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

No annotations are provided, so the description carries the full burden. It discloses that output is structured data (not prose), includes source and check date for each row, and lists the attributes compared. This gives agents a clear behavioral model. However, it does not mention whether the tool caches data, has rate limits, or how recent the check date is, which would be helpful for a comparison tool.

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 core purpose, then provides specific examples and use cases. It is concise at about 4 sentences, but could be slightly more streamlined by moving the category examples to the schema or a separate note. Every sentence adds value, but the long list of categories in the middle might be better as a structured list.

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 the tool has 2 parameters, no output schema, and no annotations, the description is fairly complete. It explains the output format (structured data, rows with source and date), the categories, and the attributes compared. However, for a comparison tool, it would be helpful to know if the output is a table or JSON, and how missing data is handled (e.g., if a vendor doesn't publish a figure). The sibling tools like 'explain_missing_figure' suggest that handling missing data is relevant.

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 both parameters having enum descriptions. The description adds value beyond the schema by explaining what each category means (e.g., 'chat (where the wall is - only 1 of 10 vendors publishes a message count)') and giving examples of the data structure. However, the 'lang' parameter is not elaborated in the description beyond the enum, so the value add is marginal for that parameter.

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 the tool compares free tiers of AI tools side by side with structured data, listing specific attributes (grants, metering, reset, unmetered items, publication status). It distinguishes itself from siblings like 'compare_free_tiers' vs 'check_free_tier_claim' or 'find_free_alternatives' by emphasizing structured comparison. The verb 'compare' and resource 'free tiers' are specific and unambiguous.

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 when-to-use guidance: 'Use this for "which free tier should I pick" and "how do these compare" questions.' It also lists example categories (chat, coding, etc.) implying context. However, it does not explicitly state when NOT to use this tool versus alternatives like 'check_free_tier_claim' or 'get_free_tier_limit', leaving some ambiguity for edge cases.

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