# local-notes-search-mcp

> local-notes-search-mcp — furkiozknn-local-notes-search-mcp. Use this tool when you need to search your local files using natural language queries, solving the problem of remembering specific keywords or phrases used in files from months ago. It provides semantic search capabilities with incremental re-indexing, returning answers as file and line numbers without requiring a server, API key, or network connection. Ideal for use cases where you need to quickly find information within your own files, such as when working with git repositories.

Canonical page: https://skillsregistry.net/skills/furkiozknn-local-notes-search-mcp  
JSON: https://api.skillsregistry.net/v1/skills/furkiozknn-local-notes-search-mcp

## Description

Ask your own files a question in plain language instead of guessing the keyword you typed six months ago. Semantic search as an MCP server: sqlite-vec + a multilingual ONNX model, incremental re-indexing, answers returned as file:line. No server, no API key, no network at query time. Grounded LLM Q&A is opt-in. 50 tests.

## Trust

- **Trust score (0–1):** 0.87
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** MIT
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/Furkiozknn/local-notes-search-mcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "furkiozknn-local-notes-search-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/furkiozknn-local-notes-search-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/furkiozknn-local-notes-search-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt