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About
This chatbot example demonstrates how to integrate the Model Context Protocol (MCP) into a simple CLI chatbot. The implementation showcases MCP's flexibility by supporting multiple tools through MCP servers and is compatible with any LLM provider that follows OpenAI API standards.
If you find this project helpful, don’t forget to ⭐ star the repository or buy me a ☕ coffee.
- LLM Provider Flexibility: Works with any LLM that follows OpenAI API standards (tested with Llama 3.2 90b on Groq and GPT-4o mini on GitHub Marketplace).
- Dynamic Tool Integration: Tools are declared in the system prompt, ensuring maximum compatibility across different LLMs.
- Server Configuration: Supports multiple MCP servers through a simple JSON configuration file like the Claude Desktop App.
- Python 3.10
python-dotenvrequestsmcpuvicorn
Works with Claude (desktop and mobile), Cursor, Windsurf, VS Code, and any MCP-compatible AI app.
Category: Developer Tools
Don't lose this
Three weeks from now, you'll want MCP-Chatbot again. Will you remember where to find it?
Save it to your library and the next time you need MCP-Chatbot, it’s one tap away — from any AI app you use. Group it into a bench with the rest of the team for that kind of task and you can pull the whole stack at once.
⚡ Pro tip for geeks: add a-gnt 🤵🏻♂️ as a custom connector in Claude or a custom GPT in ChatGPT — one click and your library is right there in the chat. Or, if you’re in an editor, install the a-gnt MCP server and say “use my [bench name]” in Claude Code, Cursor, VS Code, or Windsurf.
a-gnt's Take
Our honest review
This plugs directly into your AI and gives it new abilities it didn't have before. MCP-Chatbot. Once connected, just ask your AI to use it. It's completely free and works across most major AI apps. This one just landed in the catalog — worth trying while it's fresh.
Tips for getting started
Tap "Get" above, pick your AI app, and follow the steps. Most installs take under 30 seconds.
What's New
Imported from awesome:wong2/awesome-mcp-servers
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