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register_relationship

Define and store relationships between code entities, such as calls, inherits, or imports, to maintain structured connections and metadata within a codebase.

Instructions

Register a relationship between entities.

Args: from_entity: Name of the source entity to_entity: Name of the target entity relationship_type: Type of relationship (calls, inherits, imports, etc.) metadata: Additional metadata as key-value pairs

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
from_entityYes
metadataNo
relationship_typeYes
to_entityYes

Implementation Reference

  • The handler function for the 'register_relationship' tool. It finds entities by name, creates a relation between them using KnowledgeGraph.add_relation, saves the knowledge graph if storage is configured, and returns the relation ID or error message.
    def register_relationship(
        from_entity: str,
        to_entity: str,
        relationship_type: str,
        metadata: dict[str, Any] | None = None,
    ) -> str:
        """Register a relationship between entities.
    
        Args:
            from_entity: Name of the source entity
            to_entity: Name of the target entity
            relationship_type: Type of relationship (calls, inherits, imports, etc.)
            metadata: Additional metadata as key-value pairs
        """
        # Find entities by name
        from_entities = self.knowledge.find_entity(from_entity)
        to_entities = self.knowledge.find_entity(to_entity)
    
        if not from_entities:
            return f"Error: Source entity '{from_entity}' not found"
    
        if not to_entities:
            return f"Error: Target entity '{to_entity}' not found"
    
        # If multiple entities with the same name, use the first one
        from_id = from_entities[0].entity_id
        to_id = to_entities[0].entity_id
    
        relation_id = self.knowledge.add_relation(
            from_id=from_id,
            to_id=to_id,
            relation_type=relationship_type,
            metadata=metadata,
        )
    
        if not relation_id:
            return "Error: Failed to create relationship"
    
        # Save knowledge if storage path is set
        if self.storage_path:
            self.knowledge.save_to_file(self.storage_path)
    
        return f"Relationship registered with ID: {relation_id}"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2/5.0
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations exist, and the description does not disclose any behavioral traits such as idempotency, validation, or side effects. It only repeats the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short and front-loaded with the purpose line, but the structure includes a docstring-style 'Args' section that lists parameters without adding value. It could be more concise by focusing on core information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 4 parameters, no output schema, and no annotations, the description is insufficient. It lacks details about return values, error handling, or behavior on duplicate relationships.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The parameter descriptions add no meaningful information beyond parameter names; e.g., 'from_entity: Name of the source entity' is tautological. No constraints, examples, or allowed values are given.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Register a relationship between entities' clearly defines the action and resource. It distinguishes from siblings like 'register_entity' by specifying 'relationship between entities'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines1/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives, nor any prerequisites or contextual conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.