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