- adapters/mcp_stdio.py: drives competitors over MCP stdio like a real host; payload-level error detection, fastmcp output-wrapper normalization, cold-start warmup, artifact-based verification (harness re-opens every exported DXF with ezdxf) - competitors_env.py: reproducible pinned-SHA checkout + isolated venv per competitor - adapters for puran-water/autocad-mcp (95476a3) and beiming183-cloud/AutoCAD-MCP (11f7c47) mapped to their own documented tool contracts; unmapped tasks report 'unsupported' with reasons - run_competitors.py: registry-driven --server from competitors.yaml - published reports (pro 100.0 / beiming 50.0 / puran 45.0) + render_live_chart.py -> docs/assets/autocad-mcp-livebench.svg - benchmark.yml: weekly + manual dispatch matrix Co-authored-by: Cursor <cursoragent@cursor.com>
157 lines
7.5 KiB
Markdown
157 lines
7.5 KiB
Markdown
# Benchmarks
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## Two evidence layers
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This repository publishes two deliberately separate forms of evidence:
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1. **Source-reviewed capability benchmark.** Six named AutoCAD MCP projects are
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scored with a fixed 100-point rubric covering CAD breadth, correctness and
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delivery, backend reach, engineering production, tests, and security. The
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dated data lives in [`source_review.json`](source_review.json), and the README
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graphic is generated—not hand-edited—from that file.
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2. **Fixed-task runtime benchmark.** Adapters execute the same ten tasks and
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return `pass`, `partial`, `unsupported`, `fail`, `timeout`, or `not_run`.
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AutoCAD MCP Pro currently has the reference adapter; other projects do not
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receive runtime scores until an adapter actually runs their public interface.
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Regenerate the source-review chart:
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```bash
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pip install -e ".[pdf]"
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python -m benchmarks.render_chart
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```
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The source-review chart is not presented as a shared live AutoCAD run. Review
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dates, evidence grades, project URLs, and the boundary are stored with the data.
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## Fixed-task runtime runner v2
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The v2 runner separates runtime evidence from the source-review rubric. Every
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adapter receives the same ten tasks and produces the same closed result enum:
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`pass`, `partial`, `unsupported`, `fail`, `timeout`, or `not_run`. A timeout is
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reported per task. Execution is in-process, so adapters that delegate blocking
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work must provide cancellation-safe task implementations or be wrapped by an
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external process supervisor. Reports include commit SHA, Python/platform
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details, backend capability claims, durations, coverage, and hashes for returned
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artifacts. Unsupported tasks remain in the fixed-matrix denominator with score
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zero, preventing partial implementations from receiving an inflated score.
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```bash
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python -m benchmarks.run_competitors --list
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python -m benchmarks.run_competitors --server autocad-mcp-pro --backend ezdxf --json
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python -m benchmarks.run_competitors --task table_mleader --task auditable_delivery --json
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```
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The release-machine ezdxf self-check is **10/10 (100.0)**. Repository stars and
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raw tool counts do not contribute to the score. Adapter registration lives in
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`competitors.yaml`.
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## Live competitor lane (v1.4)
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Two competitor adapters now execute the exact same task matrix, black-box over
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MCP stdio, against commits pinned in `competitors.yaml`:
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| Server | Pinned | Score | Pass | Coverage |
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|---|---|---:|---:|---:|
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| autocad-mcp-pro (reference) | working tree | 100.0 | 10/10 | 100% |
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| beiming183-cloud/AutoCAD-MCP | `11f7c47e` | 50.0 | 5/10 | 50% |
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| puran-water/autocad-mcp | `95476a33` | 45.0 | 4/10 | 50% |
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Method and boundaries:
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- `benchmarks/competitors_env.py` clones the pinned SHA into
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`benchmarks/.competitors/<id>/` (gitignored), builds an isolated venv, and
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installs the competitor from its own `pyproject.toml`.
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- `benchmarks/adapters/mcp_stdio.py` drives the competitor exactly like an MCP
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host (fastmcp `Client` over stdio). No competitor code is imported.
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- Task playbooks call each server's **own documented tool contract**, read from
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the pinned source (consolidated `operation` + payload tools; beiming's
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`doc_id`/`expected_revision` discipline is honored via
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`transaction(operation="context")`).
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- **Verification never trusts the competitor's response**: every geometry claim
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must survive `save`/`save_as_dxf` and re-opening the DXF with ezdxf inside
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the harness.
