Andreax x402 Services

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

With no annotations, the description must carry behavioral disclosure. It does reveal the workflow nature, expected output components, and a pay-per-use cost signal. However, it does not explain output format, whether the tool is interactive, or what happens after the goal is submitted.

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 very compact and front-loaded, with the core purpose stated first. The B2B/CRO and pricing fragments add context without padding, though the fragment style is somewhat telegraphic.

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 one-parameter tool, the description covers input, purpose, expected output components, and cost. But with no output schema and no annotations, more detail about the delivered design format would make the tool safer and easier to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema's only parameter description is the generic 'service input', so the description's 'input=goal' adds a useful semantic pointer. Still, it is minimal and does not specify how detailed or structured the goal should be.

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 identifies the tool's verb and resource: designing rigorous A/B tests with hypothesis, variants, and success criteria. It is specific enough to distinguish it from the broad sibling list, though it does not explicitly name an alternative tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus alternatives like custom-workflow-design or research tools. The line 'B2B: CRO teams run clean experiments' gives audience context but no actionable selection criteria or exclusions.

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

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

Annotations are absent, so the description carries the full burden. It mentions 'ENTERPRISE' and pricing, which are commercial signals rather than behavioral disclosure. The phrase 'full technical blueprint' indicates the high-level output, but there is no detail about return format, limitations, or operational 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 front-loaded: the core purpose appears first, followed by the input expectation and pricing. There is no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no output schema, the description is minimally workable. However, 'full technical blueprint' is vague about what the returned blueprint actually contains, and there is no guidance on expected output structure or 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?

The schema already documents the single 'input' parameter at 100% coverage, but the description adds meaning by stating that the input is a 'goal'. This clarifies the purpose of the parameter beyond the generic 'service input' schema 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 states that the tool produces a 'full technical blueprint for a custom AI agent/workflow' from an input goal. This identifies the deliverable and the core resource, though it does not explicitly differentiate it from related siblings such as autonomous-workflow or strategy-plan.

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 only usage clue is 'input=goal', which implies the tool is goal-driven, but there is no guidance about when to choose this tool over alternatives or when not to use it. No exclusions, prerequisites, or comparisons to sibling tools are provided.

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

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

With no annotations, the description carries the burden and does add meaningful behavior: it is pay-per-use at 15 USDC on Base via x402 and takes crop+region. It does not disclose output format, error behavior, or auth/rate-limit details, so it is only partially transparent.

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 extremely compact and front-loaded with the vertical and use case, then pricing. Every clause earns its place; 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 single-string-parameter tool with no annotations and no output schema, the description covers the required input, use case, and pricing/output nature (demand/price data + sources). It stops just short of full completeness by giving no example value or explicit response shape.

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, 'input', has a tautological schema description ('service input'), so the description's 'input=crop+region' is the essential semantic and enables invocation. It could be stronger with a concrete example or delimiter, but it far exceeds 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 identifies the tool as agricultural market intelligence with sources and ties it to crop+region input. The vertical tag and 'B2B: agri-exporters decide with demand/price data' differentiate it from generic siblings like market-intelligence/market-data, though it lacks a direct action verb like 'get' or 'provide'.

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 a clear use context: B2B agri-exporters needing demand/price data for a crop+region. It does not explicitly name when not to use it or point to an alternative sibling, but the vertical scope is enough to route an agent.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It discloses a real cost ('100.0 USDC on Base, pay-per-use'), the depth of output ('deep, cited'), and an input scope ('input=optional scope'). These are genuine behavioral traits beyond what the schema shows. However, 'input=optional scope' conflicts with the schema's required flag, which is a mild transparency concern, but not an 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?

The description is compact, front-loaded with the core 'INDUSTRY REPORT: ...' label, then adds scope, audience, and payment in short segments. Every clause carries information, and there is no filler. It is terse to the point of being cryptic, but structurally it is efficient and appropriately sized for a micro tool 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?

