Agent Orchestrator MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Agent Orchestrator MCP Servercreate a research agent for competitor analysis"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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 |
| Register an agent with name, role, department, and capabilities |
| List all agents with trust levels and task counts |
| Assign tasks to specific agents or auto-route by capability/trust |
| Mark tasks done, update agent trust based on success/failure |
| Lock files for coordinated multi-agent editing |
| Release file locks after task completion |
| Begin a focused sprint with goals and time limit |
| Close a sprint and record completion rate |
| Full orchestration overview: agents, tasks, sprints, locks |
| Browse tasks filtered by status or agent |
Installation
pip install mcpUsage
Run the server
python server.pyClaude 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
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