lens
Provides token-efficient code retrieval for coding agents by indexing repositories and enabling ranked snippet search, symbol outlines, and surgical line reads.
README
🔎 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.
searchruns an FTS5MATCHranked by bm25, then fills results up to a token budget (≈4 chars/token).outlineis regex-based per language (js/ts, py, go, rust, java, ruby, sql, markdown…).indexis incremental — files unchanged since last index (by mtime) are skipped.
MIT licensed.
推荐服务器
Baidu Map
百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。
Playwright MCP Server
一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。
Magic Component Platform (MCP)
一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。
Audiense Insights MCP Server
通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。
VeyraX
一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。
graphlit-mcp-server
模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。
Kagi MCP Server
一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。
e2b-mcp-server
使用 MCP 通过 e2b 运行代码。
Neon MCP Server
用于与 Neon 管理 API 和数据库交互的 MCP 服务器
Exa MCP Server
模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。