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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses payment cost, evidence sourcing, classification behavior, contradiction handling, confidence, provenance, locators, and explicit uncertainty. This adds rich behavioral context that the annotations alone do not provide, with no contradiction.

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 dense and front-loaded with the most important context, including cost and the core deliverable. The long feature list is somewhat enumerative, but each listed capability adds decision-relevant information for an agent. It is appropriately sized for a complex tool, though not maximally concise.

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 a rich output schema, complete parameter schema, and detailed annotations, the description fills in the remaining context: when to use the tool, what distinguishes it from simpler lookups, and what behavioral guarantees to expect (evidence, confidence, uncertainty). Nothing essential for correct invocation is missing.

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% and the query parameter is well-described in the schema, so the baseline is 3. The tool description adds meaning by characterizing the query as 'difficult open-ended' and excluding 'simple learning or single-proposition adjudication,' which clarifies acceptable inputs beyond the schema's minimal description.

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 action: 'Get a compact, source-backed answer to a difficult natural-language Bitcoin question' using an evidence system. It also differentiates from siblings by contrasting with 'retrieve topics or search results' and signaling a specialist, evidence-grounded role rather than a simple lookup. This is a specific verb+resource with clear scope.

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 explicitly says when to use the tool: 'Use for difficult open-ended Bitcoin questions requiring compact evidence synthesis,' and when not to use it: 'Do not use for simple learning or single-proposition adjudication.' It does not name an alternative sibling tool explicitly, but the exclusions are clear enough to guide selection.

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