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mcp_engram_relate

Create a directional relationship between two existing memory concepts, storing the edge as a knowledge graph link with a custom label.

Instructions

Create a directional knowledge graph edge between two concepts using VSA OP_BIND. Stores the edge as a ZEDOS_RELATION block linking concept_a →[label]→ concept_b. WHEN TO USE: When you discover a meaningful relationship between two memories — 'depends_on', 'implements', 'contradicts', 'derived_from', 'same_category', etc. This builds a navigable knowledge graph. Use mcp_engram_search_by_relation to traverse it and mcp_engram_visualize to render a Mermaid diagram of the subgraph. Both concepts must already exist in memory before relating them.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
concept_aYesSource concept
concept_bYesTarget concept
labelYesRelation label (e.g. 'depends_on', 'implements')
Behavior4/5

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

No annotations are provided, so the description carries full burden. It discloses the use of VSA OP_BIND, storage as ZEDOS_RELATION block, and the requirement for concepts to exist. However, it does not mention reversibility or idempotency, leaving some behavior undisclosed.

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

Conciseness5/5

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

The description is concise with two sentences plus a usage directive. It front-loads the core purpose and efficiently adds contextual guidance without redundancy.

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

Completeness4/5

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

Given no output schema and no annotations, the description is fairly complete. It explains the action, prerequisites, and links to sibling tools. It could mention error handling, but the core information for correct invocation is present.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining the directionality (concept_a →[label]→ concept_b) and providing example labels, which enhances understanding beyond the schema.

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 tool creates a directional knowledge graph edge between two concepts. It specifies the verb 'Create' and the resource 'directional knowledge graph edge', and distinguishes from siblings by mentioning related tools for traversal and visualization.

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

Usage Guidelines5/5

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

The description explicitly includes a 'WHEN TO USE' section with examples like 'depends_on', 'implements', and gives clear alternatives: mcp_engram_search_by_relation and mcp_engram_visualize. It also states prerequisites: both concepts must already exist.

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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