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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the description wisely adds value by scoping what 'public detail' means (verified or self-reported revenue signals, for-sale status) and the null-return contract. It doesn't add auth, rate-limit, or ordering details, but for a simple read operation with strong annotation coverage, it discloses more than the calibration baseline (which scored 3).

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 tightly packed sentences, each earning its place: what it does, when to call it, and the null edge case. The inline example is efficient, and there is zero fluff or repetition of schema data. This is a model of dense, front-loaded technical writing.

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 single-parameter, read-only tool with an output schema, the description covers the input contract (slug format, source), the output scope (public detail, revenue signals, for-sale status), and the failure mode (null case). No critical context is missing for an agent to use this safely and 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?

Input schema coverage is 100%, so the schema already documents the slug parameter. The description earns credit by adding real-world semantics: slug format via example ('/tools/acme-writer' -> 'acme-writer') and provenance ('as returned by search_tools' toolUrl'). This goes beyond a baseline of 3 by teaching the agent where valid values come from.

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?

"Fetch the full public detail for one AI tool by its listing slug" uses a specific verb+resource and immediately differentiates from the sibling search_tools by emphasizing 'full public detail.' The description also specifies the exact data returned (launch date, revenue signals, for-sale status), leaving no ambiguity about what the tool does.

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 explicitly sequences usage with 'Call this after search_tools,' names search_tools as the source of the slug, and clarifies behavior for invalid slugs ('Returns null if the slug doesn't resolve'). This provides clear when-to-use context and names the alternative tool it works with, effectively covering the when/alternatives criteria.

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