jamot-mcp — AI-to-Human Task Coordination MCP Server

A remote MCP server that lets AI agents assign tasks, check team workload, decompose complex instructions, and hand over full context to human contributors — all through a single SSE endpoint.

MCP Server Name: jamot-mcp
Transport: SSE
Endpoint: https://your-server:3001/sse

What This MCP Server Does

jamot-mcp is a task coordination MCP server that exposes 15 tools for AI agents to:

Create and assign tasks to human team members
Check workload before assigning (warns if someone is overloaded)
Decompose complex instructions into subtasks automatically
Attach full context (chat summary, goals, documents) to every task
Remember decisions and preferences across conversations
Suggest workload redistribution when the team is unbalanced

Quick Start

Run with Docker

docker run -d \
  -e MONGO_URI=mongodb+srv://user:password@cluster.mongodb.net/yourdb \
  -e WORKLOAD_THRESHOLD=5 \
  -p 3001:3001 \
  jamot/jamot-mcp:latest

Add to Your AI Platform

LibreChat (librechat.yaml):

mcpSettings:
  allowedDomains:
    - 'your-server'

mcpServers:
  jamot-mcp:
    type: sse
    url: http://your-server:3001/sse
    timeout: 60000

Claude Desktop (claude_desktop_config.json):

{
  "mcpServers": {
    "jamot-mcp": {
      "url": "http://your-server:3001/sse"
    }
  }
}

Environment Variables

Variable	Required	Default	Description
`MONGO_URI`	✅	—	MongoDB connection string
`WORKLOAD_THRESHOLD`	❌	`5`	Max active tasks per user before warning

MCP Tools

Task Management

Tool	Description
`create_a2h_task`	Create a task with full contextual handover (summary, goals, docs)
`edit_task`	Update task fields (title, status, assignee, due date)
`delete_task`	Delete task and cascade to subtasks
`get_tasks`	List tasks filtered by assignee or status

Task Decomposition

Tool	Description
`decompose_task`	Break a complex instruction into parent + subtasks
`smart_assign_and_decompose`	Auto-find best assignee + decompose in one call

Workload & Analytics

Tool	Description
`get_team_workload_report`	Active task count per user
`check_workload_before_assign`	Warn if user is overloaded, suggest alternatives
`suggest_redistribution`	Identify overloaded/underloaded members
`get_overdue_tasks`	Find tasks past their due date

Users

Tool	Description
`get_assignable_users`	Fetch all team members from database
`get_human_profiles`	Filter users by minimum impact score
`recommend_best_assignee`	Find best person by workload + competency match

Memory

Tool	Description
`save_memory`	Store context and decisions across conversations
`get_memory`	Recall past decisions and team preferences
`delete_memory`	Remove a memory entry

Recommended Agent Instructions

You are a task coordination agent connected to jamot-mcp.

RULES:
1. At the start of every conversation, call get_memory() to recall context.
2. Before assigning any task, always call check_workload_before_assign first.
3. Always use tools — never answer from general knowledge.
4. After important decisions, call save_memory() to persist them.
5. If someone seems overwhelmed, proactively call suggest_redistribution().

Database Requirements

Requires MongoDB with these collections:

users — team members (read-only, queried for assignments)
tasks — created and managed by this MCP server
agent_memory — auto-created for agent long-term memory

Built With

FastMCP 3.x — MCP server framework
Motor — async MongoDB driver
Python 3.11

License

MIT — built by Jamot

This server cannot be installed

F

license - not found

-

quality - not tested

C

maintenance

How are these scores calculated?

Resources

GitHub Repository

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