mcp-master 0.0.9
A Python package for master MCP servers.
Requires Python: >=3.12
Keywords: automation, git, llm, mcp

# mcp-master

<div align="center">

<strong>A Python package for [master MCP servers](https://medium.com/@aaron.shen321/why-you-need-a-master-for-your-mcp-servers-1c294be6bfc8).</strong>

[![PyPI][pypi-badge]][pypi-url]
[![MIT licensed][mit-badge]][mit-url]
[![Python Version][python-badge]][python-url]

</div>

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## Table of Contents

- [MCP Master](#mcp-master)
  - [Overview](#overview)
  - [Installation](#installation)
  - [Quickstart](#quickstart)
    - [Master MCP Servers](#master-mcp-servers)
    - [Master MCP Clients](#master-mcp-clients)
  - [License](#license)

[pypi-badge]: https://img.shields.io/pypi/v/mcp-master
[pypi-url]: https://pypi.org/project/mcp/
[mit-badge]: https://img.shields.io/badge/license-MIT-blue
[mit-url]: https://github.com/ashen-321/mcp-master/blob/main/LICENSE
[python-badge]: https://img.shields.io/badge/python-3.12%20%7C%203.13-green
[python-url]: https://www.python.org/downloads/

## Overview

The MCP Master package allows the creation of intermediaries between clients and MCP servers. This offloads tool-related reasoning and logic to the master server without requiring client-side modifications. This Python package provides full master MCP support, making it easy to:

- Build MCP master servers that connect clients to multiple MCP servers through the Streamable HTTP transport
- Choose tools via reasoning structures like LangGraph
- Easily scale the available selection of tools

Read about the Model Context Protocol [here](https://github.com/modelcontextprotocol/python-sdk).

## Installation

For projects using pip:
```bash
pip install mcp-master
```

## Quickstart

### Master MCP Servers

A simple master MCP server that connects to two of the included demo servers:

```python
# master_server.py
from src.mcp_master import MasterMCPServer
from src.mcp_master import global_config as gconfig
from os import getenv

gconfig.selector_model_id = ''  # Set this to your tool selector model ID
gconfig.judge_model_id = ''  # Set this to your judge model ID
gconfig.judge_model_service_url = ''  # Set this to where your judge LLM is hosted
gconfig.OPENAI_API_KEY = getenv('OPENAI_API_KEY')
gconfig.OPENAI_BASE_URL = getenv('OPENAI_BASE_URL')  # Set this to where your other LLMs will be hosted, None for default

# Create an MCP server on port 3000 with two test servers
# Ensure both test servers are running by starting them in the terminal before starting demo_master_server.py
server = MasterMCPServer(
    port=3000,
    sub_servers=[
        # (server url, server identifier) pairs - ensure all server identifiers are unique
        # If the server is located locally (as the demo servers are), ensure the server identifier matches the server's file name (without the .py)
        ("http://localhost:8091/mcp", 'test_server_1'),
        ("http://localhost:8092/mcp", 'test_server_2')
    ]
)
server.startup()
```

You can run this server in the terminal by running:
```bash
python master_server.py
```

### Master MCP Clients

[MCP clients](https://modelcontextprotocol.io/quickstart/client) utilizing the streamable-HTTP protocol can connect to master MCP servers without any modification. Details provided in the URL.

For a complete working example of an MCP client, see [the MCP GitHub repository](https://github.com/modelcontextprotocol/python-sdk/blob/main/src/mcp/client/streamable_http.py).

## License

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