Overview

mcp_pyphotomol exposes PyPhotoMol analysis workflows through the Model Context Protocol. It provides tools for importing mass photometry measurements, creating histograms, fitting multi-gaussian models, calibrating measurements, plotting results, and inspecting the MCP logbook.

Basic Workflow

The typical workflow for mass photometry analysis is:

  1. Import one or more HDF5 or CSV files.

  2. Create histograms from mass or contrast data.

  3. Fit a multi-gaussian model to the detected peaks.

  4. Review fitted parameters and summary tables.

  5. Plot histograms, fitted curves, or calibration results.

MCP Tools

The server keeps separate analyzer and calibrator instances. Analysis data is handled by the analyzer instance, while calibration data is handled by the calibrator instance. Tool calls are appended to a dated MCP logbook in the results folder. By default this is ~/user_data_mcp_pyphotomol/<YYYY-MM-DD>/. Set RESULTS_DIR before starting the server to choose a different folder for plots and log files.

Local Development

For local MCP clients that support the mcp.json convention, point the server command at the repository:

{
  "mcpServers": {
    "mcp_pyphotomol": {
      "command": "uv",
      "args": ["run", "--directory", "/absolute/path/to/mcp_pyphotomol", "mcp_pyphotomol"]
    }
  }
}

Citation

If you use mcp_pyphotomol, please cite it as:

Burastero, O. (2026). mcp_pyphotomol (Version 1.0) [Computer software]. GitHub. https://github.com/osvalB/mcp_pyphotomol

@software{burastero_2026_mcp_pyphotomol,
  author = {Burastero, Osvaldo},
  title = {mcp_pyphotomol},
  version = {1.0},
  year = {2026},
  url = {https://github.com/osvalB/mcp_pyphotomol}
}