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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently reveals that the free version returns a connect-CTA and that a grounded answer requires either a session_token from verify_wallet_ownership or an x402 payment proof. This adds meaningful context beyond the schema about authentication and monetization. However, terms like 'connect-CTA' and 'x402' are not fully explained, and the description does not cover error behavior or rate limits, so it falls short of a 5.

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 concise, with the main purpose front-loaded in the first sentence. The additional details about language, free/payment tiers, and authentication are packed into a single terse line. It is efficient with no filler, but the phrasing is somewhat cryptic and lacks a clear structure (e.g., bullet points or distinct sections), making it slightly less readable than ideal.

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 4 parameters, no output schema, and no annotations, the description should provide a fuller picture. It covers the primary purpose and the authentication gate, but it does not describe the return format or structure of the 'grounded answer,' nor does it explain edge cases like invalid session tokens or payment failures. For a natural language QA tool with monetization, this leaves notable gaps in the user's understanding of what to expect.

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 0%, so the description must compensate. It does add value by explaining 'lang: en / pl' (valid values for the lang parameter), 'session_token (from verify_wallet_ownership)' (source and purpose of the token), and 'x402 payment proof' (format context for the payment parameter). The 'question' parameter is self-explanatory from its name and the tool's purpose. This goes beyond the bare schema, though it omits explicit syntax or format for the payment proof, preventing a 5.

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's function: 'Ask any market question in natural language and get a grounded answer.' This specific verb+resource ('ask market question') distinguishes it from sibling tools that retrieve specific metrics (e.g., get_buzz_score, get_trending). The phrase 'any market question' establishes it as a general-purpose QA tool, clearly differentiating it from the more focused sibling tools.

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 implies usage context by saying 'any market question' and explaining that the free tier returns a connect-CTA while unlocking a grounded answer requires a session_token or payment. However, it does not explicitly state when to use this tool versus the specialized get_* or find_* siblings, nor does it provide exclusions or alternative names. The guidance is helpful for the authentication process but lacks explicit decision-making support.

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