kevaremesh

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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it is a paid resource with a specific price, it reports measured metrics rather than demand/quality claims, and it is machine-readable. This goes beyond the annotations without contradicting them.

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 front-loaded: it states what the resource is, what it reports, what it does not claim, and the pricing. Every sentence earns its place, and there is no redundant restatement of the title or schema.

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, idempotent data resource with no output schema, the description covers the essential context: purpose, scope, limitations, and cost. It does not describe the exact output format, but with no output schema and a machine-readable benchmark, the description is reasonably complete. A small gap is the lack of detail on how the data is structured or how to interpret the confidence metric.

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 there is no parameter semantics burden on the description. The schema coverage is 100% (vacuously), and the description appropriately focuses on the resource's nature and output rather than parameters. A baseline of 4 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 paid machine-readable benchmark of observed public agent-commerce discovery/payment surfaces, and specifies what it reports (availability, latency, reliability, sample confidence). It also explicitly states what it does not claim (buyer demand or end-tool quality), which distinguishes it from related benchmark/verification tools in the sibling list.

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 states the resource is paid and gives the x402 price, which is essential usage context. It does not explicitly name alternative tools or when-not-to-use conditions, but the benchmark scope is clear enough that an agent can infer when this is appropriate versus other data-feed/verification 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 annotations already establish read-only, idempotent, open-world, and non-destructive behavior, and the description adds meaningful non-obvious traits: it is a paid resource at '$0.002 USD per call,' claims neutrality, uses 'measured public telemetry,' and explicitly states it 'does not provide legal advice or guarantee execution outcomes.' This goes beyond what annotations convey, though it still does not describe response behavior or data freshness.

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 at two sentences, with the core functional scope front-loaded and the cost/legal caveats in a short second sentence. It is not bloated, though the first sentence is somewhat packed and repeats the 'agent-commerce' concept.

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 five optional parameters, no output schema, and no parameter descriptions, the definition is not complete enough to invoke reliably. It tells the agent what broad domain the tool covers and that it is paid, but it omits how parameters combine, what response an agent should expect, and how to select this tool against the many closely named siblings.

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, but it only partially explains the 'mode' enum by listing route, verify, preflight, policy, observe, and benchmark actions. It adds no meaning for 'job,' 'protocol,' 'capability,' or 'min_reliability,' leaving an agent without enough information to populate these optional parameters correctly.

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 resource and action set: 'control intelligence for AI-agent commerce' that 'route[s], verif[ies], preflight[s], appl[ies] technical policy signals, observe[s] and benchmark[s] agent-commerce surfaces.' It is not a tautology, and it conveys a functional scope. However, it does not distinguish itself from the many sibling control-plane and preflight tools, and 'control intelligence' remains somewhat abstract.

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?

No explicit guidance is given about when to prefer this tool over siblings such as execution_safety_preflight, procurement_router, or agent_commerce_benchmark_snapshot. The list of actions and the mode enum imply possible use cases, but the description offers no when-to-use, when-not-to-use, or alternative routing.

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, openWorldHint, idempotentHint, and destructiveHint=false. The description adds useful context beyond those: the operation is evaluative, returns blocking violations and warnings, uses caller-supplied inputs, and is paid at $0.002 per call. There is no contradiction with the annotations, though it omits error/response details.

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 three sentences with no filler. The core behavior is front-loaded, followed by exclusions and pricing. Each sentence adds distinct operational information, making it appropriately concise and well-structured.

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 a nested rules object containing 500 possible items, the description should clarify return shape, rule semantics, and severity handling, but it does not. It also fails to explain how this engine differs from the sibling agent_procurement_policy_engine variants or procurement_policy_control. The description is adequate for a coarse understanding but not complete enough for confident correct invocation.

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, but it only restates that facts and rules are caller-supplied. It does not explain the shape of the facts object, how rule fields map to fact keys, how operators like 'exists' behave, what severity defaults to, or how value interacts with op. The schema's property names and enum provide minimal structure, but semantic guidance is 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 opens with a specific verb and resource: it evaluates caller-supplied facts against caller-supplied rules and returns blocking violations and warnings. It also states an intended domain (agent procurement / fraud risk) and lists excluded domains. However, it does not distinguish this engine from its three identically named agent_procurement_policy_engine siblings, so it just misses full clarity.

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 intended use ('agent procurement / fraud risk') and an explicit do-not-use list ('legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It also adds a practical cost condition. It does not name specific alternative sibling tools or explain when to choose one of the other policy engines, so it is not a 5.

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 disclose readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: the tool is a paid resource with an explicit x402 price of $0.002 USD per call, and it only evaluates caller-supplied facts/rules rather than consulting external registries. 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 three sentences with no filler. The primary action and input/output contract are front-loaded in the first sentence, followed by usage boundaries and cost. Every sentence earns 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?

The description covers the overall contract, cost, and forbidden domains, and annotations cover safety/idempotency. However, with no output schema and no parameter semantics, the agent is left to infer how to format rules and what the returned violations/warnings look like, which is a notable gap for a rule-evaluation tool with nested parameters.

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 `facts` and `rules`. It only says 'caller-supplied facts against explicit caller-supplied rules,' which names the parameters but does not explain how rules should be structured, what operators mean, how severity maps to output, or what the `facts` object should contain. The schema provides structure but the description adds little semantic value for constructing a correct request.

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 verb ('Evaluates'), the exact resource ('caller-supplied facts against explicit caller-supplied rules'), and the outcome ('returns blocking violations and warnings'). It also narrows the intended domain to 'agent procurement / regulatory mismatch' and explicitly excludes legal, identity, sanctions, fraud, contractual, and regulatory adjudication, which helps distinguish it from the many sibling policy engines.

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 states both the intended use case ('Intended for agent procurement / regulatory mismatch') and clear negative boundaries ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name a specific sibling alternative to route to, so it stops short of a full 'use X instead' pattern, but the when/when-not guidance is strong.

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 establish read-only, idempotent, non-destructive behavior; the description adds complementary context by disclosing that the resource is paid at a specific x402 price and that evaluation is limited to caller-supplied facts/rules, plus the output categories of blocking violations and warnings. 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?

Three sentences, each earning its place: purpose, applicability/exclusions, and cost. The main behavior is front-loaded and no information is repeated from the schema or annotations.

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 generic rule-evaluation tool, the description covers domain, exclusions, and return categories, and robust annotations cover safety traits. However, with no output schema and zero parameter documentation, the agent is left without guidance on facts structure, rule composition beyond the schema, or the exact shape/order of returned violations and warnings.

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 only says facts and rules are caller-supplied; it does not explain how to shape the facts object, how field paths are resolved, or how severity maps to output. The rule schema is self-explanatory, but the opaque facts parameter is not compensated for in prose.

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 specific action and resource: 'Evaluates caller-supplied facts against explicit caller-supplied rules and returns blocking violations and warnings.' It further distinguishes this engine from its many policy-engine siblings by scoping it to 'agent procurement / trust unknown' and explicitly excluding legal, identity, sanctions, fraud, contractual, and regulatory adjudication.

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 clearly states the intended context ('agent procurement / trust unknown') and gives a strong when-not-to-use list ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It stops short of naming a specific sibling tool as the alternative, so it falls just below full explicit routing.

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 signal readOnly, openWorld, idempotent, and non-destructive behavior, and the description does not contradict these. It adds genuine extra context: 'deterministic' behavior, a $0.002 per-call x402 price, and explicit non-obligations (no identity credentials, no legal advice, no downstream guarantee). This is meaningful added transparency beyond the structured hints, though it omits response-shape and error behavior.

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 each sentence adds something: purpose/scope, negative limits, and cost. The opening sentence is a dense noun phrase with a long list, but it is front-loaded and contains no filler. Minor redundancy of 'Paid' appears twice, but otherwise remains disciplined for a tool with this much surface area.

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 is complex: 7 modes, 10 mostly undocumented parameters, nested objects, and no output schema, so the description needs to explain mode-specific behavior and return semantics to be complete. It supplies only a high-level domain list, exclusions, and price; it never explains what a 'preflight' or 'verify' mode returns, how parameters combine, or how results should be interpreted. For an agent to correctly invoke this multi-mode endpoint, this is insufficient 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?

Input schema has 0% description coverage and 10 parameters, so the description carries a heavy burden for parameter meaning. It loosely maps the domains ('permission constraints' → constraints/permissions, 'schema compatibility' → schema, 'procurement comparison' → offers), but it does not explain the core verification parameters such as expected, observed, evidence, protocol, or request. This is partial compensation at best.

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 defines the tool as 'deterministic technical assurance' across a list of agent-commerce concerns (trust evidence, permission constraints, outcome/provenance consistency, schema compatibility, procurement comparison), giving a functional category rather than a single specific verb. The broad multi-domain phrasing and overlap with numerous sibling verification/assurance tools make it hard to distinguish without looking at the mode enum. It is clearer than a tautology but not a crisp action statement.

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 the intended use cases through its list of assurance domains and states exclusions ('does not issue identity credentials, provide legal advice, or guarantee downstream outcomes'). It also flags that this is a paid resource, which is useful cost context. However, it names no sibling alternatives and gives no explicit when-to-use/when-not-to-use decision guidance, leaving tool selection inference-heavy.

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 readOnly, idempotent, and non-destructive behavior. The description adds useful context on top: deterministic analysis, the specific classes of findings produced, and the $0.002 per-call cost. No contradiction with annotations exists, though exact response format is not 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?

Three sentences front-load the core function, then add a clear use case, exclusions, and cost. Every sentence earns its place, and there is no repetition of schema or annotation details.

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 gives solid high-level context: what it analyzes, when to use it, what to avoid, and cost. With no output schema and an opaque `steps` array, however, an agent still lacks enough detail about input element structure and the exact return contract to invoke it confidently.

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 sole parameter `steps` has no element schema and 0% schema description coverage, but the description supplies domain meaning by framing it as the steps of a partially completed multi-step transaction and referencing irreversible successful steps and compensating actions. However, it does not specify the exact object shape, status fields, or compensation linkage required inside each step.

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 first sentence states a precise behavior: deterministic coverage analysis for multi-step agent transactions, specifically identifying irreversible successful steps lacking compensating actions and unsafe compensation order. This goes beyond the title and clearly separates it from broader sibling tools like execution_safety_preflight or outcome_assurance_verification.

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 says to use the tool when deciding whether a partially completed multi-step transaction can be safely unwound, and instructs not to use it for unrelated general knowledge or unsupported legal/identity/sanctions/fraud/contractual/regulatory adjudication. It stops short of naming a specific sibling alternative, so it does not reach the top of the scale.

