search_memories

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

Without annotations, the description carries the full burden. It explains the semantic matching mechanism and relevance ranking, but does not explicitly state if the tool is read-only or describe side effects. However, the search nature implies non-destructiveness, and the description covers key behavioral aspects.

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 is concise (three sentences) and front-loaded with the core purpose, followed by use cases. Every sentence adds value without redundancy.

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?

Given the tool has 8 parameters and no output schema, the description is moderately complete. It explains the main functionality and use cases but does not clarify the return format (e.g., fields in results, sorting). This gap reduces completeness.

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 88%, so baseline is 3. The description adds value by explaining the 'query' parameter's semantic nature but does not enhance understanding of other parameters like 'limit', 'source', or 'min_similarity' beyond what schema already provides.

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 it performs semantic search across short-term memories using Jina embeddings, finding by meaning rather than keywords. It distinguishes from sibling tools like list_memories and recall by emphasizing natural language querying.

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?

The description provides explicit use cases: recalling past decisions, finding context, checking recent discussions. However, it lacks guidance on when not to use this tool versus alternatives, which would improve clarity further.

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