iflow-mcp-rajeevchandra-client-server

# MCP + Ollama Local Tool Calling Example This project demonstrates how a local AI agent can **understand user queries** and **automatically call Python functions** using: - Model Context Protocol (**MCP**) - **Ollama** for running a local LLM (e.g., Llama3) - **Python** MCP Client and Server --- ## 🔗 Sequence Diagram ```mermaid sequenceDiagram participant User participant MCP_Client participant Ollama_LLM participant MCP_Server User->>MCP_Client: 1) User types: "What is 5 + 8?" MCP_Client->>Ollama_LLM: 2) Send available tools + user query Ollama_LLM->>Ollama_LLM: 3) Understand query & tool descriptions Ollama_LLM->>Ollama_LLM: 4) Select tool: add(a=5, b=8) Ollama_LLM->>MCP_Client: 5) Return tool_call MCP_Client->>MCP_Server: 6) Execute add(a=5, b=8) MCP_Server-->>MCP_Client: 7) Return result: 13 MCP_Client-->>User: 8) Show final answer: 13 ``` --- ## 📚 Project Structure ``` . ├── math_server.py # MCP Server exposing add() and multiply() tools ├── ollama_client.py # MCP Client interacting with Ollama ├── README.md # Project documentation ``` --- ## 🛠️ Setup Instructions ### 1. Install Requirements ```bash pip install "mcp[cli] @ git+https://github.com/awslabs/mcp.git" openai==0.28 httpx ``` Make sure you have **Ollama installed** and running. ### 2. Pull or run an LLM model ```bash ollama run llama3 ``` (Ensure the model you run supports tool calling.) ### 3. Run the MCP Server ```bash python math_server.py ``` The server exposes two simple tools: - `add(a: int, b: int) -> int` - `multiply(a: int, b: int) -> int` ### 4. Run the MCP Client ```bash python ollama_client.py math_server.py ``` ### 5. Interact! Example queries: ``` Query: What is 5 + 8? Response: 13 Query: Multiply 7 and 9 Response: 63 ``` The MCP client sends the query and available tools to Ollama. The LLM internally decides which tool to use based on the tool descriptions and user intent. --- ## 🚀 How It…