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- Tasks with no documented equivalent are reported `unsupported` with the
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reason string; they stay in the fixed-matrix denominator at score zero.
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- This lane is headless (ezdxf backends only). File-IPC / live-AutoCAD lanes
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(including COM-only servers such as best-cad-mcp or daobataotie/CAD-MCP)
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require a local AutoCAD session and are out of CI scope by design.
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Published machine-readable reports (artifact paths sanitized to filenames)
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live under [`results/published/`](results/published/); the README chart is
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regenerated from them:
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```bash
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python -m benchmarks.run_competitors --server puran-water-autocad-mcp --backend ezdxf
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python -m benchmarks.run_competitors --server beiming183-autocad-mcp --backend ezdxf
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python -m benchmarks.render_live_chart
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```
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## Correctness A/B suite
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`correctness_suite.py` is a set of deterministic, headless (ezdxf-backend) checks
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that exercise real drawing operations. `compare_versions.py` runs the **same**
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suite against an older git ref and the current checkout, each check in its own
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subprocess so a hard crash (e.g. a matplotlib-in-thread `SIGSEGV`) is recorded as
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a miss rather than taking down the run.
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Reproduce:
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```bash
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python benchmarks/compare_versions.py # current tree vs origin/main
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python benchmarks/compare_versions.py v1.0.0 # vs a tag/ref
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python benchmarks/compare_versions.py --json results.json
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```
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### Result — v1.1.0 vs v1.0.0 (`origin/main`, commit 15fa2bc)
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21 checks, ezdxf backend, CPython 3.14, one subprocess per check.
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| Version | Checks passing | Pass rate |
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|---------|----------------|-----------|
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| **v1.0.0** (public release) | 8 / 21 | **38.1 %** |
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| **v1.1.0** (this release) | 21 / 21 | **100 %** |
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**13 defects fixed, 0 regressions** — a 2.6× higher correctness pass-rate (+61.9 pts).
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Fixed (failed on v1.0.0, pass on v1.1.0):
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| Check | Category | What it proves |
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|-------|----------|----------------|
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| `dim_aligned_no_error` | Dimensions | aligned dim no longer raises `TypeError` |
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| `dim_angular_no_error` | Dimensions | angular dim no longer raises `TypeError` |
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| `array_polar_360_distinct` | Modify | full-circle array no longer duplicates the original |
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| `point_intersection_line_line` | Geometry | deterministic line/line intersection exists & is correct |
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| `point_tangent_external` | Geometry | external-point tangent is perpendicular & on-circle |
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| `arc_has_length` | Query | ARC carries a `length` property |
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| `arc_select_by_length` | Query | `entity_select_smart` length_range selects arcs |
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| `ezdxf_bounding_box` | Query | `bounding_box` populated (COM/ezdxf parity) |
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| `mtext_rotation_roundtrip` | Entities | MTEXT honors caller rotation |
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| `screenshot_png` | Render | headless render returns a valid PNG (no GUI-thread crash) |
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| `dim_overlap_critique_fires` | Quality gate | `dim_overlap` critique is live (was a no-op) |
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| `iso13567_dim_layer` | Quality gate | `dimension_auto` lands dims on the active set's layer |
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| `gear_no_self_overlap` | Engineering | gear outline never dips inside the root circle (z ≥ 42) |
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Passing on **both** versions (6 core ops + 2 others) keep the suite honest — it is
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not a cherry-picked list of failures; the baseline genuinely does basic CAD work.
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### Caveats / honesty notes
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- The baseline is `origin/main` (the public v1.0.0). All four audited fix sprints
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land between it and v1.1.0 — see `docs/analysis/`.
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- `screenshot_png` on the baseline is host-dependent: it is recorded as a miss
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here because the default matplotlib backend is a GUI backend and the render runs
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off the main thread. On a host where matplotlib defaults to `Agg`, the baseline
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may not crash. v1.1.0 forces `Agg` and is deterministic.
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- `center_linetype_applied` passes on both because it only checks the linetype
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**attribute** string (set on both); the v1.0.0 "renders as Continuous because the
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linetype was never loaded" defect is not observable through `EntityInfo`.
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- COM-backend behaviour is not covered here (no live AutoCAD); see the mocked-COM
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suites in `tests/`.
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## Performance (throughput) — coming
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Scale / throughput numbers (1k–100k entities, render timings, model-by-model
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tool-call success rate) are tracked in the main README and will be published here
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with a hardware footprint.
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