Berghain

Provides a Berghain events MCP server that extracts, stores, and serves Berghain event data via MCP endpoints.

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

You run a modular MCP server that orchestrates data extraction, storage, and access to Berghain events. It exposes a programmable API for models and agents to query and work with event data, while the Firecrawl MCP integration handles automated data extraction and JSON generation for ingestion into the data store.

How to use

Use an MCP client to connect to the Berghain MCP server. The server exposes an API that your AI models can query to retrieve event data, and it includes tools to trigger data extraction, load data into storage, and serve the results through the FastAPI-based endpoints.

How to install

Prerequisites: Python 3.10+, uv, and an AWS account for DynamoDB/App Runner if deploying in the cloud. You will also need an API key for Firecrawl.

Clone the project and enter the directory.
Create and activate a virtual environment using uv.
Install dependencies with uv.

Additional configuration and notes

Configure the Firecrawl MCP integration to enable data extraction. The following MCP definition is used to run Firecrawl via npx and pass the required API key.

{
  "firecrawl-mcp": {
     "command": "npx",
     "args": [
       "-y",
       "firecrawl-mcp"
     ],
     "env": {
       "FIRECRAWL_API_KEY": "fc-YOUR_FIRECRAWL_API_KEY"
     }
  }
}

Security and maintenance notes

Keep the FIRECRAWL_API_KEY secret. Rotate API keys per your security policy. Manage access to the DynamoDB table and App Runner deployment with least-privilege IAM roles. Regularly update dependencies and monitor the API for rate limits and abuse.