Unichat MCP Server in Python

Also available in TypeScript

Send requests to OpenAI, Anthropic, and OpenAI-compatible providers using MCP protocol via tool or predefined prompts. For OpenAI-compatible providers such as MistralAI, xAI, Google AI, DeepSeek, Alibaba, or Inception, set UNICHAT_BASE_URL to the provider's compatible API endpoint. Vendor API key required

Tools

The server implements one tool:

• unichat: Send a request to unichat

  • Takes "messages" as required string arguments

  • Returns a response

Prompts

• code_review

  • Review code for best practices, potential issues, and improvements

  • Arguments:

    • code (string, required): The code to review"

• document_code

  • Generate documentation for code including docstrings and comments

  • Arguments:

    • code (string, required): The code to comment"

• explain_code

  • Explain how a piece of code works in detail

  • Arguments:

    • code (string, required): The code to explain"

• code_rework

  • Apply requested changes to the provided code

  • Arguments:

    • changes (string, optional): The changes to apply"

    • code (string, required): The code to rework"

Related MCP server: MCP AI Gateway

Quickstart

Install

Claude Desktop

On MacOS: ~/Library/Application\ Support/Claude/claude_desktop_config.json On Windows: %APPDATA%/Claude/claude_desktop_config.json

Supported Models:

A list of currently supported models to be used as "SELECTED_UNICHAT_MODEL" may be found here. Please make sure to add the relevant vendor API key as "YOUR_UNICHAT_API_KEY"

Example:

"env": {
  "UNICHAT_MODEL": "gpt-5.4-mini",
  "UNICHAT_API_KEY": "YOUR_OPENAI_API_KEY"
}

For OpenAI-compatible providers with custom endpoints:

"env": {
  "UNICHAT_MODEL": "PROVIDER_MODEL",
  "UNICHAT_API_KEY": "YOUR_PROVIDER_API_KEY",
  "UNICHAT_BASE_URL": "https://provider.example.com/v1"
}

When UNICHAT_BASE_URL is set, the server accepts the configured UNICHAT_MODEL without checking it against Unichat's built-in model list.

Development/Unpublished Servers Configuration

"mcpServers": {
  "unichat-mcp-server": {
    "command": "uv",
    "args": [
      "--directory",
      "{{your source code local directory}}/unichat-mcp-server",
      "run",
      "unichat-mcp-server"
    ],
    "env": {
      "UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
      "UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
    }
  }
}

Published Servers Configuration

"mcpServers": {
  "unichat-mcp-server": {
    "command": "uvx",
    "args": [
      "unichat-mcp-server"
    ],
    "env": {
      "UNICHAT_MODEL": "SELECTED_UNICHAT_MODEL",
      "UNICHAT_API_KEY": "YOUR_UNICHAT_API_KEY"
    }
  }
}

Installing via Smithery

To install Unichat for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install unichat-mcp-server --client claude

Development

Building and Publishing

To prepare the package for distribution:

1. Remove older builds:

rm -rf dist

2. Sync dependencies and update lockfile:

uv sync

3. Build package distributions:

uv build

This will create source and wheel distributions in the dist/ directory.

4. Publish to PyPI:

uv publish --token {{YOUR_PYPI_API_TOKEN}}

Debugging

Since MCP servers run over stdio, debugging can be challenging. For the best debugging experience, we strongly recommend using the MCP Inspector.

You can launch the MCP Inspector via npm with this command:

npx @modelcontextprotocol/inspector uv --directory {{your source code local directory}}/unichat-mcp-server run unichat-mcp-server

Upon launching, the Inspector will display a URL that you can access in your browser to begin debugging.

Hosted deployment

A hosted deployment is available on Fronteir AI.