Licence audit, page read and endpoint check - paid over x402 (AI-operated)

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

No annotations are provided, so the description must carry behavioral transparency. It does: it discloses the paid nature, the 402 response when unpaid, the reproducible-evidence framing, and that an AI operates the endpoint. It stops short of covering failure modes beyond non-payment, but the key side effects and expectations are disclosed.

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 information-dense and front-loads the most important operational fact (payment), then the action, return values, and failure behavior. It is efficient and well-ordered, though the final sentence about AI operation is minor extra context.

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 no output schema and no annotations, the description compensates by listing return fields and the unpaid HTTP 402 behavior. It covers the main invocation context well, though it omits potential edge behaviors such as redirects, timeouts, or unsupported HTTP methods.

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 both url and method described in the input schema. The description adds little beyond the schema for parameters, but it does clarify what the endpoint check returns. The schema already carries the parametric meaning, so the description provides no significant additional semantics.

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?

Clearly identifies the action (checks an HTTPS endpoint) and enumerates the concrete outputs returned (status code, elapsed time, content type, byte count, JSON-parse result, body head). This is sufficiently specific to distinguish it from siblings like page_read_full or licence_audit_full.

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 makes the basic use case clear and surfaces the payment requirement, but it does not explicitly state when to prefer this tool over sibling tools or when not to use it. Usage context is implied rather than prescribed.

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 fully carries the behavioral burden. It transparently discloses that it 'Reads manifests and public registry metadata only - it never clones, builds or executes anything,' and notes it does not scan for vulnerabilities. It also explains the payment requirement (HTTP 402, x402 terms) and that nothing is charged until payment is presented, offering complete transparency about side effects and limitations.

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 well-structured and front-loaded with the core purpose, followed by output details, limitations, and payment information. Each sentence contributes value without redundancy, though it is slightly long due to the inclusion of payment and operational details. Still, it remains focused and efficient.

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 is comprehensive for a tool with three parameters and no output schema. It explains the exact outputs (license, registry document, obligation findings, SBOM), the read-only nature, the payment flow, and what it does not do. Given the tool's complexity, everything an agent needs to know to invoke it correctly is covered.

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 each parameter already has a clear description (e.g., manifest_url specifies supported formats, distribution explains its role in copyleft obligations). The tool description does not add additional parameter semantics beyond what the schema provides; it focuses on behavior and outputs rather than parameter details. Thus, the baseline of 3 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 the tool's purpose with a specific verb and resource: it 'Audits EVERY package in a public dependency manifest' and lists the exact outputs (license, registry document, obligation findings, CycloneDX SBOM). It also distinguishes itself from siblings by focusing on license auditing rather than endpoint checks or page reads, making the scope unmistakable.

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 clear usage context: it is for auditing licenses in public dependency manifests, and it explicitly states what it does not do (no cloning, building, executing, or vulnerability scanning), which helps an agent decide when to use it. However, it does not mention alternative tools or conditions for choosing this over a different tool, though the sibling tools are unrelated in domain.

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 and discloses the essential behaviors: paid operation ('0.02 USDC over x402 on Base'), the failure mode ('HTTP 402 with the exact x402 terms'), non-execution of JavaScript, and the exact contents returned. This is unusually explicit for a read tool.

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?

Three sentences, each carrying distinct information: cost, operational behavior, and non-payment consequence. The payment warning is front-loaded, and there is no redundant phrasing.

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 single-parameter tool, the description covers the key return values, the payment prerequisite, and a prominent error case. With no output schema, explicitly naming the returned content types is sufficient for an agent to know 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 schema already fully documents the only parameter ('url') with 100% coverage, so the baseline applies. The description adds context that the target is a public page but no additional syntax, constraints, or format details beyond the schema.

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 names a specific action ('Reads one public web page...') and details return types ('cleaned text, the JSON-LD blocks and every outbound link'), making its purpose unmistakable. It does not explicitly differentiate itself from siblings endpoint_check_full or licence_audit_full, though the content scope strongly implies it.

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 'without executing JavaScript' implies the tool is for static page extraction rather than rendered-page checking, but there is no explicit when-to-use or when-not-to-use guidance. Sibling tools are not referenced as alternatives, leaving the choice partly to inference.

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