zFinia Intelligence

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

With no annotations provided, the description carries the full burden. It discloses that the tool computes WCAG 2.x sRGB contrast and explicitly states it makes no interface-wide claim, which is a scope disclaimer. However, it does not describe error behavior, handling of invalid inputs (though schema enforces pattern), or what the output represents (e.g., ratio vs pass/fail). The addition of the WCAG standard and scope disclaimer adds some value beyond the name, but significant behavioral details remain undisclosed.

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 a single sentence that front-loads the verb and resource, then adds the scope disclaimer. Every word earns its place—there is no fluff, and the key differentiator (single pair vs interface-wide) is included. It is optimally 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?

Although an output schema exists (so return format is covered), the description fails to explain the purpose of the large_text parameter or clarify what the computed contrast value represents. An agent might infer a ratio, but the boolean's role is ambiguous. For a tool with only three parameters, this omission is a meaningful gap.

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 0%, so the description must compensate. It does not mention any parameters by name or explain their meaning. While foreground and background are self-explanatory hex colors, the boolean large_text is left unexplained; it likely affects WCAG thresholds, but the description says nothing about it. The description adds no value beyond what the schema pattern already provides for colors.

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 specific action (calculate WCAG 2.x sRGB contrast) and the resource (a caller-supplied color pair). It also distinguishes itself from interface-wide accessibility claims, which separates it from potential broader audit tools. The verb and resource are explicit and unambiguous.

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?

There is no explicit guidance on when to use this tool versus alternatives. The description implies it is for a single pair, but does not state when not to use it or point to a sibling for broader checks. Since siblings are unrelated, it's not a critical gap, but the lack of any contextual 'when to use' instruction lowers the score.

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?

With no annotations provided, the description carries the full burden. It discloses deterministic behavior, stable outputs, and the fact that it performs no data fetching or API validation. It does not mention whether it modifies anything, but the term 'gate' implies a read-only check. This covers key behavioral traits sufficiently.

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 a single sentence that is front-loaded with the primary purpose and efficiently conveys the essential scope. It avoids unnecessary words and is well-structured for quick comprehension.

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 tool has a moderately complex schema with nested objects and enums, but the description omits details about the comparison modes, canonicalization options, contract version, and the output format (only broadly mentions 'incompatibility codes and remediation actions'). This leaves significant gaps for an agent to understand how to properly invoke the tool.

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 description does not explain any of the five parameters. It mentions 'field contracts' generically but does not define source_fields, target_fields, comparison_mode, canonicalization, or contract_version. Since the schema also lacks descriptions, the parameter meanings are entirely unexplained, providing no added value.

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 purpose with a specific verb ('gate') and specifies the context (ingestion, migration, CI deployment, machine-to-machine handoff). It also distinguishes itself by noting it does not fetch data or validate API behavior, making it distinct from sibling 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?

The description provides usage context by listing the scenarios (before ingestion, migration, CI deployment, machine-to-machine handoff) and explicitly states what it does not do (fetch data, validate API behavior), which helps an agent decide when to use this tool. However, it does not explicitly compare with alternatives or provide conditions for choosing this over the siblings.

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 that this is a paid operation ('Pay 10000 atomic USDC on Base via exact x402') and explains the effect of include_high_resolution. It does not explicitly state whether the operation is read-only or has side effects, but the delta/polling nature implies a read-style operation.

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 compact and well-structured, consisting of three sentences that cover the trigger, the return behavior, and the payment requirement. It avoids redundancy and front-loads the key usage condition.

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 has four parameters and a payment requirement, the description provides sufficient context for an agent to know when to call it, what to pass, and what to expect (delta items, nextCursor, possible empty items). The output schema is present, so no further detail is needed.

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 schema descriptions for since, cursor, and include_high_resolution add meaning beyond the raw type definitions, and the tool-level description clarifies that cursor or since must be supplied. maxItems lacks a description, but its min/max constraints are present in the schema, so the gap is minor.

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 states that this tool returns material machine-economy changes since a cursor, and positions it as the delta mechanism for a caller that already has a zFinia cursor. It clearly distinguishes it from siblings like public_catalog_offer_evidence by focusing on 'changes since its last cursor'.

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 instructs to supply either cursor or since, and tells the caller to pass the nextCursor back on the next poll. It also notes that empty items is a valid result, giving concrete guidance on how to interpret the response.

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?

With no annotations, the description carries the full burden. It discloses that the tool does not fetch URLs, execute a browser, or assert price-currentness, and it indicates the output includes SHA-256 provenance. This is transparent about what the tool does and does not do, though side effects or error behavior are not mentioned.

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 a single, dense sentence without redundant words. It efficiently packs key information about input, output, and non-goals. While slightly long, every phrase adds value, making it well-structured for an API description.

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 description covers the essential points: input requirements, output nature (normalized evidence with SHA-256 provenance), and limitations. It does not detail error handling or exact output schema, but the core functionality is sufficiently complete for an agent to use 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?

The schema provides only types and formats for source_url and html, with no descriptions. The description adds meaning by indicating html is product-page HTML and mentioning authorization, but it does not explicitly define each parameter's role (e.g., source_url as the origin for provenance). It partially compensates for missing schema descriptions.

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 function: converting caller-supplied HTML with schema.org Product JSON-LD into normalized Product/Offer evidence with SHA-256 provenance. It distinguishes itself from siblings by specifying the input type and output format, making its purpose unambiguous.

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 provides usage context by specifying that HTML must be caller-supplied, authorized, and public, and it explicitly states non-goals (no URL fetching, no browser execution, no price-currentness claim). This helps an agent decide when to use this tool, though it could be more explicit about alternatives.

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