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 provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the baseline safety profile is covered. The description adds meaningful behavioral context beyond these: the tool is paid ($0.002 per call), deterministic, and explicitly a 'technical interoperability signal only' with no downstream execution guarantee. This helps agents decide whether to spend the call.

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?

Four sentences, each adding distinct value: purpose, comparison dimensions, technical-scope caveat, and pricing. The most important facts are front-loaded and there is zero fluff 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 one-parameter tool with no output schema, the description covers the tool's purpose, cost, determinism, and limits, which is good. However, it omits any detail about what the returned matrix looks like, how the surfaces parameter should be shaped, or what happens on invalid input. These gaps matter because there is no output schema to fall back on.

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?

With 0% schema description coverage, the description must carry the parameter meaning. It mentions 'across agent-commerce surfaces' and what the tool compares, implying each surface has declared protocols, capabilities, rails, and fields, but it never defines what a 'surface' actually is (URL? object? ID?) or the expected structure of the array items. This is too vague for an agent to construct a correct request without further guessing.

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 specific verb ('compares') and resource ('compatibility matrix across agent-commerce surfaces'), and enumerates the exact dimensions compared: protocols, capabilities, authentication/payment rails, and input/output fields. This distinguishes it from siblings like assurance_interop or schema_transform_plan, which would address only a subset of these dimensions.

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: assessing technical interoperability across agent-commerce surfaces, and the caveat 'does not guarantee downstream execution' hints it is not for execution safety. However, it never names an alternative tool or gives explicit when-to-use/when-not-to-use guidance, so the agent is left to infer routing from the tool's name and context.

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 read-only, idempotent, non-destructive, and open-world behavior. The description adds valuable behavioral context: the operation is deterministic, it reports stale and missing-time evidence without claiming external truth, and it discloses that the resource is paid with a specific x402 price. This goes beyond the annotations and sets appropriate expectations about the tool's epistemic limits.

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 front-loaded with the core purpose, followed by usage guidance and cost disclosure. Each sentence contributes meaningful information, with only a minor grammatical hiccup ('when determine'). Overall it is appropriately sized and structured for quick parsing.

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 two-parameter tool with no output schema and minimal schema descriptions, the description covers purpose, behavioral semantics, usage boundaries, and cost, which is useful. However, it omits crucial parameter construction details and does not explain the output structure beyond 'reports stale and missing-time evidence.' The tool is adequately scoped for selection but not fully self-sufficient for invocation.

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 0% description coverage, so the description must compensate, but it only gives high-level hints: records contain observed timestamps and maximum-age requirements, and now is a clock reference. It does not define the shape of records, how maximum-age constraints are expressed, or the expected timestamp format for now. An agent would still struggle to construct a valid payload without external knowledge.

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 specific verb and resource: deterministic freshness evaluation for supplied records using observed timestamps, maximum-age requirements, and a clock reference. It clearly goes beyond the title by explaining the mechanism and the nature of the output. This makes the tool's purpose distinct even among a large sibling list with no similarly named freshness checker.

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 says to use this tool when deciding whether data an agent is about to act on is fresh enough for a caller-defined technical SLA. It also gives explicit negative guidance, instructing the agent not to use it for unrelated general knowledge or unsupported legal, identity, sanctions, fraud, contractual, or regulatory adjudication. This gives both clear when-to-use and when-not-to-use boundaries.

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, and the description adds context about the data source (observed telemetry) and a caveat about what the rankings are not. This exceeds the annotation baseline, though it does not disclose return format, data freshness, or whether results are historical or real-time.

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 two sentences, the core purpose is front-loaded, and every phrase adds meaning. The phrase 'free neutral' is slightly ambiguous but not redundant.

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 three undocumented parameters and no output schema, the description is too terse. An agent would not know what 'job' or 'category' mean, nor what the return object looks like. Annotations handle safety, but parameterization and output expectations are missing.

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 indirectly hints at 'variant' by naming reliability and latency, but 'job' and 'category' are completely unexplained, leaving a significant gap for an agent to correctly set all parameters.

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 a specific verb ('ranks') and resource ('endpoints'), and differentiates itself by noting the ranking is not payment or buyer-demand data. However, it does not explicitly contrast with sibling tools like reliability_quality_benchmark_data_feed, so it lacks full sibling differentiation.

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 usage context (ranking endpoints by reliability, latency, confidence) but provides no explicit when-to-use or when-not-to-use guidance. It does not name alternative tools or exclusion conditions, which is a gap given the large family of related sibling tools.

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?

Beyond the annotations (readOnly, idempotent, non-destructive), the description adds useful behavioral context: the computation is 'deterministic', the operation is over 'caller-supplied observations', and it includes pricing information ('x402 price is $0.002 USD per call'), which is important for a paid resource. These details go beyond what annotations convey, though return format and error behavior are not described.

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 three sentences with no filler: the first sentence states the core function, the second scopes intended use and exclusions, and the third gives cost. The most important operational detail is front-loaded, and every sentence earns 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 two-parameter compute tool with safety annotations already covering read-only/idempotent behavior, the description covers purpose, usage restrictions, and cost. However, there is no output schema and the description does not characterize the return value beyond 'descriptive benchmark statistics' – an agent cannot predict the shape or content of the result, which 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?

With schema description coverage at 0%, the description compensates by explaining that the tool works over 'caller-supplied observations' (the observations parameter) and 'explicitly named numeric fields' (the metric_fields parameter), clarifying that the string array should contain names of numeric fields. This adds meaningful semantic guidance that the bare schema does not provide.

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 a specific verb ('Computes') with a precise resource ('deterministic descriptive benchmark statistics over caller-supplied observations and explicitly named numeric fields'), which clearly identifies the tool's function. It also distinguishes the tool's domain ('dispute provenance / quality unknown') from the many sibling benchmark feeds, making it easy for an agent to tell them apart.

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 states when to use the tool ('Intended for dispute provenance / quality unknown') and gives explicit negative guidance ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name a specific alternative sibling tool, so it misses the full 'alternatives' criterion, but the domain scoping is clear enough to guide selection.

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 destructiveHint=false, so the safety profile is covered. The description adds value by stating determinism (consistent with idempotency), the paid nature with specific pricing, and the exclusion list for inappropriate use. 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 concise (three sentences), front-loaded with the core action, then adds usage constraints and cost. Every sentence earns its place without fluff 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?

With no output schema and no parameter descriptions, the description must fully specify how to call the tool. It omits the format of observations, the meaning of metric_fields, the set of computed statistics, and the return shape. An agent would be uncertain about how to populate the required parameters 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 0%, so the description must compensate. It only says 'caller-supplied observations and explicitly named numeric fields,' giving a vague hint that metric_fields are names of numeric fields. It does not explain the structure of observations (array of objects with what properties?), how to specify field names, or what statistics are computed. This is insufficient for an agent to correctly construct parameters.

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 a specific action: computes deterministic descriptive benchmark statistics over caller-supplied observations and numeric fields. It also names the domain (dispute provenance / price unknown) and explicitly excludes legal, identity, sanctions, fraud, contractual, and regulatory adjudication, which distinguishes it from sibling benchmark feeds like outcome_assurance and reliability_quality.

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 gives explicit intended use ('Intended for dispute provenance / price unknown') and explicit exclusions ('Do not use for...'), which tells an agent when not to use it. However, it does not name alternative tools for those excluded use cases, but the exclusion list is sufficient to guide selection among 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?

Annotations already establish read-only, open-world, idempotent, and non-destructive behavior. The description adds meaningful behavioral context: determinism, field-level diff semantics, the specific diff categories reported, hashes, required-path checks, and importantly what the tool does NOT prove (external truth, legal correctness, fraud, causal fault). It also discloses that it is a paid resource with a concrete price.

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 filler. The first sentence front-loads the core behavior and output categories, and the second efficiently adds pricing and critical limitations. Every clause contributes information an agent needs.

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 six parameters, zero schema descriptions, and no output schema, the description leaves meaningful gaps: it does not explain the meaning or constraints of max_diffs, ignore_paths, numeric_tolerance, or the output structure. The behavioral and pricing context is strong, but parameter semantics are under-specified for a tool an agent must call 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%, so the description must compensate. It effectively clarifies 'expected' and 'observed' and hints at 'required_paths' via 'required-path checks', but it leaves 'max_diffs', 'ignore_paths', and 'numeric_tolerance' completely unexplained. An agent cannot understand the semantics of nearly half the parameters from the description.

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 names the operation: a deterministic field-level evidence diff between expected and observed execution results, and specifies what it reports (missing, unexpected, type-changed, value-changed paths). It is specific about the resource and output categories, though it does not explicitly distinguish itself from the many execution-safety 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 gives no guidance about when to use this tool versus sibling tools such as execution_safety_verification_* or outcome_assurance_verification_*. It implies usage for diffing expected versus observed results, but offers no exclusions, alternatives, or scenario-based guidance. The limitation caveat is useful but is not usage direction.

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, openWorldHint, idempotentHint, and non-destructive behavior; the description does not contradict these. It adds useful behavioral context by promising a deterministic execution order or concrete blockers and by disclosing x402 cost. No annotation contradiction.

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 earning its place: core purpose, domain exclusions, and cost signal. The main behavior is front-loaded before constraints and pricing. No wasted words or redundant restatement of the title.

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 no output schema, the description at least states the output form (deterministic order or blockers) and covers non-goals plus cost. It is still light on return-value structure and field-level semantics, though annotations cover the safety profile and the schema covers shape. Overall adequate for a moderately complex validation tool, with room to explain blocker/return conventions and parameter constraints.

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 carries the burden of explaining the four parameters. It only gestures at them through 'workflow dependencies and execution constraints,' without clarifying nodes/edges structure, max_total_cost, or max_total_latency_ms semantics. This is insufficient compensation for a schema with no property 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?

Description opens with a specific verb ('Validates') and a concrete resource ('caller-supplied workflow dependencies and execution constraints'), and specifies the return outcome ('deterministic execution order or concrete blockers'). It also sets domain boundaries with an explicit non-goal list. However, it does not differentiate this control-plane instance from the sibling execution_safety_control_plane_* tools, so it stops short of full sibling distinction.

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?

States a clear intended context ('execution safety / trust unknown') and an explicit when-not boundary ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). The pricing note also signals a paid resource. It does not name alternative sibling tools or explain switching conditions, so it misses the full alternatives guidance.

