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Development/Unpublished Servers Configuration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",
      "--locked",
      "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

Clean installation from source

Prerequisites: Git, Python 3.11 or newer (as declared in pyproject.toml), and uv available on your PATH. Node.js/npm is only needed for the optional MCP Inspector below. The shell examples use Bash/Zsh syntax.

Clone the repository and restore its locked dependencies:

git clone https://github.com/amidabuddha/unichat-mcp-server.git
cd unichat-mcp-server
uv sync --locked

uv sync --locked creates the project-local .venv/, installs the project in editable mode and restores dependencies from uv.lock. It fails if the lockfile needs updating instead of silently changing it. No virtual-environment activation or global Python dependency installation is required when using uv run.

Set the same environment variables used in the Claude Desktop examples, replacing the placeholders with your provider's values:

export UNICHAT_MODEL="SELECTED_UNICHAT_MODEL"
export UNICHAT_API_KEY="YOUR_UNICHAT_API_KEY"
# Optional, for an OpenAI-compatible provider with a custom endpoint:
# export UNICHAT_BASE_URL="https://provider.example.com/v1"

The server reads environment variables; it does not load .env files itself. For Claude Desktop, keep these values in the server's env configuration shown above.

Run the local server:

uv run --locked unichat-mcp-server

This is a stdio MCP server: connect through Claude Desktop or the Inspector below to interact with it. It does not start a web page or an interactive chat prompt. The editable installation is sufficient to run it; to also create source and wheel distributions using the configured Hatchling backend:

uv build

Packages are written to dist/. uv build supplies the build backend in an isolated environment; a globally installed build frontend is unnecessary. Runtime dependencies are locked by uv.lock, but the hatchling build requirement is not version-pinned in pyproject.toml.

Normal development

After editing files under src/, restart the server or reconnect it in your MCP client:

uv run --locked unichat-mcp-server

The editable installation uses the current source. Ordinary source changes do not require deleting .venv/, reinstalling dependencies or rebuilding distribution packages. Run uv build again only when you need updated package artifacts. After pulling changes to the dependency manifest and lockfile, run uv sync --locked to update the environment to match them.

This repository has no configured automated test suite or watch command. Use the Inspector below to manually exercise tools and prompts, restarting the server after changes.

Clean rebuild of an existing checkout

Stop the running server and run these commands from the repository root (the directory containing pyproject.toml and uv.lock). These paths assume uv's default project-local environment: .venv/ contains installed dependencies and the editable project, and dist/ contains generated source/wheel archives.

rm -rf .venv dist
uv sync --locked
uv build

This recreates the environment and distribution packages. Keep uv.lock, source, configuration, .env files and user data. Other ignored names such as build/ and wheels/ are not established outputs of this project's build workflow and are not cleanup targets. Global uv caches and Python installations can be reused. To run again, retain or reapply the environment variables above and use uv run --locked unichat-mcp-server.

Intentional dependency updates

Dependency updates are separate from restoration. When deliberately changing requirements in pyproject.toml, run uv lock and review the resulting uv.lock changes, then run uv sync --locked. To deliberately upgrade an existing dependency within its declared constraints, for example:

uv lock --upgrade-package unichat
uv sync --locked

Review the lockfile changes and check server behavior before accepting the update. Routine installation and rebuilding should use the existing lockfile.

Publishing

Publishing is a separate maintainer action and is not part of local installation or rebuilding. Prepare fresh distributions with the clean-rebuild workflow above and verify that dist/ contains only the intended release before uploading to PyPI:

uv publish --token "YOUR_PYPI_API_TOKEN"

The repository also has a publishing workflow in .github/workflows/publish.yml, triggered by changes to pyproject.toml on main or manual dispatch.

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 "/path/to/unichat-mcp-server" run --locked 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.