The description covers the report topic, audience, scope input, and payment, which is a good baseline for a single-parameter tool. However, it does not explain the output format, language, or expected structure of the generated report, and the input parameter remains ambiguous ('optional scope' with no example). Given that the tool charges 100 USDC, more guidance on what to provide and what to expect would be valuable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% but the only property is described unhelpfully as 'service input'. The description adds that the input is a 'scope', which gives some meaning beyond the schema. Yet this guidance is thin—no examples of scope, no format details—and its claim of 'optional' contradicts the schema's required field. This is a minimal-value addition over a poorly informative 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 opens with 'INDUSTRY REPORT: El mercado de AI agents 2026 (deep, cited)' which clearly identifies the resource: an industry report on the 2026 AI agents market. It adds target audience ('VCs y equipos de estrategia') and depth/citation expectations, making it distinguishable from generic sibling report tools. However, the action is implied by the noun phrase rather than stated as an explicit verb like 'generates' or 'provides'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'B2B: VCs y equipos de estrategia dimensionan el mercado de agentes IA' gives a clear context: this tool is for VCs and strategy teams sizing the AI agents market. It implies when to use it and what problem it solves. It does not name alternatives or exclude other cases, but the audience/task framing is specific 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?

With no annotations, the description carries the transparency burden. It does disclose that the output is a deep, cited industry report and that it is pay-per-use at 150 USDC on Base. However, it never describes the output format, data sources, or behavior when scope is omitted, and it claims input is optional while the schema marks it required.

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 deliverable ('INDUSTRY REPORT'). The fragment style and pricing bracket are terse, but there is no wasted prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter report generator with no output schema, it conveys the topic, depth, citation, target audience, and cost. It is incomplete on what 'scope' can contain, what the returned report looks like, and which jurisdictions/regulations are covered; the optional-vs-required mismatch also leaves an actionable 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?

The schema covers the single 'input' parameter 100%, but only describes it as 'service input.' The description adds that it represents an optional scope for the report, which is useful meaning; the 'optional' wording conflicts with the required flag, slightly reducing reliability.

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 the tool produces an industry report on global AI compliance, described as deep and cited, targeting B2B legal/compliance teams. The 'global' scope helps separate it from local compliance siblings, but it never names or directly contrasts them, 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?

It gives a clear context: B2B legal/compliance teams planning global AI compliance use this tool, and the input is an optional scope. It does not say when not to use it or point to alternatives like compliance-audit or enterprise-compliance, so it misses explicit exclusions.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It responsibly reveals that the tool is pay-per-use and includes pricing on Base (0.002 USDC), which is meaningful. However, it does not describe return behavior, failure modes, or any side effects beyond cost.

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 very short and front-loaded with the primary action. Minor redundancy: 'pay-per-use' appears twice, and the bracketed x402 note restates the parenthetical cost detail.

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?

This is a simple one-parameter tool with no output schema and no annotations. The description includes cost details, which is useful, but it leaves the expected return format implicit ('answer' suggests text but is not explicit) and gives no information about model behavior or 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 coverage for the single 'input' parameter is 100%, so the schema already defines it as a service input. The description adds only the loose synonym 'your query', which gives marginal semantic context but not substantial new 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 clear action ('Model answer to your query') and identifies the resource as pay-per-use AI compute. The core purpose is understandable, though it does not explicitly differentiate from the sibling tool 'inference', which is ambiguously close in name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to prefer this tool over alternatives. The pricing info hints at cost considerations, but no when-to-use, when-not-to-use, or alternative tool mentions are provided.

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?

There are no annotations, so the description carries the behavioral burden. It does disclose a significant behavioral trait: x402 pay-per-use at 50.0 USDC on Base. It also frames itself as a workflow. However, it does not explain output form, data handling, or any side effects beyond the payment mechanism.

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, leading with the workflow type and input definition. The B2B sentence adds context but is somewhat vague; the pricing note is compact and useful. Overall, every line contributes.

