lens

lens

Provides token-efficient code retrieval for coding agents by indexing repositories and enabling ranked snippet search, symbol outlines, and surgical line reads.

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🔎 lens

Token-efficient code & doc retrieval for agents.

The biggest token sink for a coding agent is reading whole files to find a few relevant lines. lens fixes that: index a repo once, then search for ranked snippets, get a symbol outline of a file, or do a surgical line read — pulling just enough context instead of the whole file.

Part of tools-for-agents. Zero dependencies — Node standard library + built-in node:sqlite with FTS5 (BM25 ranking).


Why

Without lens With lens
Read a 600-line file to find one function → ~6k tokens lens_search "parse auth header" → ~300 tokens of the exact snippets
Read a file just to learn its structure lens_outline → a symbol map, ~100 tokens
Re-read whole files after each edit incremental reindex touches only changed files

CLI

node src/cli.js index .                       # build the index (incremental on re-run)
node src/cli.js search "websocket reconnect" -k 6 --tokens 1500 --glob 'src/*'
node src/cli.js outline src/server.js         # symbol map, no full read
node src/cli.js read src/server.js 40 80      # surgical line range
node src/cli.js stats                         # index stats

Index location is ./.lens/index.db (override with LENS_DB).

MCP server (for agents)

{
  "mcpServers": {
    "lens": { "command": "node", "args": ["/abs/path/to/lens/mcp/mcp-server.js"],
              "env": { "LENS_DB": "/abs/path/to/repo/.lens/index.db" } }
  }
}

Tools

Tool Use it to…
lens_index Index / refresh a path (incremental: only changed files re-read).
lens_search Get ranked snippets within a token budget — use instead of reading files.
lens_outline Get a file's symbol map (functions/classes/headings) with line numbers.
lens_read Read an exact line range.
lens_map List indexed files + language breakdown.
lens_stats Index statistics.

How it works

  • Walks a tree (skipping node_modules, .git, build dirs, binaries, huge files).
  • Chunks each file into overlapping line windows and stores them in an FTS5 virtual table.
  • search runs an FTS5 MATCH ranked by bm25, then fills results up to a token budget (≈4 chars/token).
  • outline is regex-based per language (js/ts, py, go, rust, java, ruby, sql, markdown…).
  • index is incremental — files unchanged since last index (by mtime) are skipped.

MIT licensed.

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