AOP Medical Swarm

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Overview

• 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

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Architecture

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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

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Files

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Usage

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1. Start the AOP Server

python -m examples.aop_examples.medical_aop.server

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2. Configure Cursor MCP Integration

{
  "mcpServers": {
    "Medical AOP": {
      "type": "http",
      "url": "http://localhost:8000/mcp"
    }
  }
}

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3. Use in Cursor

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Discover agents:

Call tool discover_agents with: {}

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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}

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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"}

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4. Run Python Client

python -m examples.aop_examples.medical_aop.client

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Features

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Structured Schemas

• Custom input/output schemas with validation
• Priority levels (low/normal/high)
• Image processing support
• Confidence scoring

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Discovery Tools

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Real-world Integration

• Keyless API integration (disease.sh for epidemiology data)
• Structured medical coding workflows
• Executive-level policy recommendations
• Cost estimation and implementation timelines

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Response Format

{
  "result": "Agent response text",
  "success": true,
  "error": null,
  "confidence": 0.95,
  "codes": ["N18.3", "Z51.11"]
}

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Configuration

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Server Settings

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Agent Metadata

• Tags for categorization
• Capabilities for matching
• Role classification
• Model configuration

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Best Practices

1. Use structured inputs: Leverage the custom schemas for better results
2. Chain agents: Pass results between agents for comprehensive analysis
3. Monitor timeouts: Adjust based on task complexity
4. Validate responses: Check the success field in all responses
5. Use discovery: Query available agents before hardcoding tool names

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Troubleshooting

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References

• AOP Reference
• MCP Integration
• Protocol Overview