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A real-world demonstration of the Agent Orchestration Protocol (AOP) using medical agents deployed as MCP tools.
Overview
This example showcases how to:
- Deploy multiple medical agents as MCP tools via AOP
- Use discovery tools for dynamic agent collaboration
- Execute real tool calls with structured schemas
- Integrate with keyless APIs for enhanced context
Architecture
Medical Agents
- Chief Medical Officer: Coordination, diagnosis, triage
- Virologist: Viral disease analysis and ICD-10 coding
- Internist: Internal medicine evaluation and HCC tagging
- Medical Coder: ICD-10 code assignment and compliance
- Diagnostic Synthesizer: Final report synthesis with confidence levels
Files
| File | Description |
|---|
medical_aop/server.py | AOP server exposing medical agents as MCP tools |
medical_aop/client.py | Discovery client with real tool execution |
README.md | This documentation |
Usage
1. Start the AOP Server
python -m examples.aop_examples.medical_aop.server
~/.cursor/mcp.json:
{
"mcpServers": {
"Medical AOP": {
"type": "http",
"url": "http://localhost:8000/mcp"
}
}
}
3. Use in Cursor
Enable “Medical AOP” in Cursor’s MCP settings, then:
Discover agents:
Call tool discover_agents with: {}
Execute medical coding:
Call tool Medical Coder with: {"task":"Patient: 45M, egfr 59 ml/min/1.73; non-African American. Provide ICD-10 suggestions and coding notes.","priority":"normal","include_images":false}
Review infection control:
Call tool Chief Medical Officer with: {"task":"Review current hospital infection control protocols in light of recent MRSA outbreak in ICU. Provide executive summary, policy adjustment recommendations, and estimated implementation costs.","priority":"high"}
4. Run Python Client
python -m examples.aop_examples.medical_aop.client
Features
Structured Schemas
- Custom input/output schemas with validation
- Priority levels (low/normal/high)
- Image processing support
- Confidence scoring
| Tool | Description |
|---|
discover_agents | List all available agents |
get_agent_details | Detailed agent information |
search_agents | Keyword-based agent search |
list_agents | Simple agent name list |
Real-world Integration
- Keyless API integration (disease.sh for epidemiology data)
- Structured medical coding workflows
- Executive-level policy recommendations
- Cost estimation and implementation timelines
All tools return consistent JSON:
{
"result": "Agent response text",
"success": true,
"error": null,
"confidence": 0.95,
"codes": ["N18.3", "Z51.11"]
}
Configuration
Server Settings
| Setting | Value |
|---|
| Port | 8000 |
| Transport | streamable-http |
| Timeouts | 40-50 seconds per agent |
| Logging | INFO level with traceback enabled |
- Tags for categorization
- Capabilities for matching
- Role classification
- Model configuration
Best Practices
- Use structured inputs: Leverage the custom schemas for better results
- Chain agents: Pass results between agents for comprehensive analysis
- Monitor timeouts: Adjust based on task complexity
- Validate responses: Check the
success field in all responses
- Use discovery: Query available agents before hardcoding tool names
Troubleshooting
| Issue | Solution |
|---|
| Connection refused | Ensure server is running on port 8000 |
| Tool not found | Use discover_agents to verify available tools |
| Timeout errors | Increase timeout values for complex tasks |
| Schema validation | Ensure input matches the defined JSON schema |
References
