scrapecheck

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

Annotations already mark read-only/idempotent; the description adds that it's free, the trust model (offline verification), and the specific returned data. No contradictions, and the added context goes beyond the annotations.

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 with no fluff. It front-loads the key facts and provides actionable guidance, every sentence earning 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?

For a zero-parameter tool with no output schema, the description covers return values and usage context. It lacks exact response structure but is sufficient for an agent to decide and invoke correctly.

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?

There are no parameters to document, and schema coverage is 100%. The description doesn't need to elaborate on parameters, and the baseline for zero params 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?

The description clearly states what the tool does—returns public key, engine version, benchmark summary, and scope—and positions it as a trust prerequisite for offline verification. This distinguishes it from sibling verification tools.

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?

It explicitly instructs to 'Call this first' when selection policy requires evidence, and explains the benefit (no runtime trust). It doesn't mention alternatives or exclusions, but the guidance is concrete and actionable.

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?

The description goes well beyond annotations by disclosing that the tool 'Never returns pass', the possible verdicts ('present, fail, or unverifiable'), and that values like 'a was-price, a different variant's price, a shipping cost, or a tax figure' can satisfy presence. It also states the check_type and payment method. No contradiction with readOnlyHint/openWorldHint is present.

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 efficient and information-dense. Every sentence contributes: purpose, non-return of 'pass', examples, verdict types, usage guidance, alternative tool, scope, and cost. It is front-loaded with the core distinction ('Presence only...') and remains focused despite its length.

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?

The description covers all essential context: what the tool does, when to use it, when not to, behavioral nuances, limitations (server-rendered pages), and pricing. Since an output schema exists, the absence of return-format detail is acceptable. It is fully adequate for an AI agent to select and invoke the tool correctly.

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?

While the schema already provides 100% coverage for parameter descriptions, the tool description adds crucial interpretation for the 'claim' parameter: presence is not correctness, and gives concrete examples of what counts as present. This adds semantic value beyond the schema definitions.

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 performs a presence-only check: 'confirms the value appears on the page, NOT that it is the right answer to what was asked.' It explicitly differentiates from the sibling tool verify_web_field by naming it and stating when to call it instead. The title also reinforces the purpose.

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 gives explicit usage guidance: 'Use it as a cheap screen before a full check, or when you already know what the value means and only need to confirm it still appears on the page.' It also provides a clear exclusion: 'If your decision depends on the value being the right value, call verify_web_field instead.' It even notes scope (server-rendered pages) and cost.

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?

The description discloses many behavioral traits beyond the annotations: it independently re-fetches the page and 'never trusts the fetch you were given', explains the structural guarantee ('the model judge can only veto a pass, never create one'), describes the signed verdict payload (verdict_id, engine digest, offline-verifiable signature), notes that JS-only content returns unverifiable rather than guessing, and even adds cost. This is far richer than the annotations alone.

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 yet every sentence earns its place: purpose, non-retrieval distinction, usage trigger, re-fetch behavior, structural guarantee, verdict details, scope, and pricing. It is front-loaded with the primary purpose and structured logically, making it efficient despite its length.

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?

Given the tool's complexity, the presence of an output schema, and detailed annotations, the description is operationally complete. It covers limitations (JS-only content), what happens with unconfirmed claims, the format and verifiability of verdicts, and the pricing model. An agent has all the information needed to decide when to invoke it and what to expect.

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?

The input schema has 100% coverage for all three parameters, each with descriptive text and examples (e.g., 'What the scraper was asked to extract'). The description only recaps the parameter list ('Takes the URL, the claimed value, and what was asked') without adding new constraints, formats, or semantics beyond the schema. Baseline 3 is appropriate because the schema already carries the full meaning.

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 opens with a precise statement of action and outcome: 'Checks whether a value you did not fetch yourself is actually on the source page right now, and returns a signed pass/fail/unverifiable verdict.' It explicitly disambiguates from retrieval tools ('Not a retrieval tool: it checks a value you already have, it does not find data for you'), which also distinguishes it from sibling tools. This is a clear, specific verb+resource definition.

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 gives an explicit when-to-use scenario: 'Use it when you hold a value that came from somewhere else... and you need to know it is on the source page before you act on it.' It also states when not to use it ('Not a retrieval tool') and references get_verifier_info for signature verification, providing an alternative link. The scope limitation to server-rendered pages further guides appropriate use.

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