This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
An MCP server that lets an LLM query OpenPowerlifting meet results: about four million entries from raw and equipped meets worldwide, with every attempt recorded.
Ask things like "How has Taylor Atwood's raw total changed since 2015?" or "Where would a 600 kg total put a tested 93 kg raw lifter?" and the model answers from the actual data instead of from memory.
Tool | What it does |
| Find lifters by name. Handles the |
| Every meet for one lifter with each attempt. Missed attempts are negative; fourth attempts (records only) are kept apart. |
| Rank a total against each lifter's best total, filtered by sex, equipment, weight class, event and drug testing. |
| Career bests side by side for 2 to 10 lifters. |
Related MCP server: mcp-data-lens
Needs uv.
git clone https://github.com/sebastianpantin/openpowerlifting-mcp
cd openpowerlifting-mcp
uv sync
uv run python scripts/fetch_data.pyfetch_data.py downloads the latest bulk CSV (about 170 MB zipped, 830 MB unzipped) into raw/ and converts it once with DuckDB into data/openpowerlifting.parquet (about 150 MB). It takes under a minute on a decent connection. Run it again with --force to pick up a newer export.
uv run mcp install server.py --name openpowerlifting --with-editable .Restart Claude Desktop and the tools show up. Any other MCP client can start the server over stdio with uv run --directory /path/to/openpowerlifting-mcp openpowerlifting-mcp.
The server looks for data/openpowerlifting.parquet in the repo. Set OPL_PARQUET to use a file somewhere else.
uv run mcp dev server.py # MCP Inspector in the browser
uv run pytest
uv run ruff check . && uv run ruff format .Tests run against a nine-row CSV in tests/fixtures/ that uses the real column layout, so they don't need the full download.
Queries go straight to the Parquet file through DuckDB. Lookups take well under 100 ms on a laptop, so there is no database to run.
This project uses data from the OpenPowerlifting project, https://www.openpowerlifting.org. You may download a copy of the data at https://data.openpowerlifting.org.
The data is in the public domain. This repository does not include it; fetch_data.py downloads it from OpenPowerlifting.
MIT for the code. See LICENSE.
This server cannot be deployed
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