Simple MCP Client to Explore MCP Servers

Quickly test and explore MCP servers from the command line!

A simple, text-based CLI client for Model Context Protocol (MCP) servers built with LangChain and Python.
Suitable for testing MCP servers, exploring their capabilities, and prototyping integrations.

Internally it uses LangChain Agent and a utility function convert_mcp_to_langchain_tools() from langchain_mcp_tools.

A TypeScript equivalent of this utility is available here

Prerequisites

• Python 3.11+
• [optional] uv (uvx) installed to run Python package-based MCP servers
• [optional] npm 7+ (npx) to run Node.js package-based MCP servers
• LLM API key(s) from OpenAI, Anthropic, Google AI Studio (for GenAI/Gemini), xAI, Cerebras, and/or Groq, as needed

Quick Start

• Install mcp-chat tool. This can take up to a few minutes to complete:

  pip install mcp-chat
  

• Configure LLM and MCP Servers settings via the configuration file, llm_mcp_config.json5

  code llm_mcp_config.json5
  

  The following is a simple configuration for quick testing:

  {
    "llm": {
      "provider": "openai",       "model": "gpt-5-mini"
      // "provider": "anthropic",    "model": "claude-haiku-4-5"
      // "provider": "google_genai", "model": "gemini-2.5-flash"
      // "provider": "xai",          "model": "grok-4-1-fast-non-reasoning"
      // "provider": "cerebras",     "model": "gpt-oss-120b"
      // "provider": "groq",         "model": "openai/gpt-oss-20b"
    },
  
    "mcp_servers": {
      "us-weather": {  // US weather only
        "command": "npx", 
        "args": ["-y", "@h1deya/mcp-server-weather"]
      },
    },
  
    "example_queries": [
      "Tell me how LLMs work in a few sentences",
      "Are there any weather alerts in California?",
    ],
  }
  

• Set up API keys

  echo "ANTHROPIC_API_KEY=sk-ant-...
  OPENAI_API_KEY=sk-proj-...
  GOOGLE_API_KEY=AI...
  XAI_API_KEY=xai-...
  CEREBRAS_API_KEY=csk-...
  GROQ_API_KEY=gsk_..." > .env
  
  code .env
  

• Run the tool

  mcp-chat
  

  By default, it reads the configuration file, llm_mcp_config.json5, from the current directory.
  Then, it applies the environment variables specified in the .env file, as well as the ones that are already defined.

Features

• Easy setup: Works out of the box with popular MCP servers
• Flexible configuration: JSON5 config with environment variable support
• Multiple LLM/API providers: OpenAI, Anthropic, Google (GenAI), xAI, Ceberas, Groq
• Command & URL servers: Support for both local and remote MCP servers
• Local MCP Server logging: Save stdio MCP server logs with customizable log directory
• Interactive testing: Example queries for the convenience of repeated testing

Limitations

• Tool Return Types: Currently, only text results of tool calls are supported. It uses LangChain's response_format: 'content' (the default) internally, which only supports text strings. While MCP tools can return multiple content types (text, images, etc.), this library currently filters and uses only text content.
• MCP Features: Only MCP [Tools]

... View full README on GitHub