mcp-recall-md

mcp-recall-md

Enables semantic search over local markdown note collections using vector embeddings, with real-time file watching and zero-config setup.

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README

<h1 align="center">mcp-recall-md</h1>

<p align="center"> Local semantic search for your markdown notes — via <a href="https://modelcontextprotocol.io/">MCP</a>. </p>

<p align="center"> <a href="https://pypi.org/project/proj-mcp-recall-md/"><img src="https://img.shields.io/pypi/v/proj-mcp-recall-md" alt="PyPI version"></a> <a href="https://pypi.org/project/proj-mcp-recall-md/"><img src="https://img.shields.io/pypi/pyversions/proj-mcp-recall-md" alt="Python versions"></a> <a href="LICENSE"><img src="https://img.shields.io/github/license/kalikin-artem/proj-mcp-recall-md" alt="License"></a> <a href="https://ko-fi.com/kalikinartem"><img src="https://img.shields.io/badge/Ko--fi-Support%20this%20project-FF5E5B?logo=ko-fi&logoColor=white" alt="Ko-fi"></a> </p>

<br>

graph LR
    A["<b>.md files</b><br><i>Obsidian · Logseq · plain md</i>"] -- "watch + index" --> B["<b>mcp-recall-md</b><br><i>local vector store</i>"]
    C["<b>AI client</b><br><i>Claude · Cursor · Windsurf</i>"] -- "MCP search" --> B
    style A fill:#f9f4eb,stroke:#c9a96e,color:#333
    style B fill:#e8f0fe,stroke:#4a86c8,color:#333
    style C fill:#e8f5e9,stroke:#5b9a5b,color:#333

"Search my notes about Kubernetes networking"

→ finds kubernetes-networking.md (similarity: 0.53) — even though you phrased it differently than the note

<br>

  • Search by meaning, not keywords — finds notes even when your wording doesn't match
  • 100% offline — no API keys, no cloud, nothing leaves your machine
  • Zero config — point at your folders, restart your AI client, done
  • Real-time sync — file watcher picks up changes instantly, re-embeds only what changed

Quick start

Add to your MCP client config and restart:

{
  "mcpServers": {
    "mcp-recall-md": {
      "command": "uvx",
      "args": ["mcp-recall-md", "--vaults", "C:/Users/you/notes"]
    }
  }
}

Config file location: .mcp.json (Claude Code) · claude_desktop_config.json (Claude Desktop) · Cursor / Windsurf MCP settings

That's it. Your notes are searchable.


Installation

The quick start above uses uvx (recommended). Other options:

<details> <summary><b>pip</b></summary>

pip install mcp-recall-md
{
  "mcpServers": {
    "mcp-recall-md": {
      "command": "mcp-recall-md",
      "args": ["--vaults", "C:/Users/you/notes"]
    }
  }
}

</details>

<details> <summary><b>Standalone .exe (no Python needed)</b></summary>

  1. Download mcp-recall-md.exe from the latest release
  2. Put it somewhere permanent (e.g. C:\Tools\mcp-recall-md\)
{
  "mcpServers": {
    "mcp-recall-md": {
      "command": "C:/Tools/mcp-recall-md/mcp-recall-md.exe",
      "args": ["--vaults", "C:/Users/you/notes"]
    }
  }
}

</details>


Configuration

Multiple vaults

List all folders — each is indexed independently:

"args": ["--vaults", "C:/notes/work", "C:/notes/personal", "C:/docs"]

.recallignore

Drop a .recallignore in any vault root to exclude files. Standard .gitignore syntax:

.obsidian/
_templates/
drafts/

CLI flags

Flag Default Description
--vaults (none) Folders to index and watch
--db-path ~/.mcp-recall-md/db ChromaDB storage location
--verbose off Debug logging to stderr

Tools

Your AI assistant gets these tools automatically via MCP:

Tool Description
search Find notes by meaning — returns ranked results with similarity scores and file paths
status Show indexed article count and watched vaults
index Manually store an article (for use without --vaults)
remove Delete an article from the index

Most users only interact with search — everything else is automatic.


Troubleshooting

Problem Fix
Search returns nothing Check that --vaults points to folders with .md files
First run is slow Embedding model (~80 MB) downloads once on first use
Need to debug Add --verbose, check ~/.mcp-recall-md/server.log
Force re-index Delete ~/.mcp-recall-md/db and restart

Logs: ~/.mcp-recall-md/server.log (5 MB max, 3 rotated backups)


Limitations

  • Single-chunk embedding — large files (10k+ words) may search less precisely than shorter notes
  • English-optimized — other languages work but with lower accuracy

License

MIT

<!-- mcp-name: io.github.kalikin-artem/recall-md -->

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