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Advanced MCP Server Setup with

@sidhyaashu

Repository

Basic information

Category

AI & Agents

Transports

stdio

Author

sidhyaashu

About Advanced MCP Server Setup with

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 config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "ollama-mcp-integration": {
      "command": "uv",
      "args": [
        "init",
        "mcp-server"
      ]
    }
  }
}

Advanced MCP Server Setup with tools

Tool list not available yet

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Overview

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

Frequently asked questions

1

What are the prerequisites?

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

2

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`.

3

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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