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 cover readOnly, idempotent, and non-destructive behavior. The description adds useful behavioral context: the tool returns either a deterministic execution order or concrete blockers, and it discloses that it is a paid resource with a specific x402 price. This goes beyond the structured annotations without contradicting them.

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 earning its place: the first states the core behavior, the second gives exclusion scope, and the third discloses cost. The description is front-loaded with the most actionable information and has no filler 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 tool with 4 parameters, no output schema, and many ambiguous siblings, the description is adequate but has clear gaps: parameter roles are not explained, this control plane is not distinguished from its sibling control plane, and the return shape beyond 'order or blockers' is absent. The annotations mitigate safety concerns, but the description alone is not 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 description coverage is 0%, so the description must compensate by explaining parameter meaning, but it only vaguely references 'workflow dependencies and execution constraints.' It does not clarify that nodes/edges are required, what cost, latency_ms, and enabled mean, or how max_total_cost and max_total_latency_ms constrain execution. Parameter semantics are insufficiently explained.

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 specific function: validating caller-supplied workflow dependencies and execution constraints and returning a deterministic execution order or concrete blockers. It clearly communicates what the tool does, though it does not explicitly differentiate this control plane from the many similarly named execution_safety siblings (e.g., execution_safety_control_plane_63e1c9dc).

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 intended use ('execution safety / trust unknown') and explicit non-uses ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). However, it names no alternative sibling tools or conditions for choosing among the other execution_safety_* variants, so it stops short of full routing guidance.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds valuable context beyond annotations: it is a paid resource with a specific price ($0.002 USD per call), and it returns either a deterministic execution order or concrete blockers. The 'cannot select' phrase is slightly cryptic but does convey a behavioral constraint.

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 with no filler. The core purpose is front-loaded, exclusions follow, and the pricing note is a single compact clause. Every sentence earns 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 read-only, idempotent validation tool with no output schema, the description covers purpose, exclusions, and cost. It does not explain the exact meaning of the four parameters or the shape of the returned blockers/order, but the annotations cover safety and the schema covers parameter structure. The main gap is parameter semantics, which is already reflected in that dimension.

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 carries the burden for parameter meaning. The description mentions 'workflow dependencies and execution constraints' which loosely maps to nodes/edges and max_total_cost/max_total_latency_ms, but it does not explain the semantics of individual parameters (e.g., what 'cost' or 'enabled' mean, how edges are interpreted). This is a baseline 3: the description gives a general sense but does not fully compensate for the 0% schema coverage.

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 specific verb ('validates') and resource ('caller-supplied workflow dependencies and execution constraints'), and specifies the output ('deterministic execution order or concrete blockers'). It also explicitly scopes the tool to 'execution safety / cannot select' and lists excluded domains, which distinguishes it from the many sibling policy/verification/control-plane 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 explicitly says 'Intended for execution safety / cannot select' and 'Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication.' This gives clear when-to-use and when-not-to-use guidance, and the sibling list shows many domain-specific alternatives (e.g., trust_identity_permission_*, procurement_policy_*) that the exclusion list helps disambiguate against.

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=false, destructiveHint=false, and idempotentHint=true, so the description's additional statement that it returns a deterministic order or blockers and the $0.002 cost adds value without contradiction. However, it does not describe failure modes, authentication, or other behavioral details, so it only marginally exceeds what annotations provide.

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 brief and front-loaded with the core purpose. It efficiently includes price and exclusions in a few sentences, though the phrase 'Intended for execution safety / quality unknown' is vague and could be clearer. No filler words, but the vague phrase lowers it from a 5.

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 4 parameters, no output schema, and many closely-related siblings, the description is incomplete. It does not explain node/edge semantics, constraint behavior, or how to interpret the returned order/blocks, nor does it position itself relative to other orchestration tools. The cost note is helpful but cannot compensate for these gaps.

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 only vaguely mentions 'workflow dependencies and execution constraints,' hinting at nodes/edges and max_total_cost/latency, but does not explain the meaning of individual parameters like cost, latency, or enabled. This is insufficient for a 0% coverage situation, earning a low score.

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 validates caller-supplied workflow dependencies and execution constraints and returns a deterministic execution order or concrete blockers, giving a specific verb and resource. However, it does not differentiate from the many other execution_safety_orchestration_* siblings, so it earns a 4 rather than a 5.

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 exclusions (do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication) and notes the paid nature, but it does not state when this tool should be used over alternatives or name any sibling tools. The intent is implied but not explicit, leaving the agent without clear selection guidance.

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 cover idempotency, destructiveness, and open-world behavior; the description adds the concrete $0.002 per-call price and the promise of deterministic output. It does not explain side effects, required auth, or how blockers are represented, but it does not contradict 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?

Three sentences with the function first, then exclusions, then pricing; no boilerplate or redundant fluff. The phrase 'cannot select' is cryptic, but it does not inflate the length. The structure is easy to scan.

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, so the return description should be more precise than 'deterministic execution order or concrete blockers'. It also omits error behavior for invalid graphs or cycles and does not explain how the optional constraint parameters relate to the blockers. For a graph-based orchestration tool with four undocumented parameters, this is incomplete.

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?

With 0% schema-description coverage, the description must carry parameter meaning. It broadly labels 'workflow dependencies' (nodes/edges) and 'execution constraints' (max_total_cost/max_total_latency_ms), but never explains individual fields, units, optionality, or how enabled/cost/latency affect ordering. Most semantic weight remains on the field names.

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 ('Validates') and resource ('workflow dependencies and execution constraints') and says what it returns ('deterministic execution order or concrete blockers'). The 'Intended for execution safety / cannot select' line plus explicit exclusions separates it from legal/identity/selection siblings, but it does not differentiate this hashed orchestration variant from the other execution_safety_orchestration_* 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?

Gives positive scope ('execution safety / cannot select') and explicit negative scope (legal, identity, sanctions, fraud, contractual, regulatory adjudication), so an agent knows when not to invoke it. It does not name a fallback sibling tool for those excluded domains, so the guidance stops short of a full decision tree. The paid-resource note adds a practical cost consideration.

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 annotations already provide idempotency, read-only, open-world, and destructive hints, and the description adds context beyond them: paid resource with a specific x402 price, deterministic output, and a 'trust unknown' framing. It does not describe side effects despite readOnlyHint=false, but the cost disclosure and determinism note are meaningful additions.

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 dense sentences: the first delivers the core function and output, the second bounds applicability, and the third handles pricing. There is no filler or repeated schema information.

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 moderate-complexity validation tool with no output schema, the description gives enough about inputs, outputs, exclusions, and cost. It falls slightly short on explaining optional parameters and error/blocker shape in more detail, but the core agent decision of whether to call this tool is well supported.

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 the absence of parameter docs. It only loosely maps 'workflow dependencies' to nodes/edges and 'execution constraints' to max_total_cost/max_total_latency_ms, leaving units, the enabled flag, cost fields, and the meaning of omitted constraints unexplained.

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 a specific verb ('Validates') and names the resource ('caller-supplied workflow dependencies and execution constraints') plus the concrete output ('deterministic execution order or concrete blockers'). It clearly distinguishes itself from legal, identity, and financial adjudication tools, though it does not explicitly differentiate from similarly named execution_safety_* siblings such as workflow_dependency_validator.

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 gives an explicit intended context ('execution safety / trust unknown') and explicit exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It stops short of naming alternative tools an agent should use instead, so it is not a full 5.

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 cover idempotency and destructiveness. The description adds valuable behavioral context: it is a paid resource with a concrete price, and the result is deterministic. It does not explain any side effects despite readOnlyHint being false, but the annotations carry that signal, so the description is not misleading.

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 with the core purpose front-loaded and pricing/exclusions placed after. The phrase 'Intended for execution safety / cannot select' is somewhat awkward and redundant, but overall the description is compact and well-ordered.

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 moderately complex tool with no output schema, the description explains the high-level purpose, exclusions, and cost. It does not specify how the returned blockers or execution order are structured, but the schema and the 'deterministic execution order or concrete blockers' phrase provide enough orientation for invocation.

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 maps 'dependencies' to edges and 'execution constraints' to max_total_cost/max_total_latency_ms, but it does not explain node-level fields like cost, enabled, or latency_ms, nor how blockers are represented. Partial compensation, not full.

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 a specific verb ('validates') and resource ('caller-supplied workflow dependencies and execution constraints'), and states the output ('deterministic execution order or concrete blockers'). However, it does not differentiate this tool from the many sibling execution_safety_orchestration_* variants, and the phrase 'Intended for execution safety / cannot select' is vague.

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 provides explicit negative guidance ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication') and frames the intended domain ('execution safety'). It stops short of naming a specific alternative tool for those exclusions, but the usage context is clear enough for an agent to avoid misuse.

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=false, destructiveHint=false, and idempotentHint=true, covering the side-effect profile. The description adds that the tool is deterministic and costs $0.002 USD per call, which are meaningful behavioral details beyond the annotations. It does not mention rate limits or error semantics, but the annotation coverage lowers the bar and the added context is useful.

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: three sentences covering purpose, exclusions, and pricing. Each sentence adds distinct information, and the main purpose is front-loaded. The pricing detail, while secondary, is practical for a paid resource and does not bloat the 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?

With no output schema and zero parameter descriptions, the agent has only the schema types (strings, numbers, arrays) and the vague phrase 'workflow dependencies and execution constraints' to rely on. The description does not explain how blockers are represented, what deterministic execution order means, or how the cost/latency fields are evaluated. It is adequate for high-level selection but not for correct invocation.

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 all four parameters. It only offers the generic phrase 'workflow dependencies and execution constraints,' which does not explain the structure of nodes and edges, the meaning of 'enabled,' cost/latency thresholds, or how constraints like max_total_cost and max_total_latency_ms interact. This is insufficient for an agent to correctly construct a request without further inference.

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 specific action: validates caller-supplied workflow dependencies and execution constraints, returning a deterministic execution order or concrete blockers. This is clear about the tool's purpose and output. However, it does not differentiate from the many sibling tools with nearly identical names (e.g., execution_safety_orchestration_0a797593), so it misses the chance to distinguish itself among them.

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 'Do not use for' list covering legal, identity, sanctions, fraud, contractual, and regulatory adjudication, which is strong negative guidance. It also notes it is a paid resource, implying cost-aware invocation. However, it does not name any alternative tools for those excluded domains, nor does it specify positive conditions that would make this tool preferable over other execution_safety_orchestration variants.

