CI-DEV
2 days ago
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import asyncio
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import json
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from typing import Dict
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import requests
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from swarms.structs.aop import AOPCluster
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from swarms.tools.mcp_client_tools import execute_tool_call_simple
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def _select_tools_by_keyword(tools: list, keyword: str) -> list:
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"""
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Return tools whose name or description contains the keyword
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(case-insensitive).
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"""
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kw = keyword.lower()
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selected = []
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for t in tools:
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name = t.get("function", {}).get("name", "")
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desc = t.get("function", {}).get("description", "")
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if kw in name.lower() or kw in desc.lower():
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selected.append(t)
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return selected
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def _example_payload_from_schema(tools: list, tool_name: str) -> dict:
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"""
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Construct a minimal example payload for a given tool using its JSON schema.
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Falls back to a generic 'task' if schema not present.
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"""
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for t in tools:
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fn = t.get("function", {})
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if fn.get("name") == tool_name:
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schema = fn.get("parameters", {})
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required = schema.get("required", [])
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props = schema.get("properties", {})
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payload = {}
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for r in required:
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if r in props:
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if props[r].get("type") == "string":
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payload[r] = (
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"Example patient case: 45M, egfr 59 ml/min/1.73"
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)
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elif props[r].get("type") == "boolean":
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payload[r] = False
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else:
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payload[r] = None
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if not payload:
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payload = {
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"task": "Provide ICD-10 suggestions for the case above"
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}
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return payload
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return {"task": "Provide ICD-10 suggestions for the case above"}
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def main() -> None:
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cluster = AOPCluster(
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urls=["http://localhost:8000/mcp"],
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transport="streamable-http",
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)
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tools = cluster.get_tools(output_type="dict")
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print(f"Tools: {len(tools)}")
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coding_tools = _select_tools_by_keyword(tools, "coder")
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names = [t.get("function", {}).get("name") for t in coding_tools]
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print(f"Coding-related tools: {names}")
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# Build a real payload for "Medical Coder" and execute the tool call
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tool_name = "Medical Coder"
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payload: Dict[str, object] = _example_payload_from_schema(tools, tool_name)
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# Enrich with public keyless data (epidemiology context via disease.sh)
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try:
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epi = requests.get(
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"https://disease.sh/v3/covid-19/countries/USA?strict=true",
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timeout=5,
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)
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if epi.ok:
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data = epi.json()
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epi_summary = (
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f"US COVID-19 context: cases={data.get('cases')}, "
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f"todayCases={data.get('todayCases')}, deaths={data.get('deaths')}"
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)
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base_task = payload.get("task") or ""
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payload["task"] = (
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f"{base_task}\n\nEpidemiology context (no key API): {epi_summary}"
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)
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except Exception:
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pass
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print("Calling tool:", tool_name)
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request = {
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"function": {
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"name": tool_name,
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"arguments": payload,
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}
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}
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result = asyncio.run(
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execute_tool_call_simple(
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response=request,
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server_path="http://localhost:8000/mcp",
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output_type="json",
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transport="streamable-http",
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verbose=False,
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)
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)
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print("Response:")
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print(result)
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if __name__ == "__main__":
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main()
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@ -0,0 +1,166 @@
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# Import medical agents defined in the demo module
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from examples.demos.medical.medical_coder_agent import (chief_medical_officer,
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internist,
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medical_coder,
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synthesizer,
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virologist)
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from swarms.structs.aop import AOP
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def _enrich_agents_metadata() -> None:
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"""
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Add lightweight tags/capabilities/roles to imported agents for
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better discovery results.
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"""
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chief_medical_officer.tags = [
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"coordination",
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"diagnosis",
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"triage",
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]
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chief_medical_officer.capabilities = [
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"case-intake",
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"differential",
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"planning",
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]
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chief_medical_officer.role = "coordinator"
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virologist.tags = ["virology", "infectious-disease"]
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virologist.capabilities = ["viral-analysis", "icd10-suggestion"]
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virologist.role = "specialist"
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internist.tags = ["internal-medicine", "evaluation"]
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internist.capabilities = [
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"system-review",
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"hcc-tagging",
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"risk-stratification",
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]
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internist.role = "specialist"
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medical_coder.tags = ["coding", "icd10", "compliance"]
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medical_coder.capabilities = [
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"code-assignment",
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"documentation-review",
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]
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medical_coder.role = "coder"
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synthesizer.tags = ["synthesis", "reporting"]
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synthesizer.capabilities = [
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"evidence-reconciliation",
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"final-report",
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]
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synthesizer.role = "synthesizer"
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def _medical_input_schema() -> dict:
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return {
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"type": "object",
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"properties": {
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"task": {
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"type": "string",
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"description": "Patient case or instruction for the agent",
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},
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"priority": {
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"type": "string",
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"enum": ["low", "normal", "high"],
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"description": "Processing priority",
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},
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"include_images": {
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"type": "boolean",
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"description": "Whether to consider linked images if provided",
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"default": False,
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},
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"img": {
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"type": "string",
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"description": "Optional image path/URL",
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},
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"imgs": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of images",
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},
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},
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"required": ["task"],
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"additionalProperties": False,
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}
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def _medical_output_schema() -> dict:
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return {
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"type": "object",
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"properties": {
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"result": {"type": "string"},
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"success": {"type": "boolean"},
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"error": {"type": "string"},
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"confidence": {
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"type": "number",
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"minimum": 0,
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"maximum": 1,
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"description": "Optional confidence in the assessment",
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},
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"codes": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional list of suggested ICD-10 codes",
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},
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},
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"required": ["result", "success"],
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"additionalProperties": True,
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}
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def main() -> None:
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"""
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Start an AOP MCP server that exposes the medical agents as tools with
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structured schemas and per-agent settings.
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"""
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_enrich_agents_metadata()
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deployer = AOP(
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server_name="Medical-AOP-Server",
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port=8000,
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verbose=False,
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traceback_enabled=True,
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log_level="INFO",
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transport="streamable-http",
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)
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input_schema = _medical_input_schema()
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output_schema = _medical_output_schema()
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# Register each agent with a modest, role-appropriate timeout
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deployer.add_agent(
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chief_medical_officer,
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timeout=45,
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input_schema=input_schema,
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output_schema=output_schema,
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)
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deployer.add_agent(
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virologist,
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timeout=40,
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input_schema=input_schema,
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output_schema=output_schema,
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)
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deployer.add_agent(
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internist,
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timeout=40,
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input_schema=input_schema,
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output_schema=output_schema,
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)
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deployer.add_agent(
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medical_coder,
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timeout=50,
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input_schema=input_schema,
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output_schema=output_schema,
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)
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deployer.add_agent(
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synthesizer,
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timeout=45,
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input_schema=input_schema,
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output_schema=output_schema,
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)
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deployer.run()
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if __name__ == "__main__":
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main()
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