# TinyRAG

> TinyRAG — spacecat398-tinyrag-mcp. Use this tool when you need to integrate large text datasets into a language model's knowledge base without requiring complex setup or infrastructure. TinyRAG solves the problem of efficiently indexing and accessing private knowledge bases by automatically chunking and indexing .txt documents. It provides a simple interface for LLM agents to query and retrieve relevant information from the indexed documents through MCP.

Canonical page: https://skillsregistry.net/skills/spacecat398-tinyrag-mcp  
JSON: https://api.skillsregistry.net/v1/skills/spacecat398-tinyrag-mcp

## Description

Enables LLM agents to access a private knowledge base through MCP by automatically chunking and indexing .txt documents, with zero configuration and no Docker or vector database required.

## Trust

- **Trust score (0–1):** 0.70
- **Verification tier:** verified
- **Last scanned:** 2026-08-30

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** database
- **Updated:** 2026-08-30

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/em3ue822gm)
- **Repository:** <https://github.com/spacecat398/TinyRAG-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": "spacecat398-tinyrag-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/spacecat398-tinyrag-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/spacecat398-tinyrag-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