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 registrytasks.json- Task historysprints.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