_severity` | [`docs/methodology/thermal_wildfires.md`](docs/methodology/thermal_wildfires.md) | • Key & Benson (FIREMON dNBR, 2006) [USDA GTR-RMRS-164]<br>• Parks et al. (RBR, 2014) [DOI: 10.3390/rs6031827](https://doi.org/10.3390/rs6031827) | USGS & EFFIS Burn Severity Scale | | **Tropospheric Emissions**<br>`monitor_atmospheric_emissions` | [`docs/methodology/atmospheric_chemistry.md`](docs/methodology/atmospheric_chemistry.md) | • Veefkind et al. (TROPOMI, 2012) [DOI: 10.1016/j.rse.2011.09.027](https://doi.org/10.1016/j.rse.2011.09.027)<br>• van Geffen et al. (2020) [DOI: 10.5194/amt-13-1315-2020](https://doi.org/10.5194/amt-13-1315-2020) | EU Ambient Air Quality Directive (2008/50/EC)<br>EU Methane Regulation (2024/1787) | | **Surface Water & Drought**<br>`analyze_reservoir_drought` | [`docs/methodology/surface_water_drought.md`](docs/methodology/surface_water_drought.md) | • Pekel, Cottam, Gorelick, & Belward (2016 Nature) [DOI: 10.1038/nature20584](https://doi.org/10.1038/nature20584) | Water Framework Directive (WFD 2000/60/EC)<br>UN SDG Indicator 6.6.1 | | **Urban Heat Island & LST**<br>`analyze_urban_heat_island` | [`docs/methodology/urban_heat_island.md`](docs/methodology/urban_heat_island.md) | • Valor & Caselles (1996) [DOI: 10.1016/0034-4257(96)00039-9](https://doi.org/10.1016/0034-4257(96)00039-9)<br>• Sobrino et al. (2004, 2008) [DOI: 10.1016/j.rse.2004.02.003](https://doi.org/10.1016/j.rse.2004.02.003)<br>• Jiménez-Muñoz et al. (2009) [DOI: 10.1109/TGRS.2008.2007125](https://doi.org/10.1109/TGRS.2008.2007125) | EU Green Deal Climate Resilience | | **Crop Phenology Dynamics**<br>`monitor_crop_phenology` | [`docs/methodology/crop_phenology.md`](docs/methodology/crop_phenology.md) | • Reed et al. (1994) [DOI: 10.2307/3235884](https://doi.org/10.2307/3235884)<br>• Zhang et al. (2003) [DOI: 10.1016/S0034-4257(02)00135-9](https://doi.org/10.1016/S0034-4257(02)00135-9)<br>• Jönsson & Eklundh (TIMESAT, 2004) [DOI: 10.1016/j.cageo.2004.05.006](https://doi.org/10.1016/j.cageo.2004.05.006) | Common Agricultural Policy (CAP)<br>Agro-Climatic Monitoring | | **Topography & Radar Hydrology**<br>`get_elevation_profile`, `detect_water_sar` | [`docs/methodology/inundation_dem.md`](docs/methodology/inundation_dem.md) | • Horn (1981) [DOI: 10.1109/PROC.1981.11918](https://doi.org/10.1109/PROC.1981.11918)<br>• Zevenbergen & Thorne (1987) [DOI: 10.1002/esp.3290120107](https://doi.org/10.1002/esp.3290120107)<br>• Guth & Geoffroy (2021) [DOI: 10.1111/tgis.12825](https://doi.org/10.1111/tgis.12825)<br>• Twele et al. (2016) [DOI: 10.1080/01431161.2016.1192304](https://doi.org/10.1080/01431161.2016.1192304)<br>• Bioresita et al. (2018) [DOI: 10.3390/rs10020217](https://doi.org/10.3390/rs10020217) | Copernicus DEM Validation Report (ESA, 2020)<br>Copernicus Emergency Management Service (CEMS) | For the complete formal bibliography and BibTeX database, inspect [`docs/methodology/references.md`](docs/methodology/references.md) and [`docs/methodology/references.bib`](docs/methodology/references.bib). --- ## Contributing `eo-mcp` is an open-source project by [sounny.com](https://sounny.com) to establish an open standard for agentic Earth Observation. Contributions are warmly welcome! --- ## License & Legal Licensed under the **Apache License, Version 2.0**. See [LICENSE](LICENSE) for details. - [Contact Maintainer](https://eo-mcp.github.io/contact.html) - [Privacy Policy](https://eo-mcp.github.io/privacy.html) - [Terms of Use](https://eo-mcp.github.io/terms.html)