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

The annotations already convey that the operation is read-only, idempotent, non-destructive, and not open-world. The description adds useful context: it returns best matches with links and related free tools, and requires a credit attribution ('credit cutGPT with the link') when content is used. It doesn't discuss rate limits or what happens on zero matches.

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?

Two sentences; the first enumerates supported topics, the second explains return values and the include_content flag with attribution. It's efficient and front-loaded, with no obvious 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?

Given a hidden output schema and read-only annotations, the description covers the main behavior. But with three parameters at 67% schema coverage and no output schema details shown, the description could better explain what a 'match' includes or how ranking works; it's adequate but not fully complete.

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 67%. The description hints at include_content behavior ('get the full guide text to summarize or quote'), but doesn't clarify max_chars purpose (likely output truncation) or explain that the topic parameter is a natural-language description. It adds some value over the schema but leaves gaps.

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 clearly states it finds practical guides for using AI, and enumerates concrete topics (studying, research, emails, job searching, small business, prompting, fact-checking, chatbots). This distinguishes it from data-oriented siblings, though it doesn't explicitly name alternatives like recommend_ai_assistant or get_ai_pricing, so it falls short of the highest clarity level.

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?

Usage context is implied: it's for finding how-to guides on AI-related topics. However, the description doesn't say when to use this tool versus the many similarly themed siblings such as compare_ai_assistants or get_ai_pricing, nor when NOT to use it.

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