BioContextAI Knowledgebase MCP

Instructions

Implementation Reference

• src/biocontext_kb/core/scholarly/_search_publications.py:12-72 (handler)

  Core handler function for the 'search_google_scholar_publications' tool (likely the target 'bc_search_google_scholar_publications'), decorated with @core_mcp.tool(). Implements search logic using scholarly library, proxy support, and result parsing.
  @core_mcp.tool() def search_google_scholar_publications( query: Annotated[ str, Field(description="Search query (e.g., 'machine learning' or 'author:\"John Smith\" deep learning')"), ], max_results: Annotated[int, Field(description="Maximum number of publications to return (1-50)", ge=1, le=50)] = 10, use_proxy: Annotated[bool, Field(description="Use free proxies to avoid rate limiting")] = True, ) -> Dict[str, Any]: """Search Google Scholar for publications with support for author search using 'author:"Name"' syntax. WARNING: Use responsibly, may block excessive queries. Returns: dict: Publications list with title, authors, venue, year, citations, abstract, bib entry or error message. """ try: # Set up proxy if requested if use_proxy: try: pg = ProxyGenerator() pg.FreeProxies() scholarly.use_proxy(pg) logger.info("Proxy configured for Google Scholar requests") except Exception as e: logger.warning(f"Failed to set up proxy: {e}") # Continue without proxy # Search for publications search_query = scholarly.search_pubs(query) publications = [] for count, pub in enumerate(search_query): if count >= max_results: break # Extract publication information bib = pub.get("bib", {}) pub_info = { "title": bib.get("title", ""), "author": bib.get("author", ""), "venue": bib.get("venue", ""), "pub_year": bib.get("pub_year", ""), "abstract": bib.get("abstract", ""), "pub_url": bib.get("pub_url", ""), "eprint_url": pub.get("eprint_url", ""), "num_citations": pub.get("num_citations", 0), "citedby_url": pub.get("citedby_url", ""), "url_scholarbib": pub.get("url_scholarbib", ""), } publications.append(pub_info) return {"query": query, "total_found": len(publications), "publications": publications} except Exception as e: logger.error(f"Error searching Google Scholar publications: {e}") return { "error": f"Failed to search Google Scholar publications: {e!s}", "note": "Google Scholar may be blocking requests. Publication searches are particularly risky. Try again later or use alternative databases like PubMed/EuropePMC.", }
• src/biocontext_kb/core/__init__.py:27-28 (registration)

  Conditional import that registers the scholarly tools, including search_google_scholar_publications, by importing the module containing the @core_mcp.tool()-decorated function.
  if os.getenv("MCP_ENVIRONMENT") != "PRODUCTION" or os.getenv("MCP_INCLUDE_SCHOLARLY", "false").lower() == "true": from .scholarly import *
• src/biocontext_kb/core/scholarly/_search_publications.py:13-20 (schema)

  Pydantic schema definition via Annotated Fields for input parameters: query (str), max_results (int 1-50), use_proxy (bool). Output is Dict[str, Any].
  def search_google_scholar_publications( query: Annotated[ str, Field(description="Search query (e.g., 'machine learning' or 'author:\"John Smith\" deep learning')"), ], max_results: Annotated[int, Field(description="Maximum number of publications to return (1-50)", ge=1, le=50)] = 10, use_proxy: Annotated[bool, Field(description="Use free proxies to avoid rate limiting")] = True, ) -> Dict[str, Any]:
• src/biocontext_kb/core/scholarly/__init__.py:9-13 (registration)

  Module __init__.py re-exports the tool function, facilitating its import and registration when the scholarly module is imported.
  from ._search_publications import search_google_scholar_publications __all__ = [ "search_google_scholar_publications", ]
• src/biocontext_kb/core/_server.py:3-7 (helper)

  Definition of core_mcp FastMCP server instance with 'BC' prefix, used for tool registration via decorators.
  core_mcp = FastMCP( # type: ignore "BC", instructions="Provides access to biomedical knowledge bases.", )