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

Annotations only declare a safe, closed-world read; the description goes well beyond that by disclosing what is returned for each tool (verdict, two distinct probe dates, pricing reality, sentiment, editorial verdict), the update cadence (weekly rolling re-probe), and an important interpretive caveat that absence from the catalog is not evidence of death. That caveat is exactly the kind of non-obvious behavior an agent could otherwise misreport to a user.

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?

Front-loads the trigger condition, then the payload, then provenance, then exclusions — a sound order with no wasted clause. The catalog-size and re-probe statistics are slightly promotional, but they do communicate data freshness, so they nearly earn their place.

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 output schema, the description carries the return-value burden itself and does so thoroughly, enumerating each returned signal and the two comparison modes. Combined with the exclusions and the catalog caveat, an agent has everything needed to call this correctly.

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 all three parameters carry their own descriptions, including the response_format enum semantics. The description adds no parameter-level detail (no name/slug format guidance, no note on what 'detailed' costs in tokens), so the 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 and resource ('compare two AI tools head-to-head') and immediately distinguishes itself from siblings by naming the exact conditions it serves ("X vs Y", which-of-two-to-choose). The scope limitation to exactly two tools is unambiguous, so an agent can separate it from check_tool_status and category_landscape without opening any 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 opening trigger sentence tells the agent precisely when to reach for this tool, and the explicit 'Not for:' clause names three excluded cases, routing single-tool checks to check_tool_status and category browsing elsewhere. This is the when/when-not/alternatives trifecta.

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