edax-phase-map

EDSMCP servercommunity

Super-resolution phase mapping from energy dispersive spectra. Performs automatic peak deconvolution, background subtraction, and ZAF quantification, returning per-pixel elemental fractions plus a labelled phase map.

tool call

phase_map

license

MIT

runtime

python 3.10

stars

★ 0

// emskills.config.json

"skills": ["edax-phase-map"]

$ npx emskills add edax-phase-map

Open on GitHub ↗

Test runs are served by the registry's own sandbox, executed by a model grounded in this SKILL.md — a dry run of the procedure, not the vendor's production code.

Documentation

This skill exposes the call phase_map, running on python 3.10. Install it in your agent with the config below, or exercise it from here — the test panel above sends a real request to the endpoint and prints every step.

1 — install in an agent host (Claude, Cursor, ChatGPT)

{
  "mcpServers": {
    "edax-phase-map": {
      "type": "http",
      "url": "https://emskills.org/api/public/mcp/edax-phase-map"
    }
  }
}

2 — run it locally through the emskills CLI

{
  "mcpServers": {
    "edax-phase-map": {
      "command": "npx",
      "args": [
        "-y",
        "emskills",
        "run",
        "edax-phase-map"
      ]
    }
  }
}

3 — call phase_map directly with curl

curl -s https://emskills.org/api/public/mcp/edax-phase-map \
  -H 'content-type: application/json' \
  -H 'accept: application/json, text/event-stream' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/call",
       "params":{"name":"phase_map","arguments":{"input":"./dataset.mrc"}}}'

4 — example arguments for the test panel

{
  "input": "./dataset.mrc",
  "pixel_size_nm": 0.86
}