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

No annotations are provided, so the description carries the full behavioral burden — and it delivers: ordering semantics (FNI plus term-match), the note that the ordering score may differ from the returned fni_score field, result-set bounding, the 5-factor FNI composition with the caveat that the semantic factor is a query-time baseline with fni_s returned null, read-only/no-side-effects guarantees, absence of paid placement, and retryable 503 behavior with Retry-After guidance.

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 long but every clause earns its place: purpose, ordering behavior, caveats, usage routing, and error semantics are each distinct and non-redundant. It is front-loaded with the core discovery action and scoping before delving into scoring mechanics and exclusions.

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 annotations and no output schema, this description covers everything an agent needs to invoke the tool safely, interpret a non-obvious ordering behavior, and understand the empty/null-return scenarios. The only residual gaps, such as exact return shape details, are minor given 'metadata' is stated and the 3-parameter schema fully documents inputs.

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 is 3 even with no additional parameter info in the description. The description's relevance-ordering semantics enriches understanding of query behavior slightly, and the catalog scope implies some of the type enum values, but it does not add parameter-specific syntax or format guidance beyond what the schema already documents.

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: 'Keyword discovery over the Free2AITools catalog of AI models, datasets, papers, and tools. Returns matching catalog entries (metadata).' This precisely distinguishes it from siblings like free2aitools_compare, free2aitools_explain, and free2aitools_rank, and from free2aitools_select_model, which is explicitly named as the alternative for entity selection.

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?

Provides explicit 'USE WHEN' and 'DO NOT USE' conditions, covering general web search, executing models, generating answers, and routing to inference providers. It also names the exact alternative tool and the condition that selects it: 'Use free2aitools_select_model instead when you have specific hardware or license constraints.' Nothing is left to inference.

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