eo-processor-mcp

An MCP (Model Context Protocol) server for Earth Observation processing, powered by eo-processor.

Similar to stac-mcp for STAC catalog operations, this server exposes eo-processor's high-performance Rust-accelerated EO computation functions as MCP tools that AI agents can invoke.

Features

• 15 MCP tools covering spectral indices, change detection, temporal statistics, masking, morphology, distances, trend analysis, classification, texture features, zonal statistics, and more
• Rust-accelerated computation via eo-processor's PyO3 bindings (GIL-released, multi-core parallel)
• stdio and HTTP transport support
• Observability built in (structured logging, metrics, correlation IDs, latency histograms)
• Prompts to guide AI agents in using the tools effectively

Tools

Tool	Description
compute_spectral_index	Compute spectral indices (NDVI, NDWI, EVI, SAVI, etc.) from band .npy files
compute_change_index	Change detection (delta_ndvi, delta_nbr, dnbr, rbr) from pre/post imagery
temporal_statistics	Median, mean, std, sum along the time axis
temporal_composite	Weighted temporal composite of 4D arrays
moving_average	Moving average with optional stride/downsampling
apply_mask	Unified masking (by values, range, SCL, NaN replacement)
morphological_operation	Binary dilation, erosion, opening, closing
compute_distances	Pairwise distances (euclidean, manhattan, chebyshev, minkowski)
analyze_trends	Linear regression and trend analysis with break detection
bfast_monitor	BFAST Monitor change detection on time series
classify	Random Forest train/predict, complex multi-band classification
texture_features	Haralick/GLCM texture features
zonal_statistics	Zonal statistics (count, sum, mean, min, max, std per zone)
pixelwise_transform	Linear transform with optional clamping
list_capabilities	List all available indices, operations, and tools

Installation

pip install -e .
# or with uv
uv pip install -e .

Usage

stdio (default)

eo-processor-mcp
# or
python -m eo_processor_mcp

HTTP/SSE

EO_PROCESSOR_MCP_TRANSPORT=http python -m eo_processor_mcp

MCP Client Configuration

{
  "eo-processor": {
    "command": "uvx",
    "args": ["--from", "git+https://github.com/BnJam/eo-processor-mcp", "eo-processor-mcp"],
    "transport": "stdio"
  }
}

Container (GHCR)

A pre-built image is published to GitHub Container Registry on every merge to main:

docker pull ghcr.io/bnjam/eo-processor-mcp:latest
docker run --rm ghcr.io/bnjam/eo-processor-mcp:latest

Available tags:

• latest — most recent main build
• sha-<short-sha> — immutable build for a specific commit
• <X.Y.Z>, <X>.<Y>, <X> — semver tags (only when a version bump occurs)

To use the container with an MCP client:

{
  "eo-processor": {
    "command": "docker",
    "args": ["run", "--rm", "ghcr.io/bnjam/eo-processor-mcp:latest"],
    "transport": "stdio"
  }
}

Data Format

All tools accept NumPy .npy file paths as input and write results to .npy files. Tool responses include:

• output_path: Path to the result .npy file
• stats: Summary statistics (shape, dtype, min, max, mean, std, NaN count)

Environment Variables

Variable	Default	Purpose
EO_PROCESSOR_MCP_TRANSPORT	stdio	Transport mode (stdio, http, streamable-http, sse)
EO_PROCESSOR_MCP_HOST	127.0.0.1	HTTP host
EO_PROCESSOR_MCP_PORT	8000	HTTP port
EO_PROCESSOR_MCP_LOG_LEVEL	WARNING	Logging level
EO_PROCESSOR_MCP_LOG_FORMAT	text	Log format (text or json)
EO_PROCESSOR_MCP_ENABLE_METRICS	true	Enable in-process metrics
EO_PROCESSOR_MCP_ENABLE_TRACE	false	Enable trace span logging

Development

make install-dev    # Install with dev dependencies
make test           # Run tests
make lint           # Run ruff
make format         # Auto-format
make coverage       # Coverage report

License

Apache-2.0