po3_MCP

po3_MCP

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The Poe o3 MCP Server is a lightweight server implementation that facilitates interaction with OpenAI models via the Poe API. It features model selection, efficient request handling, and integration options for applications using the Model Context Protocol.

Poe o3 MCP Server

A lightweight Model Context Protocol (MCP) server implementation that provides access to OpenAI's o3 model and other models via Poe's API. This server allows you to integrate Poe's AI capabilities into any MCP-compatible application.

Features

  • Simple MCP server implementation using FastMCP
  • Direct integration with Poe's API to access the o3 model and other models
  • Model selection via command-line style flags in prompts
  • Asynchronous request handling for efficient processing
  • Comprehensive error handling and logging
  • Easy setup and configuration

Prerequisites

Installation

  1. Clone this repository:

    git clone https://github.com/Anansitrading/po3_MCP.git
cd po3_MCP

  2. Create a virtual environment (recommended):

    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

  3. Install the required dependencies:

    pip install -r requirements.txt

  4. Set up your environment variables:

    cp sample.env .env

  5. Edit the .env file and add your Poe API key:

    POE_API_KEY=your_poe_api_key_here

Usage

Running the MCP Server

Run the server with:

python poe_o3_mcp_server.py

The server will start and listen for MCP protocol messages on standard input/output.

Model Selection via Flags

You can select different models available on Poe by adding a flag to your prompt:

--Claude-3.5-Sonnet Tell me about quantum computing

This will route your query to the Claude-3.5-Sonnet model instead of the default o3 model.

The flag can be placed anywhere in the message:

  • At the beginning: --GPT-4 What is the capital of France?
  • In the middle: Tell me --Claude-3-Opus about the history of Rome
  • At the end: What are the three laws of robotics? --Claude-3.5-Sonnet

The flag will be automatically removed from the message before it's sent to the model.

If no flag is specified, the server defaults to using the "o3" model.

Integrating with MCP Clients

This server provides two tools:

  1. o3_query - Send a query to the o3 model (or another model via flags) and get a response
  2. ping - A simple test tool that returns "pong"

Example of using the server with an MCP client:

from mcp.client import MCPClient

# Connect to the MCP server
client = MCPClient(server_command=["python", "path/to/poe_o3_mcp_server.py"])

# Call the o3_query tool with the default o3 model
response = client.call_tool("o3_query", {"message": "Tell me about quantum computing"})
print(response)

# Call the o3_query tool with a different model using a flag
response = client.call_tool("o3_query", {"message": "--Claude-3.5-Sonnet Tell me about quantum computing"})
print(response)

# Test the connection with ping
ping_response = client.call_tool("ping", {})
print(ping_response)  # Should print "pong"

You can also run the included example script:

python example.py

Configuration

The server uses the following environment variables:

  • POE_API_KEY: Your Poe API key (required)
  • LOG_LEVEL: Logging level (optional, defaults to DEBUG)

Troubleshooting

If you encounter issues:

  1. Check that your Poe API key is valid and correctly set in the .env file
  2. Ensure you have the correct dependencies installed
  3. Check the server logs for detailed error messages
  4. Verify that you have an active internet connection
  5. If using a model flag, make sure the model name is correct and available on Poe

License

MIT

Acknowledgements