booklint

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

Goes well beyond the annotations: the keyless free plan has a daily cap, missing data is 'not checked, never assumed', 'a result with zero flags is not a pass', the verdict vocabulary is disclosed, and it states nothing is executed and that it will not fetch URLs. This is exactly the kind of behavioral framing readOnlyHint alone cannot convey.

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 purpose and well organized around the three input modes, and nearly every sentence carries information. It is a dense wall of text with some overlap between the sample guidance and the description body, so it is not maximally tight.

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 an 11-parameter, nested-object, no-output-schema tool, the description compensates by explaining the return shape (verdict, coverage, flags and per-rule passed/failed/not-checked statuses). An agent has enough to call it correctly and interpret the result.

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%, so baseline is 3, but the description adds real semantics: what each input shape means, which offer fields are evaluated (mandatory fees over headline price, free-cancellation window, auto-renewal, renewal price, trial length), that page_text must be text you already have rather than a URL, and that as_of is used instead of the server clock.

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 ('deterministic rule checks of any agent's actions against the limits its owner wrote') and ties itself to a known endpoint (POST /v1/check). It also enumerates the three distinct input modes (book, actions+limits, offer+limits), so an agent can tell exactly what this tool does without reading 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?

Gives explicit when-to-use guidance per mode: send a book or an actions list for post-hoc checks, or 'BEFORE booking or subscribing' send one offer for the Fine-Print Check. It names the alternative path (pass sample=... to see a full result) and the fallback behavior without api_key, so selection reasoning is complete.

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

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

Annotations already declare readOnlyHint, idempotentHint and openWorldHint=false, so safety is covered; the description adds meaningful behavioral context by disclosing the concrete contents of each sample and that data is 'stamped now' (timestamped at request time). It does not describe how check_book consumes the edited payload or any size/pagination concerns.

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 core statement and the sibling hand-off, then enumerates samples efficiently. It is dense with quoted literals and slightly long, but every clause carries information about a distinct sample.

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?

There is no output schema, and the description partially compensates by describing the shape of the returned data per sample. Combined with the annotations covering the read-only/idempotent profile, an agent has enough to call it correctly, though a brief note on the response envelope would make it fully complete.

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 already 100% with an enum, but the description goes well beyond the schema by explaining what each value actually contains (trading = snapshot/rulebook/broker/journal; spend = actions and owner-written limits; booking/subscription = offer, limits, as_of, page text). This semantic mapping is exactly the value the schema cannot carry.

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 ('Returns a public sample, the same as GET /v1/sample') and immediately disambiguates from the only sibling by naming it: 'Edit it and pass it to check_book.' An agent can distinguish this from check_book without opening either 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?

Clearly implies the use case (fetching seeded sample data, then editing it and feeding it to check_book for Fine-Print Check requests on booking/subscription), naming the downstream alternative. It lacks an explicit 'when not to use this' statement or default-name guidance beyond '(the default)', so it stops short of a 5.

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