From 3796ca520fea65691192cecac1dc846099e06ea5 Mon Sep 17 00:00:00 2001 From: CI-DEV <154627941+IlumCI@users.noreply.github.com> Date: Mon, 6 Oct 2025 20:30:58 +0300 Subject: [PATCH] Add files via upload --- docs/examples/aop_medical.md | 171 +++++++++++++++++++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 docs/examples/aop_medical.md diff --git a/docs/examples/aop_medical.md b/docs/examples/aop_medical.md new file mode 100644 index 00000000..76ed1508 --- /dev/null +++ b/docs/examples/aop_medical.md @@ -0,0 +1,171 @@ +# 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 + +```mermaid +graph LR + A[Medical Agents] --> B[AOP MCP Server
Port 8000] + B --> C[Client
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 +```bash +python -m examples.aop_examples.medical_aop.server +``` + +### 2. Configure Cursor MCP Integration + +Add to `~/.cursor/mcp.json`: + +```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 +```bash +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: +```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 + +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 + +## 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 + +- [AOP Reference](https://docs.swarms.world/en/latest/swarms/structs/aop/) +- [MCP Integration](https://docs.swarms.ai/examples/mcp-integration) +- [Protocol Overview](https://docs.swarms.world/en/latest/protocol/overview/)