MCP + Ollama Local Tool Calling Example
Description
# 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…
About
# 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…
Details
- Author
- rajeevchandra
- GitHub stars
- 8
- Downloads
- 699
- Categories
- AI, Developer Tools, Other
Jump to
- Uses MCP to describe tools for the LLM
- Runs a local LLM (e.g., Llama3) via Ollama
- Python functions (add, multiply) become callable tools
- Fully autonomous – no manual tool selection
- Everything runs offline and locally
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
MCP + Ollama Local Tool Calling ExampleCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies with pip install "mcp[cli] @ git+https://github.com/awslabs/mcp.git" openai==0.28 httpx, ensure Ollama is installed and running, pull a tool-calling‑capable model (e.g., ollama run llama3), run the MCP server (python math_server.py), then start the client with python ollama_client.py math_server.py. Finally, type queries like “What is 5 + 8?” and the system will respond with the computed result.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"mcp + ollama local tool calling example": {
"mcp-client-server-example": {
"command": "python",
"args": [
"math_server.py"
]
}
}
}
}McpServers
{
"mcp-client-server-example": {
"command": "python",
"args": [
"math_server.py"
]
}
}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 Works
- MCP Client lists available tools.
- Sends tools + user query to Ollama LLM.
- LLM reasons about the best matching tool.
- LLM generates a tool_call.
- MCP Client invokes the function via the MCP Server.
- Final result is returned and displayed.
✅ No manual hardcoding!
✅ Everything runs locally!
✅ Fully autonomous!
---
📢 Why This Matters
This pattern enables building smart local AI agents that:
- Understand user intent
- Dynamically select the correct actions
- Operate fully offline and locally
It opens doors for:
- Autonomous developers
- Local intelligent assistants
- Secure AI workflows
---
🏷️ Hashtags for Sharing
`text
#MCP #ModelContextProtocol #Ollama #LocalLLM #FunctionCalling #Python #AI #DeveloperTools #AIEngineering #AutonomousAgents
``
---
🙌 Credits
- Model Context Protocol - Ollama --- > "Smarter AI agents start with understanding how they think!" --- > Next Steps: Add Streamlit UI or Dockerize this project 🚀Sign in to leave a review
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