netintel_semantic_rank

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

With no annotations, the description carries the full behavioral burden. It discloses that it returns ranked candidates with scores and that candidates are limited to up to 100, but it omits scoring range, error behavior, and whether top_k or min_score filtering changes the returned set.

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 text is front-loaded with the core operation and remains short. The value-proposition sentence is somewhat promotional and the trailing ellipsis is odd, but the description is still efficient.

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 five-parameter tool with no annotations and no output schema, the description is incomplete. It gives a basic input and output sketch but does not document model selection, ranking controls, or the exact shape of the ranked results.

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% across five parameters. The description covers only query and candidates (including the 100-candidate cap) and leaves model, top_k, and min_score completely unexplained, so it does not compensate for the missing schema documentation.

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 states a specific operation: take a query and candidate texts, return them ranked by semantic similarity with scores. It distinguishes itself from embedding-based workflows by saying 'no vectors, no cosine math, no embedding model to manage,' but it does not explicitly name or contrast with sibling tools such as netintel_embeddings.

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 explains what the tool does but gives no guidance on when to use it versus alternatives. It does not mention scenarios, exclusions, or named sibling tools that an agent should compare against.

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