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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive). The description adds genuine context beyond them: the return shape (confidence flag plus candidate organisations) and the pricing/billing behavior (free within daily allowance, then $0.001 USDC via x402). That billing detail is material and not encoded anywhere else.

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 sentences, zero waste, and front-loaded: the core operation comes first, then the behavioral/billing addendum. Every sentence earns its place.

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?

An output schema exists, so return values need not be spelled out, and the description still summarizes them. For a one-parameter, read-only lookup, the description plus annotations plus output schema cover everything an agent needs to invoke it correctly, including cost.

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% with a single parameter, so the schema already documents 'affiliation'. The description slightly enriches it by clarifying the expected format is a free-text string as printed on a paper, but this is marginal beyond the schema. 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?

States a specific verb+resource: converting a free-text affiliation into the matching organisation. This is clearly distinguishable from siblings like ror__search (query-based lookup) or ror__organisation (ID lookup) because it names the exact input ('as printed on a paper') and output (match with confidence flag and candidates).

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 'as printed on a paper' implies the use case (resolving raw affiliation strings from publications), but no alternatives are named and no explicit when-not guidance is given against ror__search or ror__organisation. Usage context is inferable but not stated.

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