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 indicate readOnlyHint=false, idempotentHint=true, destructiveHint=false. The description adds cost information (x402 price) and output behavior (returns order or blockers), which goes beyond annotations. It does not contradict annotations, and since the tool is non-destructive and idempotent, the bar for additional disclosure is lower. However, it does not clarify what side effects (if any) occur, so it only partially fulfills transparency.

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 three sentences and front-loads the core purpose. It includes cost and exclusions efficiently. However, the second sentence ('Intended for execution safety / quality unknown') is vague and adds little value. Overall, it is concise and well-structured, though not maximally informative.

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 4 parameters (2 required), no output schema, and no parameter descriptions, the description is incomplete. It does not explain input structure, output format, or how constraints like cost and latency are handled. Given the large number of similar sibling tools, it also fails to provide distinguishing context. The description is too high-level for an agent to call the tool correctly without further schema details.

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 explain the parameters. It mentions 'workflow dependencies and execution constraints' but does not map these to the actual parameters (nodes, edges, max_total_cost, max_total_latency_ms). No details on how these fields are used or interpreted are provided, leaving the agent without essential parameter 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?

The description states a specific action ('Validates caller-supplied workflow dependencies and execution constraints') and its output ('deterministic execution order or concrete blockers'). It clearly identifies the tool's function. However, it does not explicitly differentiate from the many similarly-named sibling tools (e.g., execution_safety_control_plane, execution_safety_policy_engine), so it lacks sibling distinction.

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 a list of exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication') and a vague 'Intended for execution safety / quality unknown', but it offers no positive guidance on when to use this tool versus alternatives, nor any conditions for selection. The exclusions are negative constraints, not actionable usage instructions.

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 mark the tool as non-read-only, idempotent, open-world, and non-destructive. The description adds useful behavioral context: it is a paid resource with a specific price, and it returns deterministic results. It does not mention authentication or rate limits, but the annotation coverage lowers the burden.

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: three sentences covering purpose, exclusions, and cost. It is front-loaded with the core behavior and avoids filler, though the first sentence is somewhat dense.

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 no output schema and 0% parameter coverage, the description provides the core purpose, exclusions, and pricing but leaves important context under-specified: how blockers are determined, how constraints affect the returned order, and what callers should infer from the open-world annotation. It is adequate, 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 the undocumented parameters. It vaguely refers to 'workflow dependencies and execution constraints' but never maps these to nodes, edges, max_total_cost, or max_total_latency_ms, and gives no guidance on how the constraint fields interact with the graph.

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 specific verb ('validates') and resource ('caller-supplied workflow dependencies and execution constraints') and describes the return value as a deterministic execution order or concrete blockers. It does not explicitly name or differentiate from sibling orchestration tools, so it stops short of a 5.

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 intended use ('execution safety / permission mismatch') and an explicit do-not-use list for legal, identity, sanctions, fraud, contractual, or regulatory adjudication. It provides clear context and exclusions, but it does not name alternative tools or describe when to prefer them.

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 provide idempotentHint=true and destructiveHint=false. The description adds valuable context: the tool is paid ($0.002 per call) and its output is either a deterministic order or concrete blockers, which suggests a non‑destructive, repeatable operation. No contradiction with annotations 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?

Three concise sentences, each earning its place: function, intended use/exclusions, and pricing. No redundancy or filler.

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, the description explains the output generally (order or blockers) but not its structure or error format. It also does not explicitly mention that nodes and edges are required, though the schema conveys that. For a graph‑based tool with many similarly named siblings, the description provides enough high‑level context but lacks detail on input validation semantics and output specifics.

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 offers a high‑level interpretation ('workflow dependencies' for nodes/edges, 'execution constraints' for cost/latency limits) but does not map each parameter explicitly. Parameter names are somewhat self‑explanatory, but the description adds only partial value beyond the raw 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 states a specific verb ('Validates') and resource ('caller-supplied workflow dependencies and execution constraints'), and clarifies the output ('deterministic execution order or concrete blockers'). It also explicitly excludes unrelated domains (legal, identity, etc.), which distinguishes it from sibling tools in other categories.

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 a clear intended use ('execution safety / settlement failure') and strong when-not guidance (not for legal, identity, sanctions, fraud, contractual, or regulatory adjudication). However, it does not differentiate between the many execution_safety_orchestration_* siblings or other execution-safety tools (e.g., control planes, policy engines, preflights), leaving the agent to infer which specific orchestration variant to call.

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 provide readOnlyHint=false, destructiveHint=false, idempotentHint=true, and openWorldHint=true. The description adds that it returns a deterministic execution order or concrete blockers, and states the x402 price. It does not disclose any side effects despite readOnlyHint=false, nor does it elaborate on the 'trust unknown' phrase. The added cost and return behavior are useful, but the description doesn't go beyond that, so it's adequate but not rich.

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 front-loaded: the primary purpose is stated in the first sentence, followed by usage exclusions and pricing. There is no fluff. It could be slightly more structured (e.g., separating usage notes), but it is appropriately concise for the information it conveys.

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 is a graph-based orchestrator with constraints, but the description provides no details on input format, output structure (beyond 'execution order or blockers'), error conditions, or the meaning of 'concrete blockers'. It also omits the maxItems/minItems constraints. Given the complexity and lack of an output schema, the description is incomplete for an agent to call it correctly without additional inference.

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 has 0% description coverage, and the description does not explain any of the four parameters (nodes, edges, max_total_cost, max_total_latency_ms). It only vaguely refers to 'caller-supplied workflow dependencies and execution constraints', which does not help an agent understand parameter semantics, types, or constraints. With no schema descriptions, the description fails to compensate, leaving agents to infer from names alone.

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 validates workflow dependencies and execution constraints, returning a deterministic execution order or concrete blockers. It names a specific verb and resource, and adds exclusions (not for legal, identity, etc.) that help scope it. However, it does not explicitly differentiate from the many execution_safety_* sibling tools, so it lacks direct sibling contrast.

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 context: 'Intended for execution safety / trust unknown' and lists domains where it should NOT be used (legal, identity, sanctions, fraud, contractual, regulatory). This provides some usage boundaries but no explicit alternatives or when-to-use guidance relative to sibling tools. It also mentions it's a paid resource, which may affect decision-making, but the 'when not to use' is the strongest part.

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 provide idempotentHint=true and destructiveHint=false. The description adds useful behavioral context by disclosing paid resource pricing ($0.002 per call), deterministic output, and the possibility of concrete blockers. 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?

Three concise sentences: function, usage boundaries, and cost. Each sentence earns its place, and the core purpose is front-loaded.

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, the description helpfully states the return type (deterministic execution order or concrete blockers) and pricing. However, it does not explain what constitutes a blocker, how constraints interact, or what the caller should do with the output, leaving some gaps for a 4-parameter 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?

Schema description coverage is 0%, so the description must compensate. It only broadly refers to 'workflow dependencies and execution constraints' and does not explain nodes, edges, max_total_cost, or max_total_latency_ms beyond what their names imply.

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 validates workflow dependencies and execution constraints and returns an execution order or blockers. It uses a specific verb and resource, but does not explicitly differentiate it from the several execution_safety_orchestration and workflow_dependency_validator siblings.

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 is explicit that this is for execution safety and not for legal, identity, sanctions, fraud, contractual, or regulatory adjudication. This provides strong when-to-use and when-not-to-use guidance, though it does not name alternative sibling tools.

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 read-only, idempotent, open-world, and non-destructive behavior, so the safety profile is covered. The description adds useful behavioral details beyond those annotations: the tool returns blocking violations and warnings, and it is a paid resource priced at $0.002 per call. No contradiction with annotations exists.

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 with no filler: function first, then scope/exclusions, then pricing. Each sentence earns its place, and the most important operational information is front-loaded.

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, yet the description only says 'returns blocking violations and warnings' without describing the response shape. The facts parameter is a freeform object with no examples or guidance, and the rules semantics are left mostly to the schema. The pricing and exclusion notes add useful context, but an agent still lacks enough detail to confidently construct a valid request for an arbitrary safety evaluation.

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 carries the burden of explaining the two parameters. It only refers generically to 'caller-supplied facts' and 'caller-supplied rules' without explaining fact structure, rule syntax, severity defaults, or how fields and operators work. The schema itself provides the only real semantics for the rules array, and the facts object remains completely unspecified.

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 action ('Evaluates caller-supplied facts against explicit caller-supplied rules') and names the output ('blocking violations and warnings'). It also clarifies the domain ('execution safety') and excludes many adjacent domains. However, among the many execution_safety_policy_engine siblings, it offers no trait that differentiates this specific engine from its similarly named peers.

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?

Explicitly says when NOT to use it: not for legal, identity, sanctions, fraud, contractual, or regulatory adjudication. It also frames the intended use case as execution safety. It does not name a specific alternative tool to use instead, so it stops short of full when-to-use-versus-alternative guidance.

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 establish read-only, idempotent, open-world, and non-destructive behavior. The description adds value beyond those annotations by disclosing the return outcome (blocking violations and warnings), emphasizing that inputs are caller-supplied, and flagging the $0.002 paid cost per call. No contradiction with annotations exists.

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 with no filler: the core behavior is first, followed by scope exclusions and cost. Every sentence contributes decision-relevant information, and the most important call-selection information is front-loaded.

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 intended use, exclusions, cost, and high-level return content, and the annotations provide safety context. However, with no output schema and no parameter-level descriptions, an agent still lacks clarity on the exact shape of facts, the rule evaluation semantics, and the response structure beyond 'blocking violations and warnings.'

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, but it only says facts and rules are 'caller-supplied.' It does not explain how facts map to rule fields, what the evaluation semantics are for operators, or how severity/message affect results. The schema documents structure, not meaning, and the description leaves the semantic burden mostly unmet.

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 specific action ('Evaluates caller-supplied facts against explicit caller-supplied rules') and a clear result ('returns blocking violations and warnings'). It narrows scope to execution safety / result unverifiable and excludes several adjacent domains, but it does not explicitly differentiate this tool from sibling policy engines such as execution_safety_policy_engine_40040417.

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 a positive use case ('Intended for execution safety / result unverifiable') and an explicit when-not list ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name specific alternative tools to route to, so it falls just short of fully explicit guidance.

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 establish read-only, idempotent, non-destructive behavior. The description adds valuable context beyond the annotations by stating the return type ('blocking violations and warnings') and disclosing that this is a paid resource with a specific x402 price per call. This is meaningful behavioral and cost information, especially given the absence of an output schema.

