# paperless-agent-toolkit

> paperless-agent-toolkit — tobiasschuerg-paperless-agent-toolkit. Use this tool when you need to streamline document management and automate tasks such as search, OCR, and metadata updates. It solves problems related to document organization, retrieval, and duplication, providing a robust interface for managing documents via natural language inputs and outputs. Ideal for use cases involving large document collections, it enables efficient tagging, correspondent, and type management.

Canonical page: https://skillsregistry.net/skills/tobiasschuerg-paperless-agent-toolkit  
JSON: https://api.skillsregistry.net/v1/skills/tobiasschuerg-paperless-agent-toolkit

## Description

MCP server for Paperless-ngx document management, enabling search, OCR content access, metadata updates, tag/correspondent/type management, and duplicate detection via natural language.

## Trust

- **Trust score (0–1):** 0.69
- **Verification tier:** scanned
- **Last scanned:** 2026-08-29

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** search
- **Updated:** 2026-08-29

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/axfppjnx98)
- **Repository:** <https://github.com/tobiasschuerg/paperless-agent-toolkit>

## 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": "tobiasschuerg-paperless-agent-toolkit"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/tobiasschuerg-paperless-agent-toolkit` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/tobiasschuerg-paperless-agent-toolkit/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