Noctua MCP Server

Instructions

Input Schema

Input Schema (JSON Schema)

Implementation Reference

• src/noctua_mcp/mcp_server.py:242-336 (handler)

  Primary handler implementation for the 'add_fact' tool. This async function is decorated with @mcp.tool(), defining the tool logic: resolves model identifiers, constructs BaristaClient request via req_add_fact, executes batch update, and returns success/error responses with validation handling. Includes comprehensive docstring serving as schema with parameters, returns, and usage examples.
  @mcp.tool() async def add_fact( model_id: str, subject_id: str, object_id: str, predicate_id: str ) -> Dict[str, Any]: """ Add a fact (edge/relation) between two individuals in a model. Args: model_id: The GO-CAM model identifier subject_id: Subject individual ID or variable object_id: Object individual ID or variable predicate_id: Relation predicate (e.g., "RO:0002333" for enabled_by) Returns: Barista API response Examples: # Connect molecular function to gene product (enabled_by) add_fact("gomodel:12345", "mf1", "gp1", "RO:0002333") # Connect molecular function to cellular component (occurs_in) add_fact("gomodel:12345", "mf1", "cc1", "BFO:0000066") # Connect molecular function to biological process (part_of) add_fact("gomodel:12345", "mf1", "bp1", "BFO:0000050") # Add causal relationship between activities add_fact("gomodel:12345", "mf1", "mf2", "RO:0002411") # causally upstream of add_fact("gomodel:12345", "mf1", "mf2", "RO:0002629") # directly positively regulates add_fact("gomodel:12345", "mf1", "mf2", "RO:0002630") # directly negatively regulates add_fact("gomodel:12345", "mf1", "mf2", "RO:0002413") # provides input for # Add regulates relationships add_fact("gomodel:12345", "mf1", "bp1", "RO:0002211") # regulates add_fact("gomodel:12345", "mf1", "bp1", "RO:0002213") # positively regulates add_fact("gomodel:12345", "mf1", "bp1", "RO:0002212") # negatively regulates # Add indirect regulation relationships add_fact("gomodel:12345", "mf1", "mf2", "RO:0002407") # indirectly positively regulates add_fact("gomodel:12345", "mf1", "mf2", "RO:0002409") # indirectly negatively regulates # Add causal relationships with effects add_fact("gomodel:12345", "mf1", "mf2", "RO:0002304") # causally upstream of, positive effect add_fact("gomodel:12345", "mf1", "mf2", "RO:0002305") # causally upstream of, negative effect # Add small molecule regulation relationships add_fact("gomodel:12345", "sm1", "mf1", "RO:0012005") # is small molecule activator of add_fact("gomodel:12345", "sm1", "mf1", "RO:0012006") # is small molecule inhibitor of # Use with existing individual IDs from model add_fact("gomodel:12345", "gomodel:12345/abc123", "gomodel:12345/def456", "RO:0002333") """ client = get_client() # Resolve any variables to actual IDs resolved_subject = client._resolve_identifier(model_id, subject_id) resolved_object = client._resolve_identifier(model_id, object_id) req = client.req_add_fact(model_id, resolved_subject, resolved_object, predicate_id) resp = client.m3_batch([req]) if resp.validation_failed: return { "success": False, "error": "Validation failed", "reason": resp.validation_reason, "rolled_back": True, "fact": { "subject": subject_id, "predicate": predicate_id, "object": object_id } } if resp.error: return { "success": False, "error": resp.error, "model_id": model_id, "fact": { "subject": subject_id, "predicate": predicate_id, "object": object_id } } # Return minimal success response return { "success": True, "fact_added": True }