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. It usefully discloses that matching is semantic rather than literal and that structured filters can be combined to narrow results, but says nothing about result ordering/relevance scores, how filters interact with the semantic ranking, or any limits or failure modes.

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 tight sentences, all front-loaded: what it does, then the example, then the two alternative routes. Every sentence earns its place with no redundancy or filler.

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 9-parameter tool with no output schema and no annotations, the description covers purpose and routing well but omits the shape of the results (ranked list? scores?) and how filtering interacts with semantic ranking. Adequate but with a real gap for an agent deciding how to interpret output.

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%, with all 9 parameters (including 3 enums) documented inline, so the baseline is 3. The description adds only a light example of combining filters (openSource + deploymentModel) and no syntax or semantics beyond the schema.

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 (AI tools) with the distinguishing mechanism (natural language semantic matching). It explicitly separates itself from siblings by naming 'search_listings' for keyword queries and 'compare_listings' for comparisons, so the agent can select correctly without opening a 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?

Provides an explicit when-to-use case ('conceptual queries like "something that automates my email workflow"'), a concrete example, and two named alternatives with the conditions that select them. Nothing about routing 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.