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LemonCrow vs code-index-mcp
What code-index-mcp says, vs. what it scored.
Tree-sitter AST indexing for 10 core languages, with fallback file indexing for 50+ more.
What code-index-mcp says about itself
“Intelligent code indexing and analysis for Large Language Models... bridges the gap between AI models and complex codebases.”
What it actually scored — same 14 repos, same 7,213 queries as every other tool
| Tool | MRR | p95 | p100 |
|---|---|---|---|
| ★ LemonCrow +semantic (BGE) | 0.727 | 390ms | 1057ms |
| ★ LemonCrow lexical (default) | 0.676 | 134ms | 319ms |
| code-index-mcp | 0.343 | 377ms | 3830ms |
No performance or accuracy claim published. Mid-pack here: 0.343 MRR, 377ms p95.
By query kind -- same benchmark, broken out (no reps in this eval: one deterministic pass per query)
| Kind | LemonCrow +semantic | LemonCrow lexical | code-index-mcp |
|---|---|---|---|
| definition | 0.873 (n=1570) | 0.871 (n=1570) | 0.562(n=1570) |
| content | 0.873 (n=1444) | 0.864 (n=1444) | 0.684(n=1444) |
| semantic | 0.759 (n=1800) | 0.576 (n=1800) | 0.003(n=1800) |
| swebench | 0.500 (n=1908) | 0.493 (n=1908) | 0.258(n=1908) |
| sessions | 0.587 (n=491) | 0.571 (n=491) | 0.214(n=491) |
n = query/gold pairs of that kind, out of 7,213 total -- every provider scored on all 5 kinds.
By repo -- all 15 repos in the corpus, same query set
| Repo | LemonCrow +semantic | LemonCrow lexical | code-index-mcp |
|---|---|---|---|
| astropy/astropy | 0.772 | 0.715 | 0.428 |
| atelier-ws/atelier-dev | 0.467 | 0.477 | 0.238 |
| atelier/atelier | 0.594 | 0.557 | 0.265 |
| django/django | 0.689 | 0.652 | 0.245 |
| matplotlib/matplotlib | 0.801 | 0.747 | 0.357 |
| mwaskom/seaborn | 0.814 | 0.768 | 0.420 |
| pallets/flask | 0.735 | 0.671 | 0.297 |
| psf/requests | 0.840 | 0.803 | 0.464 |
| pydata/xarray | 0.815 | 0.764 | 0.429 |
| pylint-dev/pylint | 0.856 | 0.784 | 0.452 |
| pytest-dev/pytest | 0.826 | 0.739 | 0.461 |
| scikit-learn/scikit-learn | 0.740 | 0.669 | 0.286 |
| sphinx-doc/sphinx | 0.637 | 0.580 | 0.288 |
| sympy/sympy | 0.694 | 0.637 | 0.321 |
| torvalds/linux | 0.726 | 0.668 | 0.359 |
MRR per repo: n-weighted blend across all 5 query kinds, same 7,213-query run.
The true story
Same 14 repositories, same 7,213 query/gold pairs as every tool here, code-index-mcp included. Full methodology, every raw number, and the other 9 tools →