goldyard2025
3fe919e37c
Move SKILL.md + references/ scripts/ evals/ from skills/halcon/ up to the plugin root. Per Claude Code plugins-reference, a plugin with SKILL.md at its root and no skills/ subdir is auto-loaded as a single-skill plugin (v2.1.142+), so the invocation name = frontmatter name = halcon → clean /halcon. Bump plugin.json 2.0.0 → 2.0.1 so existing installs receive the update. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
65 lines
2.6 KiB
Markdown
65 lines
2.6 KiB
Markdown
# halcon-mcp — the HALCON MCP server
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A local MCP server that exposes common HALCON algorithms + script runners as
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tools, for driving HALCON experiments without hand-writing Python each time. It
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lives at the **project root** (outside this skill): `halcon-mcp/`. Source of truth
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is `halcon-mcp/README.md`; this file is the pointer from the skill.
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## What it is
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- Python + official `mcp` SDK (FastMCP), **stdio** transport.
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- Runs **in-process** in the shared HALCON venv (`import halcon`), so algorithm
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tools call operators directly and return structured JSON + the **equivalent
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HDevelop code** + an **overlay PNG**.
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- Errors return `{ok:false, error:...}` (no crash) so the model can self-correct.
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## Tools (11)
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Run/experiment: `halcon_check_env`, `halcon_run_script` (inline HDevelop snippet),
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`halcon_run_program` (.hdev), `halcon_run_procedure` (.hdvp), `halcon_run_hrun`
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(headless, GBK-decoded), `halcon_open_in_hdevelop` (GUI), `halcon_teach_roi`
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(interactive pan/zoom/box-select ROI → `.hobj`, or headless via `box`; runs
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`halcon-mcp/roi_picker.py` in a subprocess and returns the saved bbox).
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Algorithms: `halcon_enhance_image`, `halcon_find_circles` (edge-fit + metrology,
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same chain as `examples/find_circles.hdvp`), `halcon_measure_metrology`
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(circle/line/rectangle2), `halcon_match_shape` (2D shape-based matching).
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`halcon_teach_roi`'s output feeds `halcon_find_circles` / `halcon_match_shape` /
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`halcon_measure_metrology` as their `roi_file` / `template_roi`.
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## Prerequisite (one-time)
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Add `mcp` to the shared venv (mvtec-halcon is already there):
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```bash
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/mnt/c/Users/NAURA/.local/bin/uv.exe pip install mcp
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```
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## Register in Claude Code (this WSL)
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A ready config is at the project root as `.mcp.json` (copied from
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`halcon-mcp/.mcp.json.example`). Or via CLI:
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```bash
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claude mcp add halcon -- /mnt/c/Users/NAURA/.local/bin/uv.exe run \
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--directory C:/workspace/agent-studio/halcon-001 python halcon-mcp/server.py
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```
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Then `/mcp` lists `halcon`. The server is a Windows process launched from WSL;
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it speaks JSON-RPC over stdio across the interop boundary (verified working).
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## Test without a client
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```bash
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/mnt/c/Users/NAURA/.local/bin/uv.exe run python halcon-mcp/examples/smoke_test.py
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```
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## Relationship to this skill
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The skill is the **knowledge** (how to run HALCON here, the manuals, recipes);
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the MCP server is the **executable surface** built from that knowledge. When a
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task needs repeatable, parameterized calls (enhance / find circles / metrology /
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matching / run a script), prefer the `halcon_*` MCP tools; for one-off custom
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pipelines, use the skill's `scripts/` runners or write an `.hdvp`.
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