Jamot

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:[email protected]/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