stac-mcp 0.3.0

An MCP Server for STAC requests

STAC MCP Server

An MCP (Model Context Protocol) Server that provides access to STAC (SpatioTemporal Asset Catalog) APIs for geospatial data discovery and access. Supports dual output modes (text and structured json) for all tools.

Overview

This MCP server enables AI assistants and applications to interact with STAC catalogs to:

• Search and browse STAC collections
• Find geospatial datasets (satellite imagery, weather data, etc.)
• Access metadata and asset information
• Perform spatial and temporal queries

Features

Available Tools

All tools accept an optional output_format parameter ("text" default, or "json"). JSON mode returns a single MCP TextContent whose text field is a compact JSON envelope: { "mode": "json", "data": { ... } } (or { "mode": "text_fallback", "content": ["..."] } if a handler lacks a JSON branch). This preserves backward compatibility while enabling structured consumption (see ADR 0006 and ASR 1003).

• search_collections: List and search available STAC collections
• get_collection: Get detailed information about a specific collection
• search_items: Search for STAC items with spatial, temporal, and attribute filters
• get_item: Get detailed information about a specific STAC item
• estimate_data_size: Estimate data size for STAC items using lazy loading (XArray + odc.stac)

Data Size Estimation

The estimate_data_size tool provides accurate size estimates for geospatial datasets without downloading the actual data:

• Lazy Loading: Uses odc.stac to load STAC items into xarray datasets without downloading
• AOI Clipping: Automatically clips to the smallest area when both bbox and AOI GeoJSON are provided
• Fallback Estimation: Provides size estimates even when odc.stac fails
• Detailed Metadata: Returns information about data variables, spatial dimensions, and individual assets
• Batch Support: Retains structured metadata for efficient batch processing

Example usage:

{
  "collections": ["landsat-c2l2-sr"],
  "bbox": [-122.5, 37.7, -122.3, 37.8],
  "datetime": "2023-01-01/2023-01-31",
  "aoi_geojson": {
    "type": "Polygon",
    "coordinates": [[...]]
  },
  "limit": 50
}

Supported STAC Catalogs

By default, the server connects to Microsoft Planetary Computer STAC API, but it can be configured to work with any STAC-compliant catalog.

Installation

PyPI Package

pip install stac-mcp

Development Installation

git clone https://github.com/BnJam/stac-mcp.git
cd stac-mcp
pip install -e .

Container

The STAC MCP server publishes multi-arch container images (linux/amd64, linux/arm64) via GitHub Actions workflow (.github/workflows/container.yml). The current build uses a Python 3.12 slim Debian base (not distroless) with GDAL-related libs for raster IO and odc-stac compatibility.

# Pull the latest stable version
docker pull ghcr.io/bnjam/stac-mcp:latest

# Pull a specific version (recommended for production)
docker pull ghcr.io/bnjam/stac-mcp:0.2.0

# Run the container (uses stdio transport for MCP)
docker run --rm -i ghcr.io/bnjam/stac-mcp:latest

Container images are tagged with semantic versions when version bumps occur on main:

• ghcr.io/bnjam/stac-mcp:X.Y.Z (exact version)
• ghcr.io/bnjam/stac-mcp:X.Y (major.minor convenience tag)
• ghcr.io/bnjam/stac-mcp:X (major convenience tag)
• ghcr.io/bnjam/stac-mcp:latest (points at current main version) Pull request builds (without version bump) also produce ephemeral PR/ref tags via the metadata action.

Building the Container

To build the container locally using the provided Containerfile:

# Build with Docker
docker build -f Containerfile -t stac-mcp .

# Or build with Podman  
podman build -f Containerfile -t stac-mcp .

The Containerfile currently performs a single-stage build based on python:3.12-slim (future optimization could reintroduce a distroless runtime stage). It installs system GDAL/PROJ dependencies and then installs the package.

