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.

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🧠 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.

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🚀 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

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📦 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

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☁️ 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

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🧩 Requirements

• Python 3.7+
• Ollama installed locally
• Apify CLI (for deployment)

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❤️ Contributing

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

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📄 License

MIT License