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link_memories

Connect two memories with a relationship type to build a knowledge graph. Use for linking bug fixes to architecture decisions or preferences to patterns.

Instructions

Connect two related memories with a named relationship. Use this to build a knowledge graph — e.g., linking a bug fix to the architecture decision that caused it, or connecting a preference to the pattern it led to.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memory_idYesThe source memory ID
related_memory_idYesThe target memory ID to link to
relationshipYesHow the memories relate: led_to (A caused B), contradicts (A conflicts with B), extends (A builds on B), related (general connection)
Behavior2/5

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

No annotations are provided, so the description carries full burden. It does not disclose behavioral traits like whether links can be overwritten, permission requirements, or side effects. This is a significant gap for a writing tool.

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?

Two sentences, front-loaded with purpose and examples. No wasted words, efficient and clear.

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

Completeness3/5

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

Without output schema, the description does not explain return values or outcomes. It covers the purpose and parameters well but misses details on behavior like bidirectionality or validation.

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

Parameters3/5

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

Schema coverage is 100% with clear parameter descriptions. The description adds marginal value by providing usage context, but the schema already defines the parameters adequately.

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 action (connect two memories) and the resource (memories with a named relationship). It includes specific examples and distinguishes from sibling tools like get_memory_links and consolidate_memories.

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 gives clear use cases and context (build a knowledge graph with examples). It does not explicitly state when not to use it or mention alternatives, but the examples effectively guide usage.

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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