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

No annotations are provided, so the description carries the full burden of behavioral disclosure — and it delivers richly. It states 'Read-only, no side effects,' discloses that results are never paid placement and there is no billing, warns that the ordering score may differ from the fni_score field returned, notes the result set is bounded, and documents a retryable transient 503 with retry guidance according to Retry-After. This goes well beyond a minimal safety disclosure.

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 dense and every sentence earns its place — disambiguation, safety profile, retry behavior, and alternative routing are all necessary context. It is appropriately front-loaded with the core purpose statement. It is on the long side, but given how many wrong assumptions it prevents (paid placement, inference routing, equivalent to search), the length is justified rather than wasteful.

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 there is no output schema, the description still covers the essential calling context: what results contain (catalog entries), the fni_score field semantic, the bounded result set, the limit/query/task semantic, retry behavior, and mutation safety. An agent has everything it needs to decide whether to invoke this tool and how to handle the response.

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 the baseline starts at 3 since the schema already documents all three parameters. The description adds value beyond the schema by explaining the mechanics of how task and query interact ('task text folded into the query') and how the parameters relate to the returned scoring field ('The score used for ordering may differ from the fni_score field'). It compensates fine for the low parameter-name ambiguity.

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 opening sentence names a specific verb and resource: 'Keyword-search AI entities using the task/query text as input and return matching catalog entries.' It then actively distinguishes itself from the sibling free2aitools_search ('Mechanically this is the same keyword search... with the task text folded into the query') and states what it is NOT (not task-fit recommendation, not an inference router). An agent can tell exactly what this tool does without opening the schema.

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 provides an explicit USE WHEN condition ('USE WHEN you have task text and want catalog entries ordered by that relevance score'), names the alternatives that should be selected instead ('Use free2aitools_search for plain keyword discovery, or free2aitools_select_model to apply hardware/license metadata filters'), and lists exclusions. This is complete routing guidance with nothing left to inference.

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