BioContextAI Knowledgebase MCP

Instructions

Implementation Reference

• src/biocontext_kb/core/ols/_search_ontology_terms.py:9-98 (handler)

  The handler function for the 'search_ontology_terms' tool (likely 'bc_search_ontology_terms' under BC MCP server). Implements search across OLS ontologies using the EBI OLS API, processes results into structured terms and groups by ontology.
  @core_mcp.tool() def search_ontology_terms( search_term: Annotated[str, Field(description="Term to search for")], ontologies: Annotated[ str, Field( description="Comma-separated ontology IDs (e.g., 'efo,go,chebi'). Leave empty for all. Use get_available_ontologies() to see options" ), ] = "", size: Annotated[ int, Field(description="Maximum number of results to return"), ] = 20, exact_match: Annotated[ bool, Field(description="Whether to perform exact match search"), ] = False, ) -> Dict[str, Any]: """Search for terms across multiple ontologies in OLS. Use get_available_ontologies() first to discover ontologies. Returns: dict: Terms array, terms_by_ontology grouped results, total_results, ontologies_found list or error message. """ if not search_term: return {"error": "search_term must be provided"} url = "https://www.ebi.ac.uk/ols4/api/v2/entities" params = { "search": search_term, "size": str(size), "lang": "en", "exactMatch": str(exact_match).lower(), "includeObsoleteEntities": "false", } # Add ontology filter if specified if ontologies.strip(): # Convert comma-separated string to individual ontologyId parameters ontology_list = [ont.strip() for ont in ontologies.split(",") if ont.strip()] if ontology_list: params["ontologyId"] = ",".join(ontology_list) try: response = requests.get(url, params=params) response.raise_for_status() data = response.json() if not data.get("elements"): return {"error": "No terms found"} # Extract terms with comprehensive information terms = [ { "id": element.get("curie", "").replace(":", "_"), "curie": element.get("curie", ""), "label": element.get("label", ""), "definition": element.get("definition", ""), "synonyms": element.get("synonym", []), "ontology_name": element.get("ontologyName", ""), "ontology_prefix": element.get("ontologyPrefix", ""), "is_defining_ontology": element.get("isDefiningOntology", False), "is_obsolete": element.get("isObsolete", False), "has_hierarchical_children": element.get("hasHierarchicalChildren", False), "has_hierarchical_parents": element.get("hasHierarchicalParents", False), "num_descendants": element.get("numDescendants", 0), "appears_in": element.get("appearsIn", []), } for element in data["elements"] ] # Group results by ontology for better organization results_by_ontology: Dict[str, List[Dict[str, Any]]] = {} for term in terms: ontology = term["ontology_name"] or term["ontology_prefix"] or "unknown" if ontology not in results_by_ontology: results_by_ontology[ontology] = [] results_by_ontology[ontology].append(term) return { "terms": terms, "terms_by_ontology": results_by_ontology, "total_results": len(terms), "ontologies_found": list(results_by_ontology.keys()), } except requests.exceptions.RequestException as e: return {"error": f"Failed to search ontology terms: {e!s}"}
• src/biocontext_kb/core/ols/__init__.py:8-8 (registration)

  Imports the search_ontology_terms tool function for exposure in the OLS module.
  from ._search_ontology_terms import search_ontology_terms
• src/biocontext_kb/core/_server.py:3-6 (registration)

  Defines the core_mcp FastMCP server instance named 'BC', where tools like search_ontology_terms are registered via @core_mcp.tool() decorator. Tool names may be prefixed with 'bc_' due to the 'BC' server identifier.
  core_mcp = FastMCP( # type: ignore "BC", instructions="Provides access to biomedical knowledge bases.", )