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mess-mcp

Provide an MCP server for AI agents to manage physical-world tasks.

Updated Feb 1, 2026
Search Tags:#task management#mcp#ai agent#github integration#physical tasks#mess
One-click install
npx skills add https://github.com/teaguesterling/git-messe-af --skill mess-mcp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: mess-mcp
Source: https://github.com/teaguesterling/git-messe-af/tree/main/mcp
Command: npx skills add https://github.com/teaguesterling/git-messe-af --skill mess-mcp

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires @messe-af/core, @modelcontextprotocol/sdk, yaml, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill bridges the gap between AI agents and the physical world by enabling the creation, management, and tracking of real-world tasks assigned to human executors.

Core Features & Use Cases

  • Task Creation: Define physical tasks with clear intents, context, and priorities.
  • Status Tracking: Monitor the progress of tasks from pending to completion.
  • Resource Management: Fetch attachments like images or documents related to tasks.
  • Use Case: An AI agent can use this Skill to ask a human to "Check if the garage door is closed and provide a photo," then track the response and retrieve the image.

Quick Start

Use the mess-mcp skill to create a new request with the intent to check if the garage door is closed and provide an image.

Frequently Asked Questions about mess-mcp

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use an MCP server to dispatch physical tasks to human executors from an AI agent?▼

To dispatch physical tasks to human executors, use this Model Context Protocol (MCP) server to define tasks with clear intents, context, and priorities, then track their progress from pending to completion.

Can I track real-world task status updates and fetch related resources with an AI agent?▼

Yes, you can track real-world task status updates and fetch related resources. The MCP server manages task progress monitoring and retrieves attachments like images or documents linked to the physical tasks.

Does the mess-mcp task management server support GitHub integration for persistent storage?▼

Yes, the task management server supports GitHub integration for persistent storage and synchronization. It operates in both local-only and hybrid modes to maintain task state across executions.

What is needed to set up an MCP server for physical-world task execution systems?▼

Setting up an MCP server for physical-world task execution requires the Model Context Protocol SDK, the core MESS protocol library, and YAML configuration to define the task execution environment.

How does capability discovery work for AI agents interacting with physical-world tasks?▼

Capability discovery works through the defined MESS protocol, allowing AI agents to query available physical-world task execution systems, match task intents with human executor capabilities, and manage resource fetching.

What are the limitations of using GitHub integration for AI agent task management synchronization?▼

GitHub integration for AI agent task management synchronization operates in local-only and hybrid modes, meaning persistent storage depends on network availability and repository access for task state synchronization.

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