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 with no filler: the core action is front-loaded, the scope and exclusions follow, and the pricing/cost note is placed last. Every sentence earns its place, and the description is appropriately sized for the tool's complexity.

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 captures the tool's purpose, domain boundaries, return type, and pricing, which is a solid foundation. However, with no output schema and no parameter-level documentation, the agent still lacks guidance on expected fact schemas, rule semantics, and response structure. For a tool with nested objects and enum-driven rule behavior, this leaves notable gaps.

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 carries the burden of explaining facts and rules, but it only says 'caller-supplied facts against explicit caller-supplied rules.' It does not explain how facts should be shaped, what field paths refer to, how operators like 'eq' or 'exists' behave, or how severity maps to blocking versus warnings. The schema enumerates rule fields but provides no semantic meaning, and the description fails to compensate.

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 specific verb and resource: 'Evaluates caller-supplied facts against explicit caller-supplied rules and returns blocking violations and warnings.' It clearly identifies the tool's domain as execution safety/risk and explicitly excludes legal, identity, sanctions, fraud, contractual, and regulatory adjudication. However, it does not distinguish this engine from the several similarly named execution_safety_policy_engine siblings, so it stops short of full sibling differentiation.

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 intended use ('Intended for execution safety / execution risk') and explicit when-not-to-use guidance ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name alternative sibling tools to use for those excluded domains, but the positive scoping plus exclusions give an agent solid routing information.

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=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the paid-resource detail ($0.002 USD per call), which is useful behavioral context beyond the annotations. It doesn't describe edge cases like rule evaluation order or error handling, but the annotations carry the main safety burden.

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 with no filler. The core function is front-loaded, the exclusions are clear, and the pricing note is a single compact clause. Every sentence earns 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 2-parameter tool with a rich schema and safety annotations, the description is nearly complete. It covers purpose, exclusions, and cost. The only minor gap is that it doesn't describe the return structure (e.g., how violations/warnings are formatted), but since there is no output schema, a bit more detail there would help. Still, the core calling context is well 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 0%, so the description does not explain the 'facts' or 'rules' parameters beyond their names. However, the schema itself is fairly rich: 'rules' has a detailed item structure with 'field', 'op', 'value', 'message', and 'severity' enums. The description's phrase 'caller-supplied facts against explicit caller-supplied rules' adds minimal semantic context but doesn't compensate for the 0% coverage. Baseline 3 is appropriate because the schema carries the parameter 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 states a specific verb ('Evaluates'), a clear resource ('caller-supplied facts against explicit caller-supplied rules'), and the exact output ('blocking violations and warnings'). It also distinguishes itself from legal/identity/sanctions/fraud/contractual/regulatory adjudication, which helps an agent differentiate it from the many policy-engine siblings.

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 says 'Intended for execution safety / trust unknown' and 'Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication.' This gives clear when-to-use and when-not-to-use guidance, and the sibling list shows many policy engines, so this exclusion is valuable.

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, open-world, idempotent, and non-destructive behavior. The description adds that it returns blocking violations and warnings and notes the price, which is useful but not extensive. It does not contradict the annotations, and the bar is lowered by the strong annotation coverage.

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-loads the core purpose. It includes necessary exclusions and pricing, but the phrase 'quality unknown' is vague and adds little. Overall it is well-structured and to the point.

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 is complex with nested objects and no output schema, so the description should explain the return format and usage details. It only says 'returns blocking violations and warnings,' which is vague. It also lacks examples of rule evaluation and does not differentiate from the many similar sibling tools, making it incomplete.

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?

With schema description coverage at 0%, the description must compensate for parameter meaning, but it only rephrases the parameter names ('caller-supplied facts' and 'explicit caller-supplied rules') without adding format, examples, or constraints. The schema provides some structure for rules, but facts remain undefined, and the description adds no value 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 clearly states the tool evaluates caller-supplied facts against rules and returns blocking violations and warnings, which specifies a concrete verb, resource, and output. However, it does not differentiate from other policy engines in the sibling list (e.g., execution_safety_policy_engine_08f4a690), so it lacks explicit sibling distinction.

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 exclusions (not for legal, identity, sanctions, fraud, contractual, or regulatory adjudication) and states it is intended for execution safety, which gives context. However, it does not mention when to use this tool over its many siblings or name any alternative tools, and it includes pricing but no usage conditions.

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=true and idempotentHint=true, so the agent knows it is a safe, non-mutating operation. The description adds the pricing information ($0.002 per call) and clarifies the return type (blocking violations and warnings), which supplements the annotations with useful context. It doesn't detail error behavior or edge cases, but the annotations cover the key safety traits.

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, with three sentences: one for core functionality, one for usage exclusions, and one for pricing. The most critical information (evaluates facts and rules) is front-loaded. It wastes no words, though the pricing sentence could be seen as auxiliary.

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 rule-evaluation tool with nested objects and no output schema, more could be done to clarify what 'facts' means structurally and the exact output format. The exclusions and pricing are helpful, and the schema provides some rule structure, but the facts parameter remains opaque Von an agent's perspective, leaving some ambiguity in how to construct valid input.

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 the lack of parameter documentation. The description only says 'facts' and 'rules' without detailing their structure or semantics; the schema defines rules as an array with fields like op, field, value, but the description leaves the facts object entirely undetermined. This is minimal compensation for two complex parameters.

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 evaluates caller-supplied facts against caller-supplied rules and returns blocking violations and warnings, which is a specific verb-resource-action combination. It further distinguishes its intended use for execution safety and explicitly excludes other domains like legal, identity, sanctions, etc., differentiating it from potentially similar policy engines.

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 states that the tool is intended for execution safety and should NOT be used for legal, identity, sanctions, fraud, contractual, or regulatory adjudication, providing clear when-not-to-use guidance. However, it does not explicitly name alternative tools for those other domains, so it stops short of a full 5.

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 establish read-only, idempotent, non-destructive behavior; the description adds that the tool returns 'blocking violations and warnings' and that the facts/rules are caller-supplied, plus the operational detail that the resource is paid (x402 $0.002 per call). There is no contradiction with the annotations, and this context goes beyond what the structured fields already 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?

The description is compact and front-loaded, delivering function, domain, exclusions, and pricing in four short sentences. The phrase 'Intended for execution safety / cannot select' is awkward and ambiguous, which slightly weakens the otherwise tight structure.

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 two-parameter tool with a fairly detailed rule schema, the description is adequate at a high level, but it lacks an output shape, examples, and any differentiation among the numerous execution_safety_policy_engine and execution_safety_orchestration siblings. Since there is no output schema, the exact response format would still require some inference.

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?

With 0% schema description coverage, the description carries meaningful weight by clarifying that 'facts' and 'rules' are caller-supplied and that results are 'blocking violations and warnings,' which maps to the severity enum. But it does not explain the shape of the facts object, the meaning of individual rule fields such as field/op/value/message, or how block vs warn outputs behave in practice.

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 a specific verb ('Evaluates') and states the inputs (caller-supplied facts and rules) and the return kind (blocking violations and warnings). It clearly ties the tool to execution safety and explicitly excludes legal, identity, sanctions, fraud, contractual, and regulatory adjudication, though it does not differentiate this engine from the many other execution_safety_policy_engine_* siblings.

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 a clear intended domain ('execution safety') and explicit negative guidance ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'), which helps an agent avoid misuse. It also notes that the resource is paid and discloses the exact price. However, it names no specific alternative tools or conditions for choosing this engine over another policy-engine variant.

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 destructiveHint=false. The description adds determinism, technical scope, and cost transparency ($0.002 per call). It also describes the outcome (identifying safe/blocked steps). No contradiction with annotations; it enhances them.

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?

Four sentences, each serving a distinct purpose: purpose, scope, usage, and exclusions/cost. The most important information (what the tool does) is front-loaded, and every sentence earns its place without unnecessary verbosity.

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 safety scope and usage well, but lacks the input structure for the steps array and does not describe the output format. Without an output schema, the agent does not know what response to expect. This is a significant gap for a tool that takes an underspecified array parameter.

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 only parameter 'steps' is an array with 0% schema description coverage. The description refers to an 'agent action plan' and 'multi-step agent action' but never specifies the structure or fields of each step. An agent cannot confidently construct valid input without additional knowledge of what a step should contain.

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 a specific verb (preflight) and resource (agent action plan), and lists concrete hazard categories it checks (irreversible side effects, idempotency, rollback gaps, retry combinations, step-order). It distinguishes from siblings by being a preflight and scoping to technical safety only, so an agent can tell it apart from verification, control plane, or orchestration 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?

Explicitly states when to use (before executing a multi-step agent action) and what it determines (safe vs. blocked steps). It also provides a clear 'do not use' list (unrelated general knowledge or unsupported legal/identity/sanctions/regulatory adjudication). This positive and negative guidance is strong for routing.

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 destructiveHint false, so the description correctly avoids repeating those. It adds value by disclosing the cost ('Paid resource; x402 price is $0.002 USD per call') and the output nature ('returns blocking violations and warnings'), which are behavioral facts not covered by 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?

Three concise sentences: the first states the function, the second gives exclusions and intent, and the third covers cost. Every sentence carries useful information with no filler 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?

The description is reasonably complete for a read-only, idempotent tool, but it lacks details on the return format (no output schema exists) and leaves 'quality unknown' ambiguous. It also does not clarify how the 'severity' field influences blocking vs. warning behavior, though the schema partly covers this via enums.

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 the lack of parameter documentation. It only names 'facts' and 'rules' without explaining their structure, expected values, or how they relate, leaving the agent to infer semantics from the raw schema. The rules schema has nested properties and enums that remain unexplained in the description.

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 specific verb and object: 'Evaluates caller-supplied facts against explicit caller-supplied rules and returns blocking violations and warnings.' This clearly states the operation and distinguishes it from more general policy engines, though it does not explicitly differentiate from the version-less sibling 'execution_safety_preflight' or explain the '8ce15b25' suffix.

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 a clear negative boundary—'Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'—and implies a preflight safety context with 'Intended for execution safety / quality unknown.' However, it doesn't specify when to use this tool over the many similar siblings (e.g., policy engines, verification tools), nor does it provide positive use-case examples.

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 annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavioral transparency beyond those: determinism, caller-supplied verification rules, reporting of material differences, and the paid-resource cost of $0.002 USD per call. No contradiction with annotations 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 tightly structured in three short sentences: behavior first, then boundary conditions, then cost. Each sentence carries distinct information with no filler or repetition, making the critical facts immediately visible.

