search_memory

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

With no annotations provided, the description bears the full burden. It discloses the tool reads from vector memory and is embedding-based, but lacks details on side effects (e.g., no write operations implied), performance, or error states. The description is adequate but not exhaustive for a read-only search.

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 about 5 sentences and includes a structured list of example queries. It front-loads the core purpose and usage guidance. While clear and efficient, it could be slightly more concise without losing information; hence 4.

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 3 parameters (1 required) with full schema coverage, a known output schema, and sibling tools provided, the description achieves reasonable completeness. It covers usage timing and query formulation well. However, it briefly mentions the output schema exists but does not reference it, which is acceptable.

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 coverage is 100%, so baseline is 3. The description adds value by providing example queries that illustrate how to use the `query` parameter effectively, going beyond the schema's simple description. This helps the agent formulate better queries, earning a 4.

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 searches local vector memory for semantically similar context, with a specific verb ('search') and resource ('local vector memory'). It distinguishes from siblings (store_memory, wipe_project_memories) by focusing on retrieval. The embedding-based nature is explicitly mentioned, clarifying the tool's approach.

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 explicitly advises calling this tool at the start of a task before reasoning or writing code, providing clear context. It explains that natural-language queries work better than exact terms. However, it does not explicitly mention when not to use it or direct alternatives, though sibling tools are listed separately.

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