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 protocol. The assistant automatically detects and calls these tools to help answer user queries.

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📦 Features

• 🔧 Tool execution through MCP server
• 🧠 Local LLM integration via HTTP or OpenAI SDK
• 📚 Knowledge base support (data.json)
• ⚡ Supports stdio and sse transports

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🗂 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

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📥 Installation

Requirements

Python 3.8+

Install dependencies:

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

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🚀 Getting Started

1. Run the MCP server

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)

python client-http.py

Option B: OpenAI SDK client

python client-openai.py

Option C: stdio transport

python client-stdio.py

Option D: SSE transport

Make sure server.py sets:

transport = "sse"

Then run:

python client-sse.py

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💬 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.

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📁 Example: data.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..."
  }
]

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🔧 Configuration

Inside client-http.py or clientopenai.py, update the following:

LOCAL_LLM_URL = "..."
TOKEN = "your-api-token"
LOCAL_LLM_MODEL = "your-model"

Make sure your LLM is serving OpenAI-compatible API endpoints.

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🧹 Cleanup

Clients handle tool calls and responses automatically. You can stop the server or client using Ctrl+C.

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🪪 License

MIT License. See LICENSE file.