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

Even though annotations already mark the tool as read-only and non-destructive, the description adds substantial behavioral context: it returns a verified verdict with link-health probes, a 5-signal viability assessment, sentiment, pricing, and alternatives; data comes from an 8,000-tool engine with weekly re-probing; and every answer states its verification time. The 'unknown means not in catalog' caveat is especially important and aligns with the closed-world annotation.

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 front-loaded with the trigger case and then flows logically through output, evidence, data source, freshness, exclusions, and the unknown-result caveat. Every sentence earns its place and there is 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 two-parameter read-only tool with no output schema, the description is complete: it names the verdict values, evidence components, verification freshness, exclusions, and the meaning of 'unknown'. An agent can determine when to use it and what to expect without needing additional documentation.

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 input schema already explains both parameters well: the tool parameter accepts a name or slug and supports arrays up to 20, while response_format has a clear concise/detailed enum. The main description mentions the evidence categories but does not add meaningful parameter-level meaning beyond what the schema already provides.

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 trigger ('asks whether a specific AI tool is alive, dead, shut down, still maintained, safe to adopt, or trustworthy') and lists the verdict types returned, making the core function concrete. It also excludes out-of-scope requests such as historical company research and legal/financial advice. However, it never explicitly contrasts sibling tools like viability_score, market_sentiment, or pricing_reality, which overlap with parts of its evidence bundle.

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 explicit 'Use this when...' and 'Not for...' guidance, so an agent knows when to invoke it and what to avoid. It also clarifies what an 'unknown' result means, preventing a common misinterpretation. Yet it does not name an alternative tool for the excluded cases, so the when-versus-alternative distinction is not fully spelled out.

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