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

With no annotations, the description carries the full behavioral burden. It usefully discloses that results are narrative stories drawn from and linking to primary sources, which sets expectations about return content, but says nothing about read-only status, result counts, pagination, or ranking/recency behavior.

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 sentences, front-loaded with what the tool does, followed by content provenance and usage routing. No filler or repetition.

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 three-parameter, no-annotation, no-output-schema search tool, the description covers purpose and use case but omits result shape details (fields returned, ordering, pagination) that an agent would need to call it confidently.

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 query (substring match), topic, and limit are already documented in the schema. The description adds no syntax, format, or default guidance beyond that, so baseline 3 applies.

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 resource (stories published by The Neural Net), and hints at differentiation from sibling fact-lookup tools with 'rather than raw model facts.' It does not name the alternatives (get_model, search_models) directly, so the distinction is implied rather than explicit.

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?

'Use for narrative context on a release rather than raw model facts' gives a clear selection condition and an implicit counter-case that routes fact queries elsewhere. There are no explicit exclusions or named sibling alternatives, but the when-to-use is clear.

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