Independent local file index and fast search for AI agents over MCP
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# Pathgrove <!-- mcp-name: io.github.elis132/pathgrove-mcp --> **Give your AI assistant a fast way to find your local files.** Pathgrove lets an MCP-compatible assistant find files without knowing their location first. It keeps a reusable filename index, so repeated searches do not have to walk every folder again. Search text inside files, inspect matches and get the paths you need through the same connection. Ask your assistant: > Find invoice PDFs modified in the last seven days. > > Find Python files containing `API_BASE_URL`. > > Show me a preview of `annual-report.txt`. Runs locally on **Windows, Linux and macOS**. You choose the search folders. Pathgrove needs no hosted search service or API key; your MCP client controls what is sent to its model. ## The payoff On a physical **Mac mini M1 with about four million indexed entries**, repeated filename searches returned these results: | Search | Fresh filesystem scan | Pathgrove MCP | Speed-up | |---|---|---|---| | `invoice` | 77.06 seconds | **64 milliseconds** | **1,196×** | | Broad substring `in` | 77.65 seconds | **2.39 seconds** | **32×** | Five trials per query, with identical ordered first-50 result pages checked each time. The comparison is against a fresh Python `os.scandir` traversal, using a **warmed filename index**. Indexing and startup are excluded; it is not a comparison against Everything or Spotlight. These measurements describe the recorded baseline, before the later indexing optimizations. [Method and raw data →](https://github.com/elis132/pathgrove-mcp/blob/main/docs/COMPARISON.md) The first index costs time: that Mac traversal took **17.2 minutes**, and inaccessible paths still limited coverage. Start with your project or document folders. Optimized Windows full-drive completion is **estimated at about 75 minutes**, not measured to completion. [Windows measurements and estimate →](https://github.com/elis132/pathgrove-mcp/blob/main/docs/WINDOWS-ESTIMATE.md) ## Get started Requires…
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