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 one vague parameter and no output schema, so the description must compensate, but it does not explain the expected output format, what 'year data' should contain, or the workflow steps. It is enough to identify the tool's purpose but not fully enough 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 schema only describes the parameter as 'service input', which is generic and unhelpful. The description adds real semantic value by specifying that the input is 'year data', giving the agent meaningful guidance on what to provide even if the exact format is not detailed.

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's purpose: producing a corporate annual report from the user's own data, with 'year data' as the input. It is more specific than generic report siblings, though 'complete' is slightly ambiguous between generating and finalizing.

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 that the input is year data and frames the use case as B2B annual report production. However, it gives no explicit guidance on when to choose this tool over alternative report workflows or what conditions make it inappropriate.

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?

No annotations are provided, so the description carries the full disclosure burden. It only reveals cost and that the input is a transcript; it does not state whether the operation is read-only, what the output looks like, whether audio can be submitted despite the tool name, or any auth/rate 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 short and front-loads the core purpose, but it relies on unexplained shorthand ('CONSUMER:', 'x402', 'input/transcript=text') that may reduce clarity for an agent.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema and no annotations, the description should explain the return value and operational behavior more fully. It omits output format, does not clarify whether audio is accepted despite the name, and gives no example, leaving an agent to guess at the result of calling this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only describes the parameter as 'service input', which is uninformative. The description adds real meaning by specifying 'input/transcript=text', telling the agent to pass the transcript text as the single input.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action and resource: 'clean + summarize a transcript', and clarifies the input is transcript text. It is not a tautology, but it doesn't differentiate from siblings like transcribe or summarize, and the 'audio-to-text' tool name is not reconciled with the text-input behavior.

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 guidance is given about when to choose this tool over alternatives such as transcribe or summarize. No exclusions, preconditions, or alternative tool names are mentioned; the only usage context is that it is a paid 'CONSUMER' service.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose the pay-per-use pricing model and the x402 payment mechanism, which is useful context for an agent. However, it does not explain what 'autonomous' means in practice, what the output looks like, or whether the plan is merely generated or also executed.

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 entire description is one compact sentence that front-loads the tool's nature and then provides input and pricing details. There is no filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool, the description provides the essential input contract and pricing, which may be enough to invoke the tool. However, given the large sibling set and absence of annotations/output schema, it leaves unclear what the returned plan contains, how it is delivered, and when a simpler tool would be more appropriate.

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 describes the parameter only as 'service input', which is essentially vacuous. The description adds meaningful semantics by specifying that the input should be a goal, going beyond the schema and helping the agent construct a correct invocation.

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 identifies the deliverable as an 'executable step-by-step autonomous workflow plan' and defines the input as a goal, which is more specific than the tool name alone. It does not explicitly contrast itself with siblings, but the wording is concrete enough to position it against generic planning or research 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?

No explicit guidance is given about when to use this tool versus alternatives such as strategy-plan, content-pipeline, or deep-research-report. The only usage signal is 'input=goal', which implies applicability but does not state selection conditions, exclusions, or prerequisites.

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?

No annotations are provided, so the description carries the burden. It discloses that the report is deep, cited, and pay-per-use via x402, which adds useful behavioral context. However, it does not describe return format, citation style, limitations, or any operational side effects.

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 audience and pricing context. It packs several useful facts into one line, though the mixed-language and label-heavy style is slightly unstructured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter report tool with no output schema or annotations, the description gives the subject and audience but leaves key context unclear: how to specify the optional scope, what the output looks like, and how this differs from the many sibling report and research tools.

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 covers the single input parameter, but only as 'service input,' which is unhelpful. The description adds meaning by calling it 'optional scope,' but this directly conflicts with the schema marking it as required, making the parameter semantics confusing rather than clarifying.

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 produces an industry report about the Base L2 ecosystem, with depth and citations. It identifies the subject matter and target B2B audience, though it does not explicitly distinguish itself from sibling report tools like market-sector-report or deep-research-report.

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 clear usage context: builders and investors evaluating opportunities on Base. It implies the input is an optional scope specification, but it does not name alternatives or provide when-not-to-use 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?

