Analysis of mass photometry data
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# mcp_pyphotomol <!-- [](https://biocontext.ai/registry) --> [![Tests][badge-tests]][tests] [![Documentation][badge-docs]][documentation] [badge-tests]: https://img.shields.io/github/actions/workflow/status/osvalB/mcp_pyphotomol/test.yaml?branch=main [badge-docs]: https://img.shields.io/github/actions/workflow/status/osvalB/mcp_pyphotomol/docs.yaml?branch=main&label=docs This repository contains a local MCP server for the analysis of mass photometry data. It is based on the Python package [pyphotomol](https://github.com/osvalB/pyphotomol). ## Video demonstration Watch a demonstration of the MCP server in action: [Video Demo](https://drive.google.com/file/d/1NQNWErh1wtd1XFVcd_Lsf-STHb5QfaaC/view?usp=share_link). ## Getting started Please refer to the [documentation] ## Installation You need to have Python 3.12 or newer installed on your system. If you don't have Python installed, we recommend installing [uv][]. ### Run from the command line ```bash uvx mcp_pyphotomol ``` By default, plots and log files are saved in `~/user_data_mcp_pyphotomol/<YYYY-MM-DD>/`. To choose a different results folder, set `RESULTS_DIR` before starting the server. This folder is where results are stored; each server run writes into a date-stamped subfolder. ```bash RESULTS_DIR=~/Documents/user_data_mcp_pyphotomol uvx mcp_pyphotomol ``` ### Configure an MCP client Add the server to any MCP-compatible client that supports the `mcpServers` configuration format: ```json { "mcpServers": { "mcp_pyphotomol": { "command": "uvx", "args": ["mcp_pyphotomol"], "env": { "RESULTS_DIR": "/absolute/path/to/results-folder" } } } } ``` After updating the configuration, restart the MCP client so it can launch the server. #### Claude Desktop In Claude Desktop, open **S…
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