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MCP + Ollama Local Tool Calling Example

@rajeevchandra

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

rajeevchandra

MCP + Ollama Local Tool Calling Example について

概要はまだありません

#mcp#ollama#local-llm#function-calling#python#ai#developer-tools#autonomous-agents

MCP + Ollama Local Tool Calling Example の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-client-server-example": {
      "command": "python",
      "args": [
        "math_server.py"
      ]
    }
  }
}

MCP + Ollama Local Tool Calling Example のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

What is MCP + Ollama Local Tool Calling Example?

It is a demonstration project that shows how a local AI agent can understand user queries and automatically call Python functions using the Model Context Protocol (MCP), a local LLM via Ollama, and a Python MCP client and server.

How to use MCP + Ollama Local Tool Calling Example?

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.

Key features of MCP + Ollama Local Tool Calling Example

  • 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

Use cases of MCP + Ollama Local Tool Calling Example

  • Building a local AI developer assistant that can perform calculations
  • Creating secure, offline‑only AI workflows for sensitive data
  • Prototyping autonomous agents that reason about and invoke functions

よくある質問

1

What does this project demonstrate?

It demonstrates how an LLM can automatically decide which Python function to call based on a user’s intent, using MCP to describe available tools and Ollama to run the model locally.

2

What are the runtime requirements?

You need Python, the `mcp[cli]`, `openai==0.28`, and `httpx` packages, plus Ollama installed and running with a model that supports tool calling (e.g., Llama3).

3

Does the system require an internet connection?

No – everything runs locally. The LLM is served by Ollama on your machine, and the MCP components communicate over local transports.

4

Which tools are exposed by the MCP server?

The example server (`math_server.py`) exposes two tools: `add(a: int, b: int) -> int` and `multiply(a: int, b: int) -> int`.

5

Can I use a different LLM model?

Yes, as long as the model supports tool‑calling. The README suggests Llama3 via the `ollama run llama3` command; you can replace it with any compatible Ollama model.

コメント

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