Ollama-Apify-MCP

Bring powerful local AI into your Apify workflows.

This project connects Ollama’s locally-run language models with the Model Context Protocol (MCP) and Apify’s scraping & automation platform. It enables you to process scraped data, extract insights, and generate intelligent responses — all without external APIs.

🧠 Overview

The Ollama-Apify-MCP Actor bridges Apify workflows with local LLMs via MCP, allowing AI-driven analysis and reasoning while preserving privacy and reducing costs.

Related MCP server: Skeleton MCP Server

🚀 Key Features

• 🔗 Local LLM integration — Run models like Llama, Mistral, CodeLlama, and more using Ollama

• 🧩 MCP-based communication — Standards-compliant protocol for tool interaction

• ⚙️ Automatic context & preprocessing — Improves model response quality

• 🛠️ Extensible tool architecture — Easily add custom MCP tools & resources

• 🔁 Robust error handling & retries — Reliable execution in workflows

📦 Quick Start

Use as in cursor, copilot, claude code or desktop

{
  "mcpServers": {
      "ollama-apify-mcp": {
        "url": "https://lenticular-negative--ollama-apify-mcp.apify.actor/mcp?token={YOUR_TOKEN}"
      }
    }
  }

💻 Run Locally

pip install -r requirements.txt
APIFY_META_ORIGIN=STANDBY python -m src

Server runs at:

http://localhost:3000/mcp

☁️ Deploy to Apify

1. Push the repo to GitHub

2. Add it as an Actor in Apify Console

3. Enable Standby Mode

4. Deploy

MCP endpoint:

https://lenticular-negative--ollama-apify-mcp.apify.actor/mcp

Include your API token:

Authorization: Bearer <APIFY_TOKEN>

🎯 Use Cases

• 📊 Analyze & summarize scraped web data

• 🔐 Privacy-first local LLM processing

• ⚡ Low-latency on-device inference

• 🧱 Build AI tools inside Apify workflows

🧩 Requirements

• Python 3.7+

• Ollama installed locally

• Apify CLI (for deployment)

❤️ Contributing

PRs and feature ideas are welcome — feel free to extend tools, improve docs, or share sample workflows.

📄 License

MIT License