CSOAI-ORG

Agent Orchestrator MCP Server

Community CSOAI-ORG
Updated

Multi-agent task management: create agents, delegate tasks with trust-based routing, coordinate file access, run sprints, and monitor performance.

By MEOK AI Labs — Sovereign AI tools for everyone.

Agent Orchestrator MCP Server

Multi-agent task management system for AI applications. Create agents with roles and capabilities, delegate tasks with trust-based routing, coordinate file access to prevent conflicts, run focused sprints, and monitor performance through a unified dashboard.

Based on the Sovereign Temple 47-agent coordination framework, simplified for standalone use. Data persists in ~/.mcp-agents/.

Tools

Tool Description
create_agent Register an agent with name, role, department, and capabilities
list_agents List all agents with trust levels and task counts
delegate_task Assign tasks to specific agents or auto-route by capability/trust
complete_task Mark tasks done, update agent trust based on success/failure
acquire_files Lock files for coordinated multi-agent editing
release_files Release file locks after task completion
start_sprint Begin a focused sprint with goals and time limit
complete_sprint Close a sprint and record completion rate
get_dashboard Full orchestration overview: agents, tasks, sprints, locks
get_task_queue Browse tasks filtered by status or agent

Installation

pip install mcp

Usage

Run the server

python server.py

Claude Desktop config

{
  "mcpServers": {
    "agent-orchestrator": {
      "command": "python",
      "args": ["/path/to/agent-orchestrator-mcp/server.py"]
    }
  }
}

Example workflow

1. Create agents:

Tool: create_agent
Input: {"name": "Research Bot", "role": "researcher", "department": "research", "capabilities": ["web_search", "analysis"]}
Output: {"status": "created", "agent_id": "research_bot", "role": "researcher"}

2. Delegate a task:

Tool: delegate_task
Input: {"task": "Research competitor pricing models", "capability": "web_search", "priority": "high"}
Output: {"status": "delegated", "task_id": "a1b2c3d4", "agent_id": "research_bot"}

3. Coordinate file access:

Tool: acquire_files
Input: {"agent_id": "research_bot", "files": ["report.md", "data.json"], "task_id": "a1b2c3d4", "exclusive": true}
Output: {"status": "acquired", "files": ["report.md", "data.json"]}

4. Complete the task:

Tool: complete_task
Input: {"task_id": "a1b2c3d4", "agent_id": "research_bot", "result_summary": "Found 5 competitor pricing tiers...", "care_score": 0.8}
Output: {"status": "completed", "task_id": "a1b2c3d4"}

5. Check the dashboard:

Tool: get_dashboard
Output: {"agents": {"total": 3, "active": 3, "avg_trust": 0.52}, "tasks": {"total": 12, "by_status": {"completed": 8, "assigned": 4}}, ...}

Trust System

Agents accumulate trust through successful task completion:

  • Successful task: trust += 0.02 x care_score (max 1.0)
  • Failed task: trust -= 0.05 (min 0.0)
  • Auto-routing prefers higher-trust agents
  • Trust persists across sessions

Data Storage

All data persists in ~/.mcp-agents/:

  • agents.json - Agent registry
  • tasks.json - Task history
  • sprints.json - Sprint records

Pricing

Tier Limit Price
Free 100 calls/day, 10 agents max $0
Pro Unlimited agents, webhook notifications, LLM-powered routing $9/mo
Enterprise Custom + team sharing + audit logs + SSO Contact us

License

MIT

MCP Server · Populars

MCP Server · New