Rxinfer Fastmcp Example
A simple example to demonstrate how to call RxInfer from Python's code and return the result to an LLM with MCP protocolg2
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RxInfer FastMCP Example
An MCP server that exposes Bayesian house price estimation powered by RxInfer.jl. Point it at a CSV of housing data, and it learns a probabilistic pricing model that predicts prices with honest uncertainty -- when information is missing, the model says "I'm less sure" instead of silently guessing.
Built with FastMCP, JuliaCall, and uv.
Tools
| Tool | Description |
|---|---|
run_bayesian_analysis | Load a CSV file and train the Bayesian regression model |
describe_data | Inspect the loaded dataset -- columns, stats, and what the model recognizes |
estimate_house_price | Predict price for a house (any features can be omitted) |
The CSV should have a price column and any of: bedrooms, bathrooms, living_area, floors, basement_area, yr_built, label.
Installation
uv sync
Julia dependencies are managed automatically by juliapkg. If you hit issues, see the juliapkg docs.
Usage
Start the server in the background before connecting any MCP client:
uv run fastmcp run main.py:mcp --transport sse --port 8001
The first startup is slow (~30s) because Julia compiles packages and generates a test CSV. Subsequent runs use the compiled cache and start much faster. Wait until you see the server listening message before proceeding.
Pluto Notebook
The Bayesian model is developed in example.jl, a Pluto.jl reactive notebook. To explore or modify the model interactively, install Pluto following the instructions on the website, then open the notebook:
using Pluto; Pluto.run()
This opens a browser-based environment where you can open example.jl, tweak the model, and see results update in real time.
Claude Desktop
To connect via SSE in Claude Desktop, add the following to your Claude Desktop MCP configuration:
{
"mcpServers": {
"rxinfer": {
"command": "npx",
"args": [
"mcp-remote@latest",
"http://localhost:8001/sse"
]
}
}
}
Make sure the server is running before launching Claude Desktop.
Claude Code
A .mcp.json is included that connects to the server on localhost:8001. Start the server first in a separate terminal:
uv run fastmcp run main.py:mcp --transport sse --port 8001
Then launch Claude Code in the project directory. The server will be detected automatically β approve it when prompted.
Example conversation
Ask Claude to run Bayesian analysis on the test dataset (created automatically when starting the server)
Prompt it to estimate a price for some house, some features might be missing
Think of the situation where you actually got the missing data about the previously unknown features and check the new result
