reqs_search_similar

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

With no annotations provided, the description carries the full behavioral disclosure burden and does so thoroughly. It explains that mismatched-width vectors are excluded rather than failing the search, empty/NaN/infinity vectors are refused, the query is not stored, and results are ranked by cosine similarity.

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 longer than average but every section adds necessary behavioral or usage detail, and the Args list provides quick parameter reference. It is front-loaded with the core purpose and organized into digestible paragraphs with clear caveats.

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?

The tool has an output schema, so return-value details are not required. The description covers preconditions, edge cases, failure behavior, and even gives a follow-up tool for diagnostics. For a semantic-search tool with no annotations, this is highly complete.

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%, so the description must compensate. It does so by explaining each parameter: query_embedding is the query vector with model-matching and validity constraints, limit is max results, min_similarity is a cosine threshold, and status is an optional filter. This adds meaningful semantics beyond the bare schema titles.

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 opens with a specific verb and resource: 'Find requirements semantically similar to a query.' It clearly differentiates this semantic-search tool from other requirement operations by emphasizing embedding-based similarity and even references the alternative reqs_embedding_stats for a related diagnostic case.

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 gives explicit usage conditions: pass a query embedding from the same model used for requirements, compute it yourself, and note that no text is sent or stored. It also tells the agent what to do when results are fewer than expected by naming reqs_embedding_stats as the diagnostic follow-up.

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