Memory Suggest Relations

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

Annotations indicate read-only, non-destructive, idempotent behavior. The description adds algorithmic details (dense embeddings with fallback) and confirms no relations are created, aligning perfectly with annotations without contradiction.

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

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

The description consists of two concise, well-structured sentences. The first states the core function and method, the second explains output nature and next steps. No unnecessary words.

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

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

For a suggestion tool with no side effects and an output schema, the description is complete. It covers what the tool does, how it works (embeddings, fallback), what it returns (ranked candidates), and what to do next (use add_relation). No gaps.

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

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

Schema description coverage is 0%, requiring the description to compensate. However, it does not explain any parameters (e.g., id, threshold, max_results, response_format) beyond what the schema already provides. The schema is clear, but the description adds no additional context for parameter usage.

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

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

The description clearly states the tool finds semantically similar memory entries that could be linked to a given memory, specifying the embedding model and fallback mechanism. It distinguishes itself from sibling tools like localnest_memory_add_relation and localnest_memory_related.

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

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

Explicitly states to use this tool for suggestions and then use localnest_memory_add_relation to confirm, providing clear guidance on when to use this tool versus alternatives.

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