Code Search

CI

Semantic + lexical code search as an MCP server. Agents query in natural language and get back ranked file:line ranges to read precisely.

Quickstart

Requires Node 22+ (for the built-in node:sqlite).

git clone https://github.com/RaziStuff/code-search-mcp.git
cd code-search-mcp
npm install      # builds automatically; first search downloads a ~90MB model once

Index a project and search it — the .code-index.db is written next to the code:

cd /path/to/your/project
node /path/to/code-search-mcp/dist/cli.js index .
node /path/to/code-search-mcp/dist/cli.js search "how is the request body parsed"

npm link puts a code-search command on your PATH so you can drop the long path. To let a coding agent search for you, see MCP server below.

How it works

• Chunking — syntax-aware via web-tree-sitter (function / class / method boundaries, with symbol names), heading-section chunks for markdown, and a line-window fallback otherwise.
• Embeddings — local all-MiniLM-L6-v2 via transformers.js (384-dim, no API, no code leaves the machine).
• Index — SQLite + sqlite-vec (.code-index.db). Incremental: only files whose content hash changed are re-embedded; deleted files are dropped. (A change to chunker/embedder logic needs a full rebuild — delete the .db — since incremental keys on file content, not code version.) Lockfiles, minified bundles, and source maps are skipped so they don't swamp results.
• Ranking — hybrid: vector KNN fused with BM25 (FTS5) via reciprocal rank fusion, plus an extra-weighted exact-phrase list, a small code-over-prose nudge, and a test-file down-weight, so exact symbol/token matches and implementing code don't get lost behind embedding-friendly prose or their own test files. Each result's score is its true cosine similarity (0–1); when the top result is below CODE_SEARCH_MIN_SCORE (default 0.25) the response is flagged low-confidence so callers can detect "no good match". Confidence uses the best cosine in the result set, and results are hybrid-ranked (#1 = best overall), so the per-row cosine is a confidence annotation, not the sort key.
• Freshness — optional chokidar watcher auto-reindexes on file changes.

Setup

cd code-search-mcp
npm install

Node 22+ required (built-in node:sqlite). First embed downloads the model (~90MB), cached locally.

CLI

npm run index ../some-project    # incremental re-index
npm run search "where are auth tokens validated"
npm run watch ../some-project    # index, then auto-reindex on changes

MCP server

CODE_SEARCH_ROOT=/path/to/project npm run serve
# add CODE_SEARCH_WATCH=1 to auto-reindex on file changes

Tools exposed: search_code (hybrid), reindex (incremental sync), and index_status. Register it with any MCP client — e.g. claude mcp add code-search -- node /abs/path/dist/server.js — or add a .mcp.json entry whose command/args point at dist/server.js. Set CODE_SEARCH_WATCH=1 in its env to auto-reindex on file changes.

Tests

npm test

Uses Node's built-in node:test runner via tsx (no extra deps). Store / indexer / watcher tests use a deterministic FakeEmbedder, so the suite runs in ~1s with no model download or network. Voyage is tested against a local mock HTTP server — no API key needed.

Retrieval quality is measured separately:

npm run eval

Runs a labeled query set (eval/*.jsonl) and reports hit@1 / hit@3 / MRR plus no-match accuracy — so ranking changes are measured, not eyeballed. Point it at any prebuilt index with EVAL_FILE=… EVAL_DB=/path/.code-index.db EVAL_SYNC=0.

Choosing an embedder

Default is local MiniLM (private, free). To use Voyage's code-tuned model:

export CODE_SEARCH_EMBEDDER=voyage
export VOYAGE_API_KEY=...           # required
# optional: VOYAGE_MODEL (voyage-code-3), VOYAGE_DIM (1024), VOYAGE_BASE_URL

Caveats: this sends your code to api.voyageai.com and costs per token. Switching embedders changes the vector dimension, which the store detects and wipes the index, forcing a full re-embed (i.e. every chunk is sent to Voyage on the next index/reindex). The local default sends nothing off-machine.

Version pin worth knowing

web-tree-sitter is pinned to 0.22.6 to match the prebuilt grammars in [email protected]. Newer web-tree-sitter (0.25+) changed its WASM ABI and can't load those grammars. Bump both together or neither.

Still to come

• ANN indexing when sqlite-vec ships it — search is currently an exhaustive (but fast, compiled-C) scan, fine into the tens of thousands of chunks.