Usage

As an MCP Server

Native Installation

Configure your MCP client to connect to this server:

{
  "mcpServers": {
    "stac": {
      "command": "stac-mcp"
    }
  }
}

Container Usage

To use the containerized version with an MCP client:

{
  "mcpServers": {
    "stac": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "ghcr.io/bnjam/stac-mcp:latest"]
    }
  }
}

Or with Podman:

{
  "mcpServers": {
    "stac": {
      "command": "podman", 
      "args": ["run", "--rm", "-i", "ghcr.io/bnjam/stac-mcp:latest"]
    }
  }
}

docker run --rm -i ghcr.io/bnjam/stac-mcp:latest

Command Line

Native Installation

stac-mcp

Each invocation starts an MCP stdio server; it waits for protocol messages (see examples/example_usage.py).

Container Usage

# With Docker
docker run --rm -i ghcr.io/bnjam/stac-mcp:latest

# With Podman
podman run --rm -i ghcr.io/bnjam/stac-mcp:latest

Examples

Example: JSON Output Mode

Below is an illustrative (client-side) pseudo-call showing output_format usage through an MCP client message:

{
  "method": "tools/call",
  "params": {
    "name": "search_items",
    "arguments": {
      "collections": ["landsat-c2l2-sr"],
      "bbox": [-122.5, 37.7, -122.3, 37.8],
      "datetime": "2023-01-01/2023-01-31",
      "limit": 5,
      "output_format": "json"
    }
  }
}

The server responds with a single TextContent whose text is a JSON string like:

{"mode":"json","data":{"type":"item_list","count":5,"items":[{"id":"..."}]}}

This wrapping keeps the MCP content type stable while enabling machine-readable chaining.

Development

Setup

git clone https://github.com/BnJam/stac-mcp.git
cd stac-mcp
pip install -e ".[dev]"

Testing

pytest -v
python examples/example_usage.py  # MCP stdio smoke test

Linting

black stac_mcp/
ruff check stac_mcp/

Version Management

The project uses semantic versioning (SemVer) with automated version management based on branch naming, implemented in .github/workflows/container.yml:

Branch-Based Automatic Versioning

When PRs are merged to main, the workflow inspects the merged branch name (via the PR head ref) and increments the version if it matches a prefix:

• hotfix/ branches → patch increment (0.1.0 → 0.1.1) for bug fixes
• feature/ branches → minor increment (0.1.0 → 0.2.0) for new features
• release/ branches → major increment (0.1.0 → 1.0.0) for breaking changes

Manual Version Management

You can also manually manage versions using the version script (should normally not be needed unless doing a coordinated release):

# Show current version
python scripts/version.py current

# Increment version based on change type
python scripts/version.py patch    # Bug fixes (0.1.0 -> 0.1.1)
python scripts/version.py minor    # New features (0.1.0 -> 0.2.0)  
python scripts/version.py major    # Breaking changes (0.1.0 -> 1.0.0)

# Set specific version
python scripts/version.py set 1.2.3

The version system maintains consistency across:

• pyproject.toml (project version)
• stac_mcp/__init__.py (version)
• stac_mcp/server.py (server_version in MCP initialization)

Container Development

To develop with containers:

# Build development image
docker build -f Containerfile -t stac-mcp:dev .

# Test the container
docker run --rm -i stac-mcp:dev

# Using docker-compose for development
docker-compose up --build

# For debugging, use an interactive shell (requires modifying Containerfile)
# docker run --rm -it --entrypoint=/bin/sh stac-mcp:dev

Current Containerfile (single-stage) notes:

• Based on python:3.12-slim for broad wheel compatibility (rasterio, shapely, etc.)
• Installs GDAL/PROJ system libraries needed by rasterio/odc-stac
• Installs the package with pip install .
• Entrypoint: python -m stac_mcp.server (stdio MCP transport)
• Multi-stage/distroless hardening can be reintroduced later (tracked by potential future ADR)

STAC Resources

• STAC Specification
• pystac-client Documentation
• Microsoft Planetary Computer
• AWS Earth Search

License

Apache 2.0 - see LICENSE file for details.