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 gives a good overall context: purpose, exclusions, and cost, and the annotations supply the safety profile. However, with no output schema and five parameters (two required, three optional verification rules), the description leaves the exact semantics of the verification rules ambiguous, so an agent may still need to introspect or experiment to invoke it reliably.

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 itself only names 'expected' and 'observed' while referring vaguely to 'caller-supplied verification rules.' It does not explain how ignore_paths, required_paths, or numeric_tolerance should be used, so an agent receives little parameter-level guidance beyond the bare parameter names in 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 states a specific verb and resource: it 'deterministically compares caller-supplied expected and observed values' and 'reports material differences.' It clearly identifies the tool as an execution-safety verification comparator, but it does not differentiate it from the many similarly named sibling tools such as execution_safety_verification_1241c3d6 or outcome_assurance_verification_0f7824e0.

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 says it is 'intended for execution safety / trust unknown' and gives a clear do-not-use list covering legal, identity, sanctions, fraud, contractual, and regulatory adjudication. It stops short of naming an alternative sibling tool, but the exclusion guidance is strong enough to guide an agent away from misuse.

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, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds deterministic behavior, 'material differences' reporting, and the paid cost ($0.002 USD per call). It does not describe the return format or error handling, which would be useful, but it adds some behavioral context beyond 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 concise and well-structured: it leads with the core function, then states intended use, exclusions, and cost. Each sentence adds distinct value with no redundancy or fluff. It is appropriately sized for the tool's complexity.

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 verification tool with five parameters and no output schema, the description covers purpose, usage, exclusions, and cost, but lacks critical details on parameter semantics (e.g., how ignore_paths and required_paths work, what numeric_tolerance does) and what the 'material differences' report looks like. The annotations cover safety, but the description is not complete enough for an agent to confidently construct a correct call, especially for the optional parameters.

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% — the description does not explain any of the five parameters. It only hints at 'expected and observed' values, which map to the two required parameters, but it completely ignores ignore_paths, required_paths, and numeric_tolerance. With no schema descriptions and no parameter explanations in the description, the agent cannot understand how to use these optional parameters, so the description fails to compensate for the schema 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 function: deterministically compares expected and observed values and reports material differences under caller-supplied rules. It also gives the intended domain (execution safety/vendor lock-in) and explicitly excludes legal, identity, sanctions, fraud, contractual, or regulatory uses. However, it does not explicitly differentiate from sibling verification tools like execution_safety_verification_0be6d99b, which have nearly identical names, so it misses a clear sibling distinction.

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 explicit usage context: intended for execution safety/vendor lock-in, and explicitly states what it should not be used for (legal, identity, sanctions, fraud, contractual, regulatory). This gives clear when-to-use and when-not-to-use guidance, though it does not name specific alternative tools for those excluded cases.

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 destructiveHint false; the description adds useful behavioral detail: deterministic comparison, material-difference reporting, and paid-resource pricing. It does not describe the output shape, but annotations lower the burden for safety traits.

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 with the core behavior first, then domain exclusions and pricing. Every sentence adds distinct information with 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?

With five parameters, no parameter descriptions, and no output schema, the definition leaves important gaps: accepted value shapes, path rule semantics, tolerance behavior, and the format of the material-difference report. An agent would struggle to reliably construct verification rules or interpret results.

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, but it only names expected/observed and vaguely refers to 'caller-supplied verification rules.' It never explains ignore_paths, required_paths, numeric_tolerance, path syntax, or how tolerance affects the comparison.

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 operation ('deterministically compares caller-supplied expected and observed values') and a concrete outcome ('reports material differences'), so the core function is clear. It does not distinguish this tool from the several other execution_safety_verification_* siblings, so it misses the top score.

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 clearly scopes the tool to execution safety/quality verification and gives explicit negative guidance ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name a preferred alternative or provide a selection rule among the many verification/control-plane 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?

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds value by disclosing deterministic comparison, material-difference reporting, and a paid x402 price. It does not fully describe output format, but it meaningfully supplements 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?

The description is short and front-loaded with the core behavior, followed by usage boundaries and pricing. Every sentence carries useful information, though 'quality unknown' is slightly ambiguous.

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 parameter-level documentation, the description leaves return shape, optional rule semantics, and sibling-tool selection under-specified. The exclusion list is useful, but the definition is incomplete for a 5-parameter tool with no output schema.

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 has 0% description coverage across 5 parameters, so the description must compensate. It names expected and observed but only vaguely references 'caller-supplied verification rules' without explaining ignore_paths, required_paths, or numeric_tolerance. The agent cannot confidently construct the optional parameters from this description alone.

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 specific action and resource: it 'compares caller-supplied expected and observed values' and 'reports material differences' under verification rules. It also gives an intended domain and explicit non-goals. However, it does not differentiate this tool from sibling tools like execution_safety_verification_0be6d99b or execution_result_evidence_diff.

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 explicit exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication') and an intended domain ('execution safety / quality'). It lacks alternatives or selection criteria among the many execution_safety_verification_* siblings, so the agent cannot confidently choose this tool over similar ones.

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 cover read-only, open-world, idempotent, and non-destructive traits. The description adds valuable behavioral context: the output types (missing, duplicate, out-of-order, orphan, coverage metrics) and the pricing/cost ($0.002 per call). No contradiction with annotations; the description enriches them.

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 front-loaded: the primary action and outcome come first, followed by usage scope, exclusions, and pricing. Every sentence adds value with no redundancy, and it fits in a few sentences without being verbose.

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 no output schema, the description sufficiently explains what the tool returns (missing, duplicate, out-of-order, orphan events, coverage metrics). It also covers usage scope, exclusions, and cost. An agent has all necessary information to decide when to call and what to expect from the response.

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 carries the burden. It gives meaning to both parameters: 'expected_steps' is the 'expected step graph' and 'events' are 'caller-supplied execution trace events'. This clarifies their roles beyond the raw names, though it does not detail the array element structure or required format.

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 a specific verb ('Checks') and resource ('caller-supplied execution trace events against an expected step graph'), and enumerates the exact outputs (missing, duplicate, out-of-order, orphan events, coverage metrics). It distinguishes itself from siblings like failure_classifier by explicitly saying it is 'not merely classify a failure'.

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?

Provides explicit when-to-use guidance ('Use this tool when determine whether an execution trace contains enough technical evidence to reconstruct the expected workflow') and when-not-to-use ('Do not use it for unrelated general knowledge or unsupported legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It also notes the paid nature, setting clear expectations.

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 mark the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context beyond those annotations: it is 'deterministic', it is a 'paid resource' with a specific price, and it returns a 'technical failure category and evidence basis'—critical because no output schema exists. It does not cover rate limits or edge cases, but the annotation-safety profile lowers the bar for those.

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 dense sentences cover purpose, inputs, output, exclusions, and pricing with no filler. The most decision-relevant facts—classification scope and return type—are front-loaded. The only minor redundancy is 'Paid' appearing twice, but it does not hurt effectiveness.

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?

Despite high complexity (9 undocumented parameters, no output schema), the description omits any detail about most input signals and does not explain how the returned evidence basis is structured. An agent could understand the tool's purpose but cannot confidently construct a correct request or interpret the full response from this definition alone.

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 has 9 parameters with 0% description coverage and no enums, so the description must compensate. It only broadly references 'HTTP status, error text, protocol and execution/payment signals', leaving booleans like permission_ok, payment_signature_present, output_match, and schema_ok completely unexplained. This is insufficient for an agent to know what values or combinations are valid.

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 verb and resource: 'paid deterministic classification of agent-commerce failures' from supplied signals. It further clarifies output as 'technical failure category and evidence basis' and explicitly excludes 'legal or fraud adjudication', making the tool's purpose distinct from legal/fraud classifiers even without naming a sibling.

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 clearly describes the intended inputs ('HTTP status, error text, protocol and execution/payment signals') and provides an explicit exclusion: 'no legal or fraud adjudication', serving as a when-not-to-use. However, it does not name alternative sibling tools or give precise trigger conditions for choosing this tool over related failure-analysis tools.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by noting it is 'Deterministic' (a behavioral trait) and by disclosing the cost ('Paid resource; x402 price is $0.002 USD per call'). It does not contradict 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?

The description is three sentences with no fluff. It front-loads the purpose, gives usage guidance, and includes relevant cost information. Every sentence earns 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 tool with two parameters, no output schema, and no parameter descriptions, the description covers the purpose and usage well but leaves gaps: it does not explain what the tool returns (e.g., a risk verdict, evidence list, or score), nor does it detail the input structure. Given the safety profile is handled by annotations, a 3 is appropriate – it is adequate but not 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 description coverage is 0%, so the description must compensate. It mentions 'operation identity, idempotency keys, replay windows and prior fingerprints,' and explicitly references 'prior_fingerprints' as a parameter, but it does not explain the structure or format of the 'requests' array or what 'prior_fingerprints' contains. The mapping from concepts to actual parameters is vague, leaving the agent to guess the expected input 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 clearly states a specific verb ('Deterministic analysis') and resource ('mutating agent requests for duplicate-effect and replay exposure'), and specifies the method (operation identity, idempotency keys, replay windows, prior fingerprints). It is distinct from siblings by its focus on idempotency and replay, and is not a tautology.

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?

Explicitly tells when to use it ('before retrying or replaying a mutating action') and when not to use it ('Do not use it for unrelated general knowledge or unsupported legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). This provides clear context and exclusions, even though it does not name specific alternative tools.

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 read-only, idempotent, non-destructive behavior. The description adds determinism, comparison semantics, material-difference reporting, and pricing, which go beyond the annotations without contradicting them. It could disclose more about return formatting, but the added context is meaningful.

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 with the core purpose, followed by usage boundaries and pricing. The awkward phrase 'cannot select' and the run-on second sentence slightly reduce clarity, but overall the structure is efficient and scannable.

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 no output schema and no property descriptions in the schema, the description does not sufficiently explain how the verification rules work, how the optional parameters behave, or what the response contains. An agent could call the tool with basic expected and observed values, but correct use of the optional parameters is not well supported.

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 mentions expected and observed values but does not explain ignore_paths, required_paths, or numeric_tolerance beyond a vague reference to 'verification rules.' This leaves the optional parameters semantically underspecified.

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 compares expected and observed values and reports material differences under caller-supplied rules, which is a specific verb-resource pairing. It also identifies the intended domain as observability, though the phrase 'cannot select' is ambiguous and it does not explicitly distinguish itself from other verification siblings.

