plantcv-mcp

Plant phenotyping over MCP — traits, plus the segmentation overlay they were measured from.

PlantCV as an MCP measurement instrument: it returns plant trait numbers and the picture they were computed from, and refuses to return numbers when the segmentation is degenerate.

Unofficial. Not affiliated with, endorsed by, or sponsored by the Donald Danforth Plant Science Center or the PlantCV maintainers. See NOTICE.

Why you are handed the overlay

Both images below come from the same file and the same threshold method — the only difference is one parameter.

✅ channel="a", object_type="dark"	❌ channel="s", object_type="dark"
Mask covers 3.1% of the frame, 9 components. area=32427	Mask covers 96.1% — it is the background. area=1007829

The failure on the right is what this server exists to prevent. Without the picture, both runs return seventeen traits with correct units and entirely believable magnitudes. The one on the right is measuring the wall behind the plants.

Red marks the pixels that were measured; a cyan line traces the mask's own boundary, drawn on the mask's edge pixels so it never touches anything unmasked (the tint alone was invisible on a photo of red beans).

segment() returns the overlay and diagnostics but no traits. measure() requires the session_id that segment() mints. You cannot get a number without first being handed the image it came from.

That is not a style preference. Measured on real images with PlantCV 4.11.3:

All three produce correctly-united, entirely believable numbers.

Install

No install is needed if the host has uv: uvx plantcv-mcp fetches the current release into its own environment and runs it. Otherwise:

pip install plantcv-mcp

Requires Python 3.11+. Installing pulls PlantCV and its scientific stack, so the first install (or first uvx run) is not fast. From a checkout: uv add /path/to/plantcv-mcp.

Configure your MCP client

claude mcp add plantcv -- uvx plantcv-mcp

Claude Desktop and other stdio hosts:

{ "mcpServers": { "plantcv": { "command": "uvx", "args": ["plantcv-mcp"] } } }

With a pip install, use "command": "plantcv-mcp" (and drop uvx from the claude mcp add line); from a checkout, "command": "uv", "args": ["run", "--directory", "/path/to/plantcv-mcp", "plantcv-mcp"]. Verify with list_methods().

Flags: --root DIR (repeatable, or PLANTCV_MCP_ROOTS) confines every read, and the one write, to your imagery: plantcv-mcp --root /data/phenotyping. --no-isolate (or PLANTCV_MCP_ISOLATE=0) runs analyses in-process instead of in the crash-containing worker.

Tools

Typical loop: suggest_segmentation → segment → look at the overlay → segment again with a different channel, method or polarity if it is wrong (or refine if it is nearly right) → measure. Pass color_correct=true to segment when a ColorChecker is in the frame: colours are corrected to the reference before segmenting and measuring, and the card itself is excluded from the mask (exclude_color_card=true does only the exclusion).

The call that produced the left-hand image above:

{
  "image_path": "multi_specimen.png",
  "channel": "a",
  "method": "otsu",
  "object_type": "dark"
}

Its response — verbatim, apart from a shortened session_id and an elided message — with the overlay arriving beside it as an image:

{
  "session_id": "9d2384c8-…",
  "channel": "a",
  "method": "otsu",
  "object_type": "dark",
  "fill_size": 200,
  "color_correct": false,
  "mask_fraction": 0.031,
  "component_count": 9,
  "major_object_count": 4,
  "largest_area": 8628,
  "overlay_scale": 1.0,
  "overlay_png_bytes": 748233,
  "warnings": [
    {
      "code": "multi_specimen",
      "message": "4 comparably-sized objects detected (areas: [8628, 7981, 7106, 6748]). …"
    }
  ]
}

What it refuses, and why

Every guard was calibrated against a real failure and names the next action. Blocking guards withhold numbers; advisories travel with them.