LLM Tool-Calling Assistant

<h1 align="center">🧠 LLM Tool-Calling Assistant with MCP Integration</h1> <p align="center"> <b>Connect your local LLM to real-world tools, knowledge bases, and APIs via MCP.</b> </p> <p align="center"> <img src="https://img.shields.io/badge/MCP%20Support-Enabled-blue?style=flat-square" /> <img src="https://img.shields.io/badge/LLM%20Backend-OpenAI%20or%20Local-brightgreen?style=flat-square" /> <img src="https://img.shields.io/badge/Tool%20Calling-Automated-ff69b4?style=flat-square" /> <img src="https://img.shields.io/badge/Python-3.8+-yellow?style=flat-square" /> </p> <p align="center"> <img src="https://user-images.githubusercontent.com/74038190/225813708-98b745f2-7d22-48cf-9150-083f1b00d6c9.gif" width="450"> </p> This project connects a local LLM (e.g. Qwen) to tools such as a calculator or a knowledge base via the [MCP](https://github.com/modelcontextprotocol) protocol. The assistant automatically detects and calls these tools to help answer user queries. --- ## 📦 Features - 🔧 Tool execution through MCP server - 🧠 Local LLM integration via HTTP or OpenAI SDK - 📚 Knowledge base support (`data.json`) - ⚡ Supports `stdio` and `sse` transports --- ## 🗂 Project Files | File | Description | |-------------------|------------------------------------------------------------| | `server.py` | Registers tools and starts MCP server | | `client-http.py` | Uses `aiohttp` to communicate with local LLM | | `clientopenai.py` | Uses OpenAI-compatible SDK for LLM + tool call logic | | `client-stdio.py` | MCP client using stdio | | `client-see.py` | MCP client using SSE | | `data.json` | Q&A knowledge base | --- ## 📥 Installation ### Requirements Python 3.8+ Install dependencies: ```bash pip install -r requirements.txt ``` ### `requirements.txt` ``` aiohttp==3.11.18 nest_asyncio==1.6.0 python-dotenv==1.1.0 openai==1.77.0 mcp==1.6.0 ``` --- ## 🚀 Getting Started ### 1. Run the MCP server ```bash python server.py ``` This launches your tool server with functions like `add`, `multiply`, and `get_knowledge_base`. ### 2. Start a client #### Option A: HTTP client (local LLM via raw API) ```bash python client-http.py ``` #### Option B: OpenAI SDK client ```bash python client-openai.py ``` #### Option C: stdio transport ```bash python client-stdio.py ``` #### Option D: SSE transport Make sure `server.py` sets: ```python transport = "sse" ``` Then run: ```bash python client-sse.py ``` --- ## 💬 Example Prompts ### Math Tool Call ``` What is 8 times 3? ``` Response: ``` Eight times three is 24. ``` ### Knowledge Base Question ``` What are the healthcare benefits available to employees in Singapore? ``` Response will include the relevant answer from `data.json`. --- ## 📁 Example: `data.json` ```json [ { "question": "What is Singapore's public holiday schedule?", "answer": "Singapore observes several public holidays..." }, { "question": "How do I apply for permanent residency in Singapore?", "answer": "Submit an online application via the ICA website..." } ] ``` --- ## 🔧 Configuration Inside `client-http.py` or `clientopenai.py`, update the following: ```python LOCAL_LLM_URL = "..." TOKEN = "your-api-token" LOCAL_LLM_MODEL = "your-model" ``` Make sure your LLM is serving OpenAI-compatible API endpoints. --- ## 🧹 Cleanup Clients handle tool calls and responses automatically. You can stop the server or client using `Ctrl+C`. --- ## 🪪 License MIT License. See [LICENSE](LICENSE) file.