Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@pybme-mcp Fit an uncertainty model to my PM2.5 observations"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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.
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_scenarioTool | Purpose |
| Import hard/soft observations and network topology |
| Detect problem type and recommend model families |
| Fit spatial or network covariance models with cross-validation |
| Run BME prediction at estimation targets |
| Identify what drives uncertainty at specific locations |
| Compare Euclidean vs graph vs physics-informed operators |
| Rank candidate sensor placements by variance reduction |
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
Related MCP server: dssat-mcp
Install pyBME first (not yet on PyPI):
pip install git+https://github.com/wiesnerfriedman/pybme.gitThen install the MCP server:
pip install git+https://github.com/wiesnerfriedman/pybme-mcp.gitOr from a local clone:
git clone https://github.com/wiesnerfriedman/pybme-mcp.git
cd pybme-mcp
pip install -e ".[dev]"Add to claude_desktop_config.json:
{
"mcpServers": {
"pybme": {
"command": "pybme-mcp"
}
}
}Add to .vscode/mcp.json:
{
"servers": {
"pybme": {
"type": "stdio",
"command": "pybme-mcp"
}
}
}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.
git clone https://github.com/wiesnerfriedman/pybme-mcp.git
cd pybme-mcp
pip install -e ".[dev]"
pytestpybme-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.pyMIT
This server cannot be deployed
Verifiable Earth ground truth for AI agents: water, hazard, ground stability, resource, with proof.
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
Agent-to-agent reasoning-as-a-service: chain-of-thought, analysis, and decision support.