gget-cryo

Search EMDB (3D cryo-EM density maps) and EMPIAR (raw cryo-EM imaging datasets) from Python or as an MCP server for LLM agents.

Features

• EMDB search — filter by resolution, method, deposition year, keyword
• EMPIAR search — filter by keyword, release year, movie count, detector
• Cross-referencing — look up which EMPIAR datasets produced a given EMDB map, and vice versa
• MCP server — expose all searches as tools for Claude, GPT, or any MCP-compatible LLM

Installation

pip install -r requirements.txt

Usage

As a Python library

from gget_cryo import emdb_search, empiar_search, emdb_to_empiar

# Find ribosome maps better than 3.5 A deposited since 2024
results = emdb_search(keyword="ribosome", max_resolution=3.5, year_range=(2024, 2026))
print(results["hit_count"])

# Find EMPIAR datasets with >1000 raw movies
results = empiar_search(keyword="spike", n_movies_min=1000)

# Cross-reference: what raw data produced EMD-3061?
empiar_ids = emdb_to_empiar("EMD-3061")

As an MCP server

Run directly:

python3 mcp_server.py

Or add to your Claude Code MCP config (~/.claude/settings.json):

{
  "mcpServers": {
    "gget-cryo": {
      "command": "python3",
      "args": ["/path/to/gget-cryo/mcp_server.py"]
    }
  }
}

This exposes four tools: search_emdb, search_empiar, cross_ref_emdb_to_empiar, cross_ref_empiar_to_emdb.

Demo

python3 demo.py

API details

Resolution convention

Resolution is in Angstroms. Lower = better. To find maps better than 3.5 A, use max_resolution=3.5.

EMPIAR search limitations

EMPIAR's search index doesn't include detector or movie-count fields. When you filter by n_movies_min or detector, each candidate entry is fetched individually — so the result is exact only among the candidates checked (controlled by max_candidates).

License

MIT