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 intended use (observability) and explicit exclusions (legal, identity, sanctions, fraud, contractual, regulatory adjudication). It does not name a specific alternative tool, but the context and restrictions are clear enough for an agent to make a reasonable selection decision.

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 provide readOnly, idempotent, openWorld, and destructive=false hints. The description adds useful beyond-annotation context: the computation is deterministic, the resource is paid at a specific x402 price, and the intended assurance domain is narrow. No contradiction with annotations exists.

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 three purposeful, front-loaded sentences with no filler. It leads with functionality, follows with usage boundaries, and closes with cost information the agent needs before invoking.

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 no output schema and two underspecified required parameters, so the description must explain what 'descriptive benchmark statistics' means and how inputs map to outputs; it does not. It also fails to clarify how this specific benchmark feed differs from its many similarly named siblings.

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 the undocumented parameters, but it only names them at a high level. It does not clarify how metric_fields relate to observations, whether metric_fields must exist in each observation object, or what constraints apply to values beyond the schema's min/max limits.

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 specific verb ('computes'), target resource ('caller-supplied observations ... numeric fields'), and output type ('deterministic descriptive benchmark statistics'). It clearly distinguishes the outcome-assurance family from reliability/dispute feeds, but it does not differentiate this hash-specific feed from the many sibling outcome_assurance_benchmark_data_feed variants.

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 intended context ('outcome assurance / trust unknown') and explicit when-not-to-use exclusions for legal, identity, sanctions, fraud, contractual, or regulatory adjudication. It does not name an alternative tool for those cases, so it stops short of the top 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds that the computation is 'deterministic' (consistent with idempotency) and notes the x402 price, which is useful but not substantial. It does not describe rate limits, authentication, or any side effects 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?

The description is three concise sentences, front-loaded with the core purpose, then exclusions, then cost. There is no fluff or repetition; every sentence adds information. The structure is clean 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?

Given there is no output schema, the description should explain what the computed statistics are and how they are returned. It only says 'descriptive benchmark statistics,' which is vague—does it return a single summary object, a list, or a structured report? It does not mention pagination, error handling, or the exact nature of the output, leaving an agent with insufficient information to interpret the result 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%, so the description must compensate for parameter meaning. It states 'caller-supplied observations' and 'explicitly named numeric fields,' clarifying that metric_fields are numeric field names. This adds value beyond the raw schema (array of objects and array of strings), but it leaves ambiguity about the structure of observations and does not specify acceptable formats or types for the numeric fields.

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 computes deterministic descriptive benchmark statistics over caller-supplied observations and named numeric fields, and specifies the intended domain (outcome assurance / execution risk). However, it does not differentiate from sibling tools with the same 'outcome_assurance_benchmark_data_feed' prefix (e.g., cdb4549a, f886f033), which likely share the same 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 provides explicit exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication') and mentions it is a paid resource, but it does not explain when to use this tool versus alternative data feeds or how it differs from the sibling benchmark feeds. No alternatives are named, and no positive usage conditions 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 readOnly/openWorld/idempotent/non-destructive behavior, and the description adds useful non-obvious traits: computations are 'deterministic' and the resource is paid at a stated x402 price. It does not describe error behavior for missing numeric fields, but the annotation coverage lowers the bar.

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 carry the full payload: computation, intended usage/exclusions, and pricing. The most important verb-and-resource statement is front-loaded, and no sentence is filler.

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 simple two-parameter compute tool with strong annotations, the description is mostly sufficient, but it lacks an output-shape hint and says nothing about how to choose this specific data-feed variant among many similar siblings. The exclusion list and pricing help, but the agent is still left to infer why this feed vs outcome_assurance_benchmark_data_feed_4936a5f9 or cdb4549a.

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?

With 0% schema description coverage, the description compensates by identifying both parameters: 'caller-supplied observations' maps to observations and 'explicitly named numeric fields' maps to metric_fields. It adds the key semantic that metric_fields must name numeric fields, though it does not cover edge cases like missing fields or nesting.

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 verb ('computes') and resource ('deterministic descriptive benchmark statistics over caller-supplied observations and explicitly named numeric fields'), making its function clear. It does not explicitly differentiate this feed from the sibling outcome_assurance_benchmark_data_feed_* variants, so it stops short of a 5.

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 gives an intended purpose ('outcome assurance / trust unknown') and an explicit exclusion list ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It stops short of naming concrete alternative tools, so the when-to-use guidance is context-rich but not fully comparative.

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 claims the tool is 'deterministic' and operates over 'caller-supplied observations,' while annotations set openWorldHint=true, which indicates interaction with or dependence on the open world. This is a direct contradiction: an agent cannot tell whether the result is a pure function of the supplied arguments or may be affected by external state.

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 short and front-loaded, placing the core computation first and following with intended use, exclusions, and pricing. Each sentence adds useful information, though the phrases 'result unverifiable' and 'at the direct resource URL' are somewhat unclear.

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?

It covers inputs, intended use, exclusions, and cost, which is enough for broad selection and basic invocation. However, with no output schema, no return-value description, and no mention of how missing or invalid numeric fields are handled, an agent still has meaningful uncertainty about the result and failure behavior.

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%, but the description partially compensates by clarifying that metric_fields are the explicitly named numeric fields computed over the caller-supplied observations. It could be more explicit that these fields should be keys within each observation object, but the intended relationship is inferable.

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 operation ('Computes deterministic descriptive benchmark statistics') and identifies the resource ('caller-supplied observations and explicitly named numeric fields'), so an agent can tell what the tool does. It does not differentiate among the several outcome_assurance_benchmark_data_feed sibling variants, so it stops short of a 5.

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 gives an explicit intended context ('outcome assurance / result unverifiable') and explicit prohibited uses ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name an alternative tool for those excluded uses, which keeps it from being fully complete guidance.

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?

Beyond the read-only, idempotent, non-destructive annotations, the description adds the deterministic nature of the computation and the paid-resource cost ($0.002 USD per call). This gives the agent useful behavioral and cost context beyond what annotations already provide.

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 earning its place: the core computation, the intended and prohibited uses, and the pricing. The most important information is front-loaded.

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 purpose, exclusions, determinism, and cost, and annotations cover safety. However, with no output schema, it never explains what descriptive statistics are returned, which is a meaningful gap for an agent deciding whether this tool answers its question.

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 carry the parameter-meaning burden. It does clarify that observations are caller-supplied and metric_fields are explicitly named numeric fields, but it does not explain how metric_fields map to observation object keys, missing-data behavior, or accepted value formats.

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 specific action ('Computes deterministic descriptive benchmark statistics') over caller-supplied observations and named numeric fields, with an explicit intended use case. It does not, however, distinguish among the several outcome_assurance_benchmark_data_feed_* siblings.

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 states when to use it ('Intended for outcome assurance / regulatory mismatch') and provides clear exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name alternative sibling tools, so it stops short of full routing guidance.

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 readOnly, openWorld, idempotent, and non-destructive. The description adds 'deterministic' and the cost structure ('Paid resource; x402 price is $0.002 USD per call'), which are useful beyond annotations. 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?

Three sentences with no redundancy: function, intended use/exclusions, and cost. The main purpose is front-loaded, and each sentence adds new information. Excellent efficiency.

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 core purpose, use cases, and cost, but lacks details about the output format, error handling, or the exact relationship between observations and metric_fields. Given the absence of an output schema, more description of expected returns would be valuable. Still, for a simple benchmark feed, the basics are present.

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 explain the parameters. It only hints at metric_fields via 'explicitly named numeric fields' and never explains the structure or expected content of observations. This is insufficient for an agent to correctly construct inputs.

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 it 'Computes deterministic descriptive benchmark statistics over caller-supplied observations and explicitly named numeric fields,' which is a specific verb and resource. It also narrows the domain to 'outcome assurance / latency excess.' However, it does not differentiate among the several similarly named outcome_assurance_benchmark_data_feed siblings, so it misses the chance to distinguish itself.

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 states when to use it ('Intended for outcome assurance / latency excess') and gives strong negative guidance ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). While it does not name alternative tools, the positive and negative contexts provide clear decision support.

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 carry the safety profile (readOnlyHint, idempotentHint, openWorldHint, destructiveHint=false), lowering the bar. The description adds valuable non-schema context: determinism, the caller-supplied scope of the computation, and the metered cost ($0.002 USD per call at the direct resource URL). No contradiction with annotations — 'deterministic' is consistent with the idempotentHint.

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?

Four tight sentences with the core function front-loaded. The pricing disclosure and prohibition list each earn their place. Minor redundancy: 'outcome assurance' in the intended-use sentence duplicates the tool's own taxonomy, and the six-item prohibition list could be compressed, but there is no filler.

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 two-parameter, compute-only tool with rich annotations, the description covers what it does, over what inputs, in which domain, when not to use it, and what it costs. Since no output schema exists, a note on the return shape would be helpful, and the observations↔metric_fields linkage plus edge-case behavior remain unexplained — but an agent can select and invoke this tool with reasonable confidence.

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, and it partially does: 'caller-supplied observations' clarifies the data source and 'explicitly named numeric fields' clarifies the metric selector. But it never states that metric_fields entries must be property keys present inside the observation objects, nor how missing or non-numeric fields are handled. The compensation is partial at best.

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 ('computes') and resource ('deterministic descriptive benchmark statistics' over 'caller-supplied observations and explicitly named numeric fields'). This clearly distinguishes the tool from verification/orchestration/policy/routing siblings by both function and scope. However, it does not differentiate among the five other outcome_assurance_benchmark_data_feed_* siblings, which share the same domain and appear interchangeable.

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?

Explicitly names the intended use ('outcome assurance / latency excess') and provides a substantial when-not list ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). This is clear contextual guidance, but it falls short of a 5 because it never names an alternative sibling to route to for those excluded use cases.

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?

Adds behavioral detail beyond annotations: 'deterministically' indicates reproducible output, 'reports material differences' describes output, and 'Paid resource; x402 price is $0.002 USD per call' discloses cost. Annotations already cover readonly/idempotent/destructive, so the description's additions are valuable context without contradiction.

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 with no filler: operation, scope/exclusions, cost. Each sentence earns its place and the key function is front-loaded.

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 purpose, exclusions, and cost, but with 5 parameters, 0% schema coverage, and no output schema, it leaves a gap: the meaning of the three optional verification-rule parameters is not explained, and the exact return behavior is only hinted at ('reports material differences'). Sibling differentiation is present through exclusions but not explicit alternatives. 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?

