# search-docs

> Use this tool when you need to quickly find specific information within local Markdown documents using natural language queries. It solves problems of manual document searching and information retrieval, providing automatic indexing and section-level retrieval of relevant content. The tool takes natural language input and returns relevant document sections as output, ideal for use cases where rapid access to local documentation is required.

Canonical page: https://skillsregistry.net/skills/otolab-search-docs  
JSON: https://api.skillsregistry.net/v1/skills/otolab-search-docs

## Description

Enables AI agents to search local Markdown documents using natural language, with automatic indexing and section-level retrieval.

## Trust

- **Trust score (0–1):** 0.58
- **Verification tier:** scanned
- **Last scanned:** 2026-09-01

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-09-01

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/snptr79zv6)
- **Repository:** <https://github.com/otolab/search-docs>

## 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": "otolab-search-docs"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/otolab-search-docs` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/otolab-search-docs/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