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get_entity_edge

Retrieve a specific relationship connection from graph memory using its unique identifier to access source, target, and relationship details.

Instructions

Get an entity edge from the graph memory by its UUID.

Args:
    uuid: UUID of the entity edge to retrieve

Returns:
    Entity edge dictionary containing edge details:
    {
        "uuid": "edge-uuid",
        "source_node_uuid": "source-uuid",
        "target_node_uuid": "target-uuid",
        "fact": "relationship description",
        "episodes": ["episode-uuid-1", "episode-uuid-2"],
        "valid_at": "2025-01-01T00:00:00Z",
        "invalid_at": null
    }

Example:
    get_entity_edge(uuid="edge-uuid-123")

@REQ: REQ-graphiti-chunk-mcp
@BP: BP-graphiti-chunk-mcp
@TASK: TASK-007-MCPTools

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that this is a retrieval operation (implied read-only) and provides the return structure, but does not mention error handling, permissions, or rate limits. It adds some behavioral context (returns a dictionary with specific fields) but lacks comprehensive details for a tool with no annotations.

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

Conciseness4/5

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

The description is well-structured with clear sections (purpose, args, returns, example) and is appropriately sized. However, the inclusion of metadata tags (@REQ, @BP, @TASK) adds minor clutter without contributing to tool understanding, slightly reducing efficiency.

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

Completeness5/5

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

Given the tool's low complexity (1 parameter), no annotations, but with an output schema (implied by the detailed return example), the description is complete. It explains the purpose, parameter, return values, and provides an example, covering all necessary aspects without redundancy.

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

Parameters5/5

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

The schema description coverage is 0%, so the description must compensate. It explicitly defines the single parameter 'uuid' as 'UUID of the entity edge to retrieve', adding crucial meaning beyond the schema's bare 'string' type. This fully compensates for the lack of schema descriptions.

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

Purpose5/5

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

The description clearly states the specific action ('Get') and resource ('entity edge from the graph memory'), and distinguishes it from siblings like 'delete_entity_edge' or 'search_memory_facts' by focusing on retrieval by UUID. It provides a precise verb+resource combination that is unambiguous.

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

Usage Guidelines4/5

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

The description implies usage by specifying retrieval by UUID, which distinguishes it from search-based siblings like 'search_memory_facts' or 'search_memory_nodes'. However, it does not explicitly state when not to use it or name alternatives, leaving some guidance implicit rather than explicit.

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

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