With 0% schema description coverage, the description carries the burden. It names the two required parameters ('expected and observed values') and vaguely refers to 'caller-supplied verification rules' for the optional ignore_paths/required_paths/numeric_tolerance. This adds some meaning but does not explain the semantics of the rule parameters, leaving the agent to infer from names alone.

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: 'Deterministically compares caller-supplied expected and observed values and reports material differences under caller-supplied verification rules.' This clearly names the operation and scope, distinguishing it from sibling verification domains by stating 'Intended for outcome assurance' and excluding legal, identity, sanctions, fraud, contractual, and regulatory adjudication.

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?

Provides explicit when-not exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication') and a positive scope ('Intended for outcome assurance / cannot select'). However, it does not name alternative sibling tools, so the routing guidance is context-rich but not fully explicit about which tool to use instead.

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 adds behavioral context beyond the annotations by describing deterministic comparison, material-difference reporting, caller-supplied verification rules, and the paid price. The readOnly, idempotent, and non-destructive hints are consistent with this read-and-compare behavior.

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 with the core behavior, followed by domain exclusions and cost. The phrase 'Intended for outcome assurance / cannot select' is slightly cryptic, but the overall structure is efficient and no sentence is wasted.

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, no parameter descriptions, and five parameters to clarify, the description is incomplete for an agent that must invoke the tool correctly. It does not describe the return format, how verification rules map to the optional parameters, or how material differences are represented.

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, but it only generically mentions expected and observed values and 'caller-supplied verification rules.' It does not explain ignore_paths, required_paths, or numeric_tolerance, leaving their meaning and expected syntax undocumented.

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 that the tool compares caller-supplied expected and observed values and reports material differences, so the purpose is specific and actionable. However, it does not distinguish this tool from the nearly identically named sibling outcome_assurance_verification_07390ac9 or other verification tools, so it lacks explicit sibling differentiation.

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 intended context ('Intended for outcome assurance / cannot select') and explicit exclusions ('Do not use for legal, identity, sanctions, fraud, contractual, or regulatory adjudication'). It does not name an alternative tool to use instead, which prevents a perfect 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?

Annotations already cover read-only, open-world, idempotent, and non-destructive behavior. The description adds meaningful behavioral context beyond those: the comparison is deterministic, results are reported as material differences, and the verification rules are caller-supplied. It also discloses pricing. No contradiction with annotations was found.

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 efficiently composed of three front-loaded sentences: core behavior first, then exclusions, then cost. There is no unnecessary repetition of schema or annotation content, and every sentence earns 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?

With no output schema, the description should clarify what 'reports material differences' returns, but it does not describe the result shape or error behavior. It also omits details about how ignore_paths, required_paths, and numeric_tolerance interact. Annotations reduce the safety-context burden, but invocation details remain incomplete.

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 clarify that expected and observed are the values being compared and that the optional controls constitute caller-supplied verification rules. However, it does not explain path syntax, how numeric_tolerance is applied, or what forms expected and observed may take, leaving noticeable gaps.

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 first sentence gives a specific, action-oriented definition: deterministically compare caller-supplied expected and observed values and report material differences under caller-supplied verification rules. The outcome-assurance scope and explicit exclusions for legal, identity, sanctions, fraud, contractual, and regulatory adjudication help distinguish this tool from the many 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?

The description explicitly scopes usage to outcome assurance and lists clear when-not-to-use categories. It also discloses that this is a paid resource with a specific per-call price. However, it does not name an alternative tool to use for the excluded adjudication domains, so it falls just short of the strongest possible guidance.

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=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds genuinely useful behavioral context beyond annotations: the operation is deterministic, and it is a paid resource with an explicit $0.002 per-call cost. This is valuable extra disclosure.

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 with no filler, and the most important framing ('Paid deterministic') is front-loaded. The only minor redundancy is mentioning 'Paid' twice, first in the opening phrase and again as 'Paid resource; x402 price...', but overall the description is tight and structured.

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—4 parameters, nested objects, an enum, no output schema, and a name suggesting policy-versus-control behavior—the description is incomplete. It omits the meaning of mode, policy, protocol, offers item structure, and the result/ranking return shape. An agent could understand the high-level purpose but would still be unsure how to 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?

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'offers' as caller-supplied candidates and names the constraints applied, but it does not explain the mode enum (policy vs. control), the policy object structure, the protocol parameter, or the expected shape of the offers array. This is insufficient for constructing a valid call.

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 a specific verb ('applies', 'ranks') and a specific resource ('caller-supplied candidate offers'), and it states the exact constraints enforced: maximum price, minimum reliability, minimum trust, and maximum latency. It does not explicitly differentiate from siblings like procurement_policy_engine_692ceeb3 or procurement_router, but it is specific enough to convey what the tool does.

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 no when-to-use guidance, no exclusions, and no mention of alternatives. It says the tool is paid and acts on caller-supplied offers, but it never helps an agent decide between this tool and the many similar procurement/policy sibling tools.

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 signal read-only, idempotent, and non-destructive behavior abstractly. The description adds concrete side-effect boundaries ('does not execute purchases'), pricing ('$0.002 USD per call'), and an optional telemetry enrichment behavior, which goes beyond what annotations alone 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?

The description is compact and front-loaded: it states the core purpose in the first sentence, then quickly covers ranking behavior, exclusions, and pricing. Every sentence adds distinct value without unnecessary elaboration.

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 0% schema coveragehol and no output schema, the description leaves significant gaps: the offer object shape is unspecified, the protocol parameter is unexplained, and the return value is not described. The read-only and pricing context helps, but an agent still lacks enough information to call this correctly without external knowledge.

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, but it only explains the high-level concept of 'caller-supplied offers' and ranking criteria. It does not clarify the structure of each offer, the meaning of 'protocol', or the exact role of max_price_usd and min_reliability beyond loose inference from the ranking criteria.

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 a specific verb and resource: it 'ranks caller-supplied offers' by named criteria (price, reliability, latency, trust). It also distinguishes itself from purchase-executing tools by explicitly saying it does not execute purchases, which helps differentiate it among the many routing/control siblings.

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 usage for offer ranking and says it does not execute purchases or provide legal advice, which gives some boundary. However, it does not explicitly state when to use this tool over sibling alternatives like selection_routing_router_* or procurement_policy_control, nor does it mention prerequisites or integration context.

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 report readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds meaningful behavior beyond these: it validates structure without 'claiming external truth,' which matches and explains the open-world hint, and it surfaces the paid x402 price. It does not contradict 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?

Three sentences, each earning its place: capability, use/exclusion guidance, and pricing. The key functional behavior is front-loaded before limitations, and there is no filler.

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 no output schema and an underspecified input schema, so the description must compensate for both return-value shape and record structure; it does neither. It explains intent and exclusions well, but an agent still lacks enough detail to reliably build the `records` input or interpret results.

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?

With 0% schema description coverage, the description carries the burden of explaining the parameter, but it only repeats the conceptual idea of 'provenance records' and 'provenance DAG continuity.' It never specifies the shape of each array element (e.g., node ID fields, parent references, timestamps), leaving a caller unable to construct a valid `records` payload from the description alone.

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 specific verb and resource: 'Validates caller-supplied provenance DAG continuity' and enumerates concrete anomaly classes (missing parents, forks, cycles, duplicate nodes, ordering anomalies). This clearly distinguishes it from a generic provenance checker, especially by contrasting with 'merely fingerprinting individual entries.'

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 gives explicit when-to-use conditions ('when... form a coherent chain/dag rather than merely fingerprinting individual entries') and a when-not-to-use scope (unrelated general knowledge, unsupported legal/identity/sanctions/fraud/contractual/regulatory adjudication). It does not name specific sibling alternatives, so it misses the strongest form of differentiation.

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=true, idempotentHint=true, destructiveHint=false, so the safety profile is covered. The description adds meaningful behavioral context: it is deterministic, computes before execution, and is a paid resource with a specific price ($0.002 USD per call). This goes beyond the annotations and helps the agent understand cost and execution semantics.

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 front-loaded with the core purpose. The exclusion and pricing info are useful but the sentence 'Do not use it for unrelated general knowledge or unsupported legal, identity, sanctions, fraud, contractual, or regulatory adjudication' is somewhat boilerplate and could be trimmed. Still, every sentence earns 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 planning tool with no output schema and 0% parameter coverage, the description gives a good sense of what it computes but leaves the agent without details on input structure or return format. The pricing and determinism notes help, but the missing parameter semantics and output description create a clear 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%, and the description does not explain the 'calls' or 'quotas' parameters beyond their names. The description mentions 'per-provider demand, quota headroom and bottleneck calls' but does not map these to the input parameters. With zero schema coverage, the description must compensate, and it does not.

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 specific verb ('Computes'), a clear resource ('per-provider demand, quota headroom and bottleneck calls'), and a precise scope ('before execution'). It distinguishes itself from siblings by naming its deterministic capacity-planning role, which is unique among the listed 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?

Explicitly states when to use: 'when determine whether a planned agent workload fits known provider quotas and identify the exact quota bottleneck before requests are sent.' It also gives a clear exclusion: 'Do not use it for unrelated general knowledge or unsupported legal, identity, sanctions, fraud, contractual, or regulatory adjudication.' This is strong routing guidance.

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 cover read-only, idempotent, open-world, and non-destructive behavior. The description adds valuable context beyond that: deterministic computation, caller-supplied data, 'result unverifiable', and the paid cost of $0.002 per call. It does not contradict 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?

The description is compact and front-loaded with the core function, followed by usage restrictions and cost. Every sentence carries useful information 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?

The description provides purpose, restrictions, determinism, and cost, but with no output schema it leaves return-value semantics underspecified. It also does not state what descriptive statistics are produced or how metric_fields must relate to observations, both of which an agent would need for correct invocation.

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 identifies 'observations' as caller-supplied observations and 'metric_fields' as explicitly named numeric fields, which adds basic meaning. However, it does not clarify how metric_fields map to observation keys, what shape observations must take, or what value constraints apply 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 states a specific verb ('computes'), resource ('descriptive benchmark statistics'), and scope ('caller-supplied observations and explicitly named numeric fields'). This clearly differentiates the tool from the control-plane and verification siblings while matching the benchmark data feed family.

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 states the intended domain ('reliability quality / result unverifiable') and lists prohibited use cases (legal, identity, sanctions, fraud, contractual, regulatory). It does not name alternative tools, but the when-not guidance is strong enough to steer an agent away from misuse.

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