ASR Graph of Thoughts (GoT) Model Context Protocol (MCP) Server

The Advanced Scientific Research (ASR) Graph of Thoughts (GoT) MCP server is a highly efficient implementation of the Model Context Protocol (MCP) that allows for sophisticated reasoning workflows using graph-based representations.

Project Overview

This project implements a Model Context Protocol (MCP) server architecture that leverages a Graph of Thoughts approach to enhance AI reasoning capabilities. It can be connected to AI models or applications like Claude desktop app or API-based integrations.

Project Structure

asr-got-mcp/
├── docker-compose.yml                          # Docker Compose configuration for multi-container setup
├── Dockerfile                                  # Docker configuration for the backend
├── requirements.txt                            # Python dependencies
├── src/                                        # Source code
│   ├── server.py                               # Main server implementation
│   ├── asr_got/                                # Core ASR-GoT implementation
│   │   ├── core.py                             # Core functionality
│   │   ├── stages/                             # Processing stages
│   │   │   ├── stage_1_initialization.py
│   │   │   ├── stage_2_decomposition.py
│   │   │   ├── stage_3_hypothesis.py
│   │   │   ├── stage_4_evidence.py
│   │   │   ├── stage_5_pruning.py
│   │   │   ├── stage_6_subgraph.py
│   │   │   ├── stage_7_composition.py
│   │   │   └── stage_8_reflection.py
│   │   ├── utils/                             # Utility functions
│   │   └── models/                            # Data models
│   └── api/                                   # API implementation
│       ├── routes.py                          # API routes
│       └── schema.py                          # API schemas
├── config/                                    # Configuration files
└── tests/                                     # Test suite

Running the Project with Docker

This project provides a multi-container Docker setup for both the Python backend (FastAPI) and the static JavaScript client. The setup uses Docker Compose for orchestration.

Project-Specific Docker Requirements

• Python Version: 3.13-slim (as specified in the backend Dockerfile)
• System Dependencies: build-essential, curl (installed in the backend image)
• Non-root Users: Both backend and client containers run as non-root users for security
• Virtual Environment: Python dependencies are installed in a virtual environment (/app/.venv)
• Static Client: Served via nginx (alpine) in a separate container

Environment Variables

The backend service sets the following environment variables (see Dockerfile):

• PYTHONUNBUFFERED=1
• MCP_SERVER_PORT=8082 (the FastAPI server port)
• LOG_LEVEL=INFO

Note: If you need to override or add environment variables, you can uncomment and use the env_file option in docker-compose.yml.

Exposed Ports

• Backend (python-app):
• Host: 8082 → Container: 8082 (FastAPI server)
• Client (js-client):
• Host: 80 → Container: 80 (nginx static server)

Build and Run Instructions

1. Build and start all services:

   docker compose up --build

This will build both the backend and client images and start the containers.

1. Access the services:

• Backend API: http://localhost:8082
• Static Client: http://localhost/

Integration with AI Models

This MCP server can be integrated with:

• Claude desktop application
• API-based integrations with AI models
• Other MCP-compatible clients

Development

To set up a development environment without Docker:

1. Clone this repository
2. Create a virtual environment: python -m venv venv
3. Activate the virtual environment:

• Windows: venv\Scripts\activate
• Linux/Mac: source venv/bin/activate

1. Install dependencies: pip install -r requirements.txt
2. Run the server: python src/server.py

License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

_If you update dependencies, remember to rebuild the images with docker compose build._