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mcp_server/knowledge.py
───────────────────
Knowledge Agent – The Web Researcher.
Responsibilities:
• Receive a medical topic / query from the Orchestrator
• Use Tavily to perform a targeted, reliable web search
• Summarise results into a concise medical context snippet
• Return findings via state.knowledge_context
All errors (network, Tavily quota, LLM) are caught and result in
a soft graceful fallback — never propagates exceptions upward.
"""
import logging
from chronic_chatbot.config import TAVILY_API_KEY
from mcp.server.fastmcp import FastMCP
logger = logging.getLogger(__name__)
mcp = FastMCP("ChronicKnowledgeServer")
# Tavily client — lazy-initialised so import errors don't crash startup
_tavily = None
def _get_tavily():
global _tavily
if _tavily is None:
try:
from tavily import TavilyClient
_tavily = TavilyClient(api_key=TAVILY_API_KEY)
except Exception as e:
logger.error(f"Tavily init failed: {e}")
return _tavily
@mcp.tool()
def tavily_search(query: str) -> str:
"""
Run a Tavily search and return a raw context string.
Returns an empty string on any error (caller handles fallback).
"""
client = _get_tavily()
if client is None:
return ""
try:
resp = client.search(
query=query,
search_depth="advanced",
max_results=4,
include_answer=True,
)
# Normalise: newer SDK returns object, older returns dict
if isinstance(resp, dict):
answer = resp.get("answer") or ""
results = resp.get("results") or []
else:
answer = getattr(resp, "answer", None) or ""
results = getattr(resp, "results", None) or []
parts = [str(answer)] if answer else []
for r in results[:4]:
if isinstance(r, dict):
parts.append(f"Source: {r.get('url','')}\n{r.get('content','')}")
else:
parts.append(f"Source: {getattr(r,'url','')}\n{getattr(r,'content','')}")
return "\n\n".join(parts)
except Exception as e:
logger.warning(f"Tavily search failed: {e}")
return ""
if __name__ == "__main__":
mcp.run(transport="stdio") |