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

With no annotations, the description carries the full load and does disclose two meaningful traits: it abstains when the context lacks the answer (no hallucination) and it is metered at 0.01 USDC per call via x402. It does not elaborate on failure modes such as an unpayable or invalid proof, but the pricing/abstention disclosure is well beyond what structured fields provide.

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 tight sentences with the core behavior front-loaded and the billing note trailing; nothing is redundant or padded.

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 three-parameter tool with a fully documented schema and no output schema, the description supplies the critical non-schema facts: grounding scope and cost. The only remaining gap is a hint at the shape of the returned answer, which is largely inferable.

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% — context, question, and the paymentProof 402 flow are all documented in the schema, including the two-step pay-then-retry pattern. The description adds no parameter-level meaning, so the baseline 3 applies.

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?

Names a specific capability — grounded reading comprehension that answers a question strictly from supplied context — which cleanly separates it from the general-purpose ai_chat sibling. It stops short of naming any sibling explicitly, so the differentiation is implied rather than stated.

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 phrase 'from the supplied context only' implies you should reach for this when you have source text plus a question, rather than for open-ended generation. However, there is no explicit when-not guidance or pointer to alternatives like ai_chat or ai_summarize.

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