Skip to main content
Glama
Sign In
deenesjakoruzh

Bayesian MCP

by wrenchchatrepo
GitHub
Python
MIT License
OverviewInspectNewSchemaRelated ServersReviewsScore
Need Help?View Source CodeReport Issue

Integrations

  • Uses FastAPI to expose the Bayesian MCP server API endpoints for model creation, belief updating, prediction generation, model comparison, and visualization.

  • Provides installation instructions for cloning the repository from GitHub to set up the Bayesian MCP server.

  • Utilizes NumPy for numerical computations in Bayesian inference, handling arrays and mathematical operations for probabilistic models.

Bayesian MCP

A Model Calling Protocol (MCP) server for Bayesian reasoning, inference, and belief updating. This tool enables LLMs to perform rigorous Bayesian analysis and probabilistic reasoning.

Features

  • 🧠 Bayesian Inference: Update beliefs with new evidence using MCMC sampling
  • 📊 Model Comparison: Compare competing models using information criteria
  • 🔮 Predictive Inference: Generate predictions with uncertainty quantification
  • 📈 Visualization: Create visualizations of posterior distributions
  • 🔌 MCP Integration: Seamlessly integrate with any LLM that supports MCP

Installation

Development Installation

Clone the repository and install dependencies:

git clone https://github.com/wrenchchatrepo/bayesian-mcp.git cd bayesian-mcp pip install -e .

Requirements

  • Python 3.9+
  • PyMC 5.0+
  • ArviZ
  • NumPy
  • Matplotlib
  • FastAPI
  • Uvicorn

Quick Start

Starting the Server

# Run with default settings python bayesian_mcp.py # Specify host and port python bayesian_mcp.py --host 0.0.0.0 --port 8080 # Set log level python bayesian_mcp.py --log-level debug

The server will start and listen for MCP requests on the specified host and port.

API Usage

The Bayesian MCP server exposes several functions through its API:

1. Create Model

Create a new Bayesian model with specified variables.

# MCP Request { "function_name": "create_model", "parameters": { "model_name": "my_model", "variables": { "theta": { "distribution": "normal", "params": {"mu": 0, "sigma": 1} }, "likelihood": { "distribution": "normal", "params": {"mu": "theta", "sigma": 0.5}, "observed": [0.1, 0.2, 0.3, 0.4] } } } }

2. Update Beliefs

Update model beliefs with new evidence.

# MCP Request { "function_name": "update_beliefs", "parameters": { "model_name": "my_model", "evidence": { "data": [0.1, 0.2, 0.3, 0.4] }, "sample_kwargs": { "draws": 1000, "tune": 1000, "chains": 2 } } }

3. Make Predictions

Generate predictions using the posterior distribution.

# MCP Request { "function_name": "predict", "parameters": { "model_name": "my_model", "variables": ["theta"], "conditions": { "x": [1.0, 2.0, 3.0] } } }

4. Compare Models

Compare multiple models using information criteria.

# MCP Request { "function_name": "compare_models", "parameters": { "model_names": ["model_1", "model_2"], "metric": "waic" } }

5. Create Visualization

Generate visualizations of model posterior distributions.

# MCP Request { "function_name": "create_visualization", "parameters": { "model_name": "my_model", "plot_type": "trace", "variables": ["theta"] } }

Examples

The examples/ directory contains several examples demonstrating how to use the Bayesian MCP server:

Linear Regression

A simple linear regression example to demonstrate parameter estimation:

python examples/linear_regression.py

A/B Testing

An example of Bayesian A/B testing for conversion rates:

python examples/ab_test.py

Supported Distributions

The Bayesian engine supports the following distributions:

  • normal: Normal (Gaussian) distribution
  • lognormal: Log-normal distribution
  • beta: Beta distribution
  • gamma: Gamma distribution
  • exponential: Exponential distribution
  • uniform: Uniform distribution
  • bernoulli: Bernoulli distribution
  • binomial: Binomial distribution
  • poisson: Poisson distribution
  • deterministic: Deterministic transformation

MCP Integration

This server implements the Model Calling Protocol, making it compatible with a wide range of LLMs and frameworks. To use it with your LLM:

import requests response = requests.post("http://localhost:8000/mcp", json={ "function_name": "create_model", "parameters": { "model_name": "example_model", "variables": {...} } }) result = response.json()

License

MIT

Credits

Based on concepts and code from the Wrench AI framework.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A Model Calling Protocol server that enables LLMs to perform rigorous Bayesian analysis and probabilistic reasoning, including inference, model comparison, and predictive modeling with uncertainty quantification.

  1. Features
    1. Installation
      1. Development Installation
      2. Requirements
    2. Quick Start
      1. Starting the Server
    3. API Usage
      1. Create Model
      2. Update Beliefs
      3. Make Predictions
      4. Compare Models
      5. Create Visualization
    4. Examples
      1. Linear Regression
      2. A/B Testing
    5. Supported Distributions
      1. MCP Integration
        1. License
          1. Credits

            Related MCP Servers

            • OptionsFlow MCP Server

              twolven
              -
              security
              A
              license
              -
              quality
              A Model Context Protocol server that enables LLMs to analyze options chains, calculate Greeks, and evaluate basic options strategies through Yahoo Finance data.
              Last updated -
              4
              Python
              MIT License
              • Apple
              • Linux

            • Math-MCP

              EthanHenrickson
              A
              security
              A
              license
              A
              quality
              A Model Context Protocol server that provides basic mathematical and statistical functions to LLMs, enabling them to perform accurate numerical calculations through a simple API.
              Last updated -
              13
              2
              TypeScript
              MIT License

            • Calculator MCP Server

              githejie
              A
              security
              A
              license
              A
              quality
              A Model Context Protocol server that enables LLMs to perform precise numerical calculations by evaluating mathematical expressions.
              Last updated -
              1
              48
              Python
              MIT License
              • Linux
              • Apple

            • Statsource MCP Server

              jamie7893
              -
              security
              F
              license
              -
              quality
              Enables LLMs to perform statistical analysis and generate ML predictions on user data from databases or CSV files through a Model Context Protocol server.
              Last updated -
              Python

            View all related MCP servers

            New MCP Servers

            ID: om8g7ez16q