Sends chat requests to multiple LLM APIs (OpenAI, Anthropic, Google, etc.) through a single unified interface. Includes built-in prompts for code review, documentation, and explanation tasks.

Interact with multiple LLM chat APIs through a unified interface.

37290 views11Local (stdio)

What it does

  • Send messages to 8+ LLM providers via one interface
  • Review code for best practices and issues
  • Generate code documentation and comments
  • Explain code functionality in detail
  • Apply requested changes to code

Best for

Developers wanting to compare responses across LLM providersCode review and documentation workflowsTeams using multiple AI models without switching tools
8+ LLM providers supportedBuilt-in code analysis prompts

About Unichat

Unichat is a community-built MCP server published by amidabuddha that provides AI assistants with tools and capabilities via the Model Context Protocol. Unichat lets you interact with multiple LLM chat APIs easily through a unified interface. Simplify your workflows with U It is categorized under ai ml, developer tools.

How to install

You can install Unichat in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.

License

Unichat is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

Unichat MCP Server in Python

Also available in TypeScript

MseeP.ai Security Assessment Badge

Released under the MIT license. Trust Score Smithery Server Installations

Hosted at MCPHub

Send requests to OpenAI, MistralAI, Anthropic, xAI, Google AI, DeepSeek, Alibaba, Inception using MCP protocol via tool or predefined prompts. 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"

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-4o-mini",
  "UNICHAT_API_KEY": "YOUR_OPENAI_API_KEY"
}

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
  1. Sync dependencies and update lockfile:
uv sync
  1. Build package distributions:
uv build

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

  1. 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.

Alternatives

Related Skills

Browse all skills
ui-design-system

UI design system toolkit for Senior UI Designer including design token generation, component documentation, responsive design calculations, and developer handoff tools. Use for creating design systems, maintaining visual consistency, and facilitating design-dev collaboration.

18
ai-sdk

Answer questions about the AI SDK and help build AI-powered features. Use when developers: (1) Ask about AI SDK functions like generateText, streamText, ToolLoopAgent, embed, or tools, (2) Want to build AI agents, chatbots, RAG systems, or text generation features, (3) Have questions about AI providers (OpenAI, Anthropic, Google, etc.), streaming, tool calling, structured output, or embeddings, (4) Use React hooks like useChat or useCompletion. Triggers on: "AI SDK", "Vercel AI SDK", "generateText", "streamText", "add AI to my app", "build an agent", "tool calling", "structured output", "useChat".

6
api-documenter

Master API documentation with OpenAPI 3.1, AI-powered tools, and modern developer experience practices. Create interactive docs, generate SDKs, and build comprehensive developer portals. Use PROACTIVELY for API documentation or developer portal creation.

4
openai-knowledge

Use when working with the OpenAI API (Responses API) or OpenAI platform features (tools, streaming, Realtime API, auth, models, rate limits, MCP) and you need authoritative, up-to-date documentation (schemas, examples, limits, edge cases). Prefer the OpenAI Developer Documentation MCP server tools when available; otherwise guide the user to enable `openaiDeveloperDocs`.

4
cli-builder

Guide for building TypeScript CLIs with Bun. Use when creating command-line tools, adding subcommands to existing CLIs, or building developer tooling. Covers argument parsing, subcommand patterns, output formatting, and distribution.

3
ydc-ai-sdk-integration

Integrate Vercel AI SDK applications with You.com tools (web search, AI agent, content extraction). Use when developer mentions AI SDK, Vercel AI SDK, generateText, streamText, or You.com integration with AI SDK.

2