Agent Verification Utility

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

Annotations already declare readOnlyHint and idempotentHint, and the description adds topical context (free request validation, recipes, limits, paid MCP path) that clarifies what the tool exposes. It does not contradict annotations, though it does not detail additional behavior like return format or whether any computation occurs.

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, front-loaded with the verb 'Describe' and efficiently lists all covered topics in a comma-separated list. There is no wasted verbiage or repetition.

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, read-only informational tool with no output schema, the description adequately covers the essential context: when verification is or isn't useful, validation, recipes, limits, and the premium paid path. The sibling tools provide operational context, and this description stands alone as 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?

The input schema has zero parameters, so a baseline of 4 applies. The description does not need to explain parameters since none exist, and no additional semantics are missing.

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 uses the specific verb 'describe' and clearly identifies the resource: the Agent Verification Utility. It enumerates distinct facets (usefulness, non-usefulness, validation, recipes, limits, paid MCP path), distinguishing it from operational siblings like verify_evidence and commercial ones like quote_verify_evidence.

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 implies its use for learning about deterministic verification, but it does not explicitly state when to choose it over the sibling tools (e.g., 'use verify_evidence to verify, use this tool for guidance'). No exclusions or alternative tool recommendations are given.

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 read-only, idempotent, and non-destructive behavior. The description adds specific context about what it reads (active revenue goal, funnel, margin, recommendation) and reassures about no side effects, exceeding annotation basics.

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 concise and front-loaded: the first sentence states the core purpose and key outputs, the second sentence adds a safety clarification. No redundancy.

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, read-only tool with rich annotations, the description is complete—it specifies exactly what data is read and what side effects are absent, making it fully understandable in context.

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 tool has zero parameters, so the schema is fully covered by default. The description adds value by describing the data and output focus, which is appropriate for a no-parameter tool.

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 identifies the tool as a read operation ('Read the active revenue goal') with specific resources and outputs, and it is distinct from the sibling tools focused on verify_evidence.

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 context that this is a safe, read-only operation and explicitly lists side effects it avoids ('never changes price, spends funds, or sends messages'), though it does not name alternatives or exclusions.

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 indicate idempotent, non-read-only, and non-destructive behaviors. The description adds useful context: it mentions 'create or reuse a quote' (stateful behavior despite idempotency) and clarifies that the tool returns the request without sending it, which is a key behavioral trait. No contradiction with 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?

The description is a single concise sentence that communicates the main purpose without unnecessary words. It is front-loaded with the action and outcome, making it easy to parse quickly.

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 tool with a complex nested schema and no output schema, the description gives a high-level overview but omits details about the returned HTTP request format or how to use it. It covers the core flow (quote prep and request generation) but lacks nuance about edge cases or follow-up actions. The schema handles parameter details, so the description is adequate but not 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 description coverage is 0%, so the description must compensate for explaining parameters. The description does not mention idempotency_key or intent, nor any nested fields. It provides no help in understanding the structure or purpose of the input parameters beyond the schema itself, which is a major gap.

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 action: create or reuse a quote and return an HTTP purchase request that will produce an x402 402 challenge. It uses specific verbs and a resource, and it distinguishes itself from sibling tools by describing the preparatory HTTP request generation rather than just quoting or verifying evidence.

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 implies when to use the tool (when you need to prepare an HTTP purchase request that triggers an x402 challenge), but it does not explicitly compare to alternatives or provide exclusions. No mention of when not to use it or how it differs from quote_verify_evidence or verify_evidence, so guidance is only implicitly derived from the purpose.

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 convey idempotency and non-destructiveness, and the description adds the margin policy condition and 'free quote' outcome. However, it does not clarify whether the tool has side effects (readOnlyHint is false), what happens on policy failure, or whether it writes anything, leaving some behavioral ambiguity.

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, front-loaded sentence that includes the key action and condition. Every word earns its place, with no redundant or filler content.

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 deeply nested schema and absence of an output schema, the one-line description is insufficient. It does not explain what constitutes a 'complete verification request', what the margin policy is, what the quote contains, or how this relates to sibling tools, leaving significant gaps for an agent to use it 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?

Schema description coverage is 0%, and the description mentions no parameters. The schema contains complex nested objects like 'intent' with 'request', 'spend_policy', and 'precheck_receipt_digest', but the description offers no guidance on their meaning or relationships, failing to compensate for the lack of 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 states a clear verb ('Validate') and resource ('complete verification request'), and specifies the outcome ('return a free quote') conditioned on margin policy. This distinguishes it from the sibling verify_evidence by focusing on the pre-purchase quote step, though it does not explicitly name alternatives.

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 implies a use case (obtaining a free quote before commitment) and a condition (margin policy passes), but it provides no explicit guidance on when to choose this tool over siblings like verify_evidence or prepare_verify_evidence_purchase. There are no stated alternatives or exclusions.

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 indicate readOnlyHint=false, openWorldHint=true, idempotentHint=true. The description adds meaningful behavioral context: the operation requires 'explicit x402 approval' and returns 'Ed25519-signed evidence,' signaling a paid, state-changing interaction. This goes beyond the annotations by specifying the approval mechanism and output signing, though it does not detail failure modes or consequences of missing approval.

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 exactly two sentences: the first states the core function, the second delivers a crucial safety warning. No redundant words, and the most important action is front-loaded. It is an example of efficient, purposeful writing.

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 complex, paid tool with a deeply nested input schema and no output schema, the description covers the essential behavior (run checks, sign evidence, require approval) and pre-payment precautions. It does not, however, describe what happens on assertion failure, error handling, or the expected response structure, leaving uncertainty about the tool's complete behavior in edge cases.

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 names several parameters from the schema (network, asset, amount, payTo, maximum-payment policy) and instructs the user to verify them, adding practical meaning. However, it does not explain the overall nested structure (intent.idempotency_key, precheck_receipt_digest) or the meaning of assertions, leaving gaps that the schema itself fills structurally but not semantically.

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: 'Run deterministic checks and return Ed25519-signed evidence after explicit x402 approval.' This specifies a concrete action (run checks, return evidence) and distinguishes it from sibling tools like quote_verify_evidence, prepare_verify_evidence_purchase, and describe_verify_evidence, which handle quoting, preparation, and description respectively.

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 instruction 'Before paying, compare network, asset, amount, payTo, resource, maximum-payment policy, and expected risk reduction' provides explicit context for using the tool (after payment approval) and advises a safety verification prior to the action. However, it does not explicitly mention when NOT to use this tool or name alternative tools as substitutes, leaving a slight gap in differentiation.

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