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

Annotations cover the safety profile (readOnlyHint, openWorldHint), but the description adds critical behavior an agent could not infer: missing values return null, the 20-field cap, an output example, and the $0.02 USDC x402 payment requirement passed via _meta. The payment/auth disclosure is exactly the kind of operational context annotations do not carry.

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?

Front-loaded with the verb and purpose, then examples, limits, output shape, and pricing in a compact paragraph. The inline output example is slightly verbose but earns its place by removing ambiguity about the return shape.

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?

With no output schema, the description supplies an example return object; with two required params, it explains both their format and edge-case behavior. Pricing and the payment channel are also disclosed, so an agent has everything needed to call it 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?

Schema coverage is 100%, so the baseline is 3; the description earns above that by giving concrete field-name examples ('fields=name,price,currency,availability') and restating the comma-separated max-20 convention plus null behavior for absent fields.

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?

States a specific verb and resource ('Extract structured JSON from a web page') and immediately clarifies scope ('Fetch a public web page and extract the fields you name'). An agent can distinguish this from ai_summarize or the chain-query siblings without opening the schema.

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 establishes clear context — public http(s) pages, up to 20 named fields — which tells the agent when the tool applies. It stops short of explicitly naming an alternative (e.g., ai_summarize for prose) or stating exclusions, so it lands at 4 rather than 5.

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