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.
🚀 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
- Push the repo to GitHub
- Add it as an Actor in Apify Console
- Enable Standby Mode
- 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