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Medical AOP Example

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

graph LR
    A[Medical Agents] --> B[AOP MCP Server<br/>Port 8000]
    B --> C[Client<br/>Cursor/Python]
    B --> D[Discovery Tools]
    B --> E[Tool Execution]
    
    subgraph "Medical Agents"
        F[Chief Medical Officer]
        G[Virologist]
        H[Internist]
        I[Medical Coder]
        J[Diagnostic Synthesizer]
    end
    
    A --> F
    A --> G
    A --> H
    A --> I
    A --> J

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

2. Configure Cursor MCP Integration

Add to ~/.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

Discovery Tools

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

Response Format

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

Agent Metadata

Each agent includes:

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

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