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

Adds substantial behavior beyond the annotations: this call spends account balance ('Cohere models are billed per search (up to 100 documents), the rest per input token'), reports usage.cost_rub, and warns that scores are only comparable within a single response. That billing and score-comparability context is exactly what the readOnlyHint=false / openWorldHint=true annotations cannot convey.

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?

Three short, front-loaded paragraphs: what it does, how it is typically used, then result and cost caveats. Every line carries load and nothing is padded.

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?

With no output schema, the description compensates by explaining the return shape (index into the input documents list, a score per document, best-first ordering, cross-response score comparability). Together with the cost disclosure, an agent has everything needed to call and interpret this correctly.

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 100%, so all four parameters are already documented in the schema, which sets the baseline at 3. The description clarifies result-pointer semantics ('results[].index points into the documents list you sent') but mostly restates the schema's own parameter text rather than adding per-parameter meaning.

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?

States a specific verb and resource ('Order documents by how well they answer a query, best first, with a relevance score each') and anchors it to a concrete API operation. The pipeline sentence (search finds candidates, rerank picks the few) makes its distinct role versus retrieval/embedding siblings unmistakable.

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?

Gives a clear typical-use scenario: 50-100 candidates from vector or keyword search, with rerank narrowing them before they reach the model. It does not name explicit alternatives or when-not-to-use conditions (e.g. versus embeddings_create or models_search), so it stops short of full routing guidance.

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