# MOP

> MOP — sirasaki-konoha-mop. Use this tool when you need to automate collaboration among multiple LLM agents on complex tasks, enabling efficient role assignment, communication, and artifact integration. It solves problems of task management, agent coordination, and result consolidation, providing inputs such as task definitions and agent capabilities, and outputs like merged results and status updates. Ideal for use cases requiring multi-agent teamwork, such as large-scale data processing, content generation, and complex problem-solving.

Canonical page: https://skillsregistry.net/skills/sirasaki-konoha-mop  
JSON: https://api.skillsregistry.net/v1/skills/sirasaki-konoha-mop

## Description

A multi-agent orchestrator MCP server that enables LLM agents to collaborate on complex tasks by automating role assignment, inter-agent communication, and artifact integration. It provides tools for task decomposition, agent assignment, status tracking, code review, and result merging.

## Trust

- **Trust score (0–1):** 0.66
- **Verification tier:** scanned
- **Last scanned:** 2026-08-30

## Facts

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

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/k88fladi8q)
- **Repository:** <https://github.com/sirasaki-konoha/mop>

## 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": "sirasaki-konoha-mop"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/sirasaki-konoha-mop` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/sirasaki-konoha-mop/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