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

Annotations cover the safety surface (readOnlyHint=false, openWorldHint=true, idempotentHint=false, destructiveHint=false), and the description adds a critical trait annotations cannot express: it is PAID (1.00 USD via MPP). It also discloses output character (provenance, exact locators, explicit uncertainty). It does not, however, explain the payment mechanics or what triggers the non-read-only/destructive=false classification, so it stops short of full disclosure.

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?

Cost and core action are front-loaded, and the usage/exclusion sentences earn their place. The nine-item enumeration of output attributes (consensus-vs-policy, locators, provenance, uncertainty...) is largely duplicated by the output schema, so it is slightly over-long.

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 one-parameter tool with an output schema and full annotation coverage, the description supplies everything needed to select and invoke it correctly, including cost and sibling exclusions. Minor gap: no note on latency, payment retry/idempotency behaviour, or question-length handling.

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?

With a single required parameter at 100% schema description coverage, the schema already carries the semantics ('Difficult natural-language Bitcoin question requiring an evidence-grade specialist answer'). The description's framing of the question as evidence-grade adds marginal value but no syntax, length or format guidance beyond the schema. Baseline 3 is appropriate.

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+resource ('Get a compact, source-backed answer to a difficult natural-language Bitcoin question') and explicitly contrasts itself with retrieval siblings ('rather than only retrieve topics or search results'). The agent can separate it from answer_bitcoin_question, get_bitcoin_topic and search_bitcoin_knowledge without opening a 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?

Gives both a positive trigger ('Use for difficult open-ended Bitcoin questions requiring compact evidence synthesis') and explicit exclusions ('Do not use for simple learning or single-proposition adjudication'), which route agents away from the simpler answer tool and the claim-verification tool. Nothing is left to inference.

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