pybme-mcp
A Model Context Protocol (MCP) server that wraps pyBME — enabling LLM agents to perform Bayesian Maximum Entropy geostatistical analysis through natural-language intent rather than code.
What it does
The server exposes 7 tools, 6 resources, and 4 prompts that form an uncertainty-reasoning pipeline:
ingest_external_scenario_evidence → inspect_modeling_context
→ fit_uncertainty_model → run_uncertainty_update
→ explain_uncertainty_drivers
→ compare_operator_approaches
→ design_next_observation_or_scenario
Tools
| Tool | Purpose |
|---|---|
ingest_external_scenario_evidence | Import hard/soft observations and network topology |
inspect_modeling_context | Detect problem type and recommend model families |
fit_uncertainty_model | Fit spatial or network covariance models with cross-validation |
run_uncertainty_update | Run BME prediction at estimation targets |
explain_uncertainty_drivers | Identify what drives uncertainty at specific locations |
compare_operator_approaches | Compare Euclidean vs graph vs physics-informed operators |
design_next_observation_or_scenario | Rank candidate sensor placements by variance reduction |
Supported model families
- spatial — Euclidean covariance (exponential, gaussian, spherical, …)
- space_time — Separable space-time covariance
- graph_laplacian — Graph-diffusion kernel on network topology
- physics_informed_network — Physically consistent network covariance
- spectral_hodge — Spectral Hodge decomposition for flow networks
Install
Install pyBME first (not yet on PyPI):
pip install git+https://github.com/wiesnerfriedman/pybme.git
Then install the MCP server:
pip install git+https://github.com/wiesnerfriedman/pybme-mcp.git
Or from a local clone:
git clone https://github.com/wiesnerfriedman/pybme-mcp.git
cd pybme-mcp
pip install -e ".[dev]"
Configuration
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"pybme": {
"command": "pybme-mcp"
}
}
}
VS Code (Copilot)
Add to .vscode/mcp.json:
{
"servers": {
"pybme": {
"type": "stdio",
"command": "pybme-mcp"
}
}
}
Usage
Once configured, ask your agent things like:
- "Fit an uncertainty model to my PM2.5 observations"
- "Run a network-aware BME update on this stormwater network"
- "Compare Euclidean vs graph Laplacian operators"
- "Where should I place the next sensor to reduce uncertainty the most?"
See examples/mcp_agent_demo.ipynb for a step-by-step walkthrough of the full tool chain.
Development
git clone https://github.com/wiesnerfriedman/pybme-mcp.git
cd pybme-mcp
pip install -e ".[dev]"
pytest
Layout
pybme-mcp/
├── docs/
│ ├── pybme-openswmm-integration.md
│ └── v1-mcp-spec.md
├── examples/
│ └── mcp_agent_demo.ipynb
├── pyproject.toml
├── src/pybme_mcp/
│ ├── __init__.py
│ ├── __main__.py
│ ├── registry.py
│ ├── schemas.py
│ ├── serialisation.py
│ ├── server.py
│ └── services/
│ ├── catalog.py
│ ├── comparison.py
│ ├── context.py
│ ├── explanation.py
│ ├── fitting.py
│ ├── hodge.py
│ ├── ingest.py
│ ├── scenario_design.py
│ └── update.py
└── tests/
├── conftest.py
├── test_ingest.py
└── test_integration.py
License
MIT