Advanced MCP Server Setup with

@sidhyaashu

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

sidhyaashu

Advanced MCP Server Setup with について

Advanced MCP Server Setup with uv, llama-index, ollama, and Cursor IDE

#rag#llama-index#ollama #mcp-server#linkup-sdk

Advanced MCP Server Setup with 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": {
    "ollama-mcp-integration": {
      "command": "uv",
      "args": [
        "init",
        "mcp-server"
      ]
    }
  }
}

Advanced MCP Server Setup with 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 🧠 Advanced MCP Server Setup with uv, llama-index, ollama, and Cursor IDE?

This guide provides step‑by‑step instructions to create a local MCP server using uv (Astral), llama-index, ollama, and Cursor IDE. It is designed for developers who want to run LLM‑powered tools and agent workflows entirely on their machine, integrating with the Model Context Protocol.

How to use 🧠 Advanced MCP Server Setup with uv, llama-index, ollama, and Cursor IDE?

Set up the project with uv init, create a virtual environment, install dependencies (mcp[cli], httpx, linkup-sdk, llama-index, etc.), and write a minimal server.py that calls mcp.cli.app. Run Ollama locally with a model (e.g., ollama run llama3.2). Finally, add a global MCP server in Cursor IDE’s settings, pointing uv at your project directory and server.py. Open a Python file in Cursor and trigger MCP tools via ⌘K or Ctrl+K.

Key features of 🧠 Advanced MCP Server Setup with uv, llama-index, ollama, and Cursor IDE?

Uses uv for fast, reproducible Python dependency management
Integrates LlamaIndex with HuggingFace embeddings and Ollama LLMs
Runs a local LLM backend via Ollama at localhost:11434
Configurable MCP server inside Cursor IDE
Environment variables stored in a .env file
Optional ipykernel for notebook usage

Use cases of 🧠 Advanced MCP Server Setup with uv, llama-index, ollama, and Cursor IDE?

Building local AI agent workflows with MCP tools
Running RAG pipelines using fully local LLMs
Integrating MCP‑based code assistance into Cursor IDE
Prototyping agent orchestrators with the Linkup SDK

よくある質問

What are the prerequisites?

Python 3.10+, `uv` (Astral) installed globally, Ollama installed and running, and Cursor IDE installed.

How do I install the required dependencies?

Inside the virtual environment, run: `uv add mcp[cli] httpx linkup-sdk llama-index llama-index-embeddings-huggingface llama-index-llms-ollama ipykernel`.

How do I run the server after configuration?

Open any `.py` file in Cursor and use the MCP tools (accessible via `⌘K` or `Ctrl+K`). The server is named `"weather"` in the configuration example.

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