Eventor API Client

What is Eventor API Client?

Eventor API Client is a Python client for integrating with the Eventor orienteering API. It provides access to events, entries, and results and includes an MCP (Model Context Protocol) server integration so AI assistants can query orienteering data.

How to use Eventor API Client?

Clone the repository, set up a virtual environment with uv, install dependencies from requirements.txt, and copy the example config file to eventor_config.py. Edit that file with your actual API key and personId. To test, run python -m eventor in debug mode, which produces a JSON file. For use with Claude, add an MCP server entry to claude_desktop_config.json pointing uv at the project directory and running mcp_server.py.

Key features of Eventor API Client

• Access event results, entries, and organization data
• Configurable caching reduces API calls
• Exposes three MCP tools for AI assistants
• Parses data into LLM-friendly format
• Debug mode generates standalone JSON output
• Uses configurable API key and personId for secure access

Use cases of Eventor API Client

• Have an LLM summarize weekly club results
• Fetch personal events for a given person over a specific period
• Retrieve detailed results for one event and person
• Integrate orienteering data into AI-driven reports or dashboards

FAQ from Eventor API Client

What MCP tools does Eventor API Client expose?

It exposes three tools: get_weekly_club_results() for weekly club results, get_personal_events() for personal events by personId and period, and get_detailed_result_for_event_and_person() for detailed results for one event and person.

How do I configure my API key and personId?

Copy eventor_config_example.py to eventor_config.py, then edit that file with your Eventor API private key (obtained from your organisation’s Eventor admin) and your own personId, which can be found in the output JSON file.

How can I test the client without an MCP host?

Run the client in debug mode using python -m eventor. It will run standalone and produce a JSON file with the results for manual review.

What are the runtime dependencies?

The project uses Python with uv for virtual environment and package management. Dependencies are listed in requirements.txt.

Where does the data live?

The client fetches data live from the Eventor orienteering API and optionally caches responses locally to reduce API calls.