Summary
The Uber Eats MCP Server provides a proof-of-concept integration between large language models and Uber Eats functionality through the Model Context Protocol. The server enables LLM applications to interact with Uber Eats using browser automation via Playwright, allowing users to leverage AI assistants to access food delivery capabilities programmatically. This integration solves the problem of connecting AI applications to real-world services by establishing a standardized protocol bridge between LLMs and external web-based platforms.
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This is a POC of how you can build an MCP servers on top of Uber Eats
https://github.com/user-attachments/assets/05efbf51-1b95-4bd2-a327-55f1fe2f958b
What is MCP?
The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external tools.
Prerequisites
- Python 3.12 or higher
- Anthropic API key or other supported LLM provider
Setup
-
Ensure you have a virtual environment activated:
uv venv source .venv/bin/activate # On Unix/Mac -
Install required packages:
uv pip install -r requirements.txt playwright install -
Update the
.envfile with your API key:ANTHROPIC_API_KEY=your_openai_api_key_here
Note
Since we're using stdio as MCP transport, we have disable all output from browser use
Debugging
You can run the MCP inspector tool with this command
uv run mcp dev server.py
Featured
Integrate web data into your AI product. One API to scrape website & brand data.
Get API Key Now →Agent, run crypto. Access onchain data & trade routes via 1inch.
Install now →On Capafy, your Skill runs online 24/7 as an agent product, and you get paid every time someone uses it.
Start earning →Connect Claude to +800M contacts, +150M companies. Find & Enrich leads in chat.
Try For Free →UpdatedFeb 7, 2026