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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and closed-world, so the safety profile is covered. The description contributes the catalog scope (563 Merkle-committed entities), but is silent on behavior an agent would want: what an omitted query returns, result limits/pagination, and match semantics.

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?

A single sentence, front-loaded with the verb and resource, with a useful scale/count detail and no filler. Nothing extraneous.

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 one-parameter, annotation-covered search tool this covers the minimum, but with no output schema the agent gets no sense of what a hit looks like or how many come back, and the optional-query behavior is left unexplained.

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% and the single 'query' param already carries examples (gpt-6.1, claude, gemini...). The description only restates 'by keyword' and notably does not explain the consequence of the zero required parameters — i.e. what an empty query returns.

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 ('Search and filter') plus a well-scoped resource: 'all 563 Merkle-committed LLM API and cloud infrastructure entities in the Etuki catalog'. That is concrete enough to distinguish it from list_supported_models, though it never names the sibling explicitly.

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 word 'search ... by keyword' implies discovery usage, but the description never says when to choose this over list_supported_models or etuki_get_audit_evidence, and gives no exclusions or prerequisites. Usage has to be inferred from the name and the sibling set.

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