With no annotations, the description carries the disclosure burden and does add useful behavioral facts: it is a one-call multi-stage workflow, produces a publish-ready post, and is pay-per-use at 15.0 USDC on Base. However, it omits the output structure, payment flow, and any limitations, so transparency is only partial.

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 wasted words: the workflow is front-loaded, followed by the target user, output promise, and pricing. Every segment 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 definition covers the essential invocation facts for a one-parameter tool: input topic, pipeline components, target user, and cost. But with no output schema, it leaves an agent unsure about the exact return shape (single article versus separate SEO meta fields) and how x402 payment is handled, so it is adequate with clear 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?

The schema parameter description is a generic placeholder ('service input'), so the tool's 'input=topic' is what actually tells the agent what to pass. This is meaningful added semantics, though it could specify topic form or length.

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 the tool assembles research, headlines, article, and SEO meta into a publish-ready blog post from a topic in one call. This clearly identifies the resource and workflow, and the combination of stages distinguishes it from single-purpose siblings such as headlines or seo-article, though it never names an alternative explicitly.

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?

"B2B: content teams ship a publish-ready post" gives a clear target audience and use case, and the workflow label signals when the full pipeline is wanted. It does not list exclusions or explicitly compare to content-pipeline, but the context is strong 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?

With no annotations, the description carries the burden. It does add useful behavioral context by disclosing the exact price ('3.0 USDC on Base') and pay-per-use model. However, it does not clarify whether the article input should be a URL, plain text, or markdown, nor does it explain response behavior or 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 every part earns its place: purpose, input expectation, and pricing. Key information is front-loaded, 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?

Given the single parameter and lack of an output schema, the description covers the core transformation and pricing but still leaves ambiguity around the input format and exactly how results will be returned. It is sufficient for a straightforward tool, though 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?

The schema's input description is generic ('service input'), so the description adds meaning by specifying 'input=article'. This clarifies the expected semantic of the single parameter, going beyond the schema's minimal documentation.

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 ('repurpose'), a clear resource ('1 article'), and concrete outputs ('thread+LinkedIn+newsletter+TLDR'). This makes it easily distinguishable from generic siblings like summarize or rewrite-formal.

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 implies when to use it: when you have a single article and want multiple repurposed formats. It does not explicitly state exclusions or alternatives, but the SMB framing and format list give adequate context for 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?

With no annotations, the description carries the disclosure burden. It reveals that the service is enterprise-grade, pay-per-use at 30.0 USDC on Base, and expects brand input. However, it does not state what the tool returns, whether any authentication or internal calls are involved, or how it behaves beyond generating a brand strategy.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact line that front-loads the purpose and packs in useful operational signals such as enterprise level, input expectation, cost, network, and payment model. There is no redundant or filler content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a pay-per-use tool with no output schema and no annotations, the description is thin. An agent cannot tell what the response looks like, what input format is required, or what edge cases may occur. More detail about expected output and invocation prerequisites would be necessary for confident use.

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 only describes the parameter as 'service input', so the description meaningfully adds that the input should be a brand. However, it leaves the exact format ambiguous — whether it accepts a brand name, a brief, or a full description — so it adds only minimal semantic 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 identifies the resource as 'brand strategy' and lists the specific components it covers: positioning, messaging, voice, and naming. It distinguishes itself from strategy-plan and gtm-strategy by the brand focus, even though no explicit comparison is made.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus alternatives such as gtm-strategy or strategy-plan. The 'input=brand' note hints at expected input, but no usage context, exclusions, or alternative routing is provided.

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?

No annotations are present, so the description carries the full burden. It does disclose meaningful operational context: this is an 'ENTERPRISE' service, costs 40.0 USDC on Base, and is pay-per-use. It does not explain output format, delivery, or any caveats, but the generation behavior is reasonably clear from 'complete business plan from an idea.'

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 very short and front-loads the core purpose before adding pricing and input details. Every clause adds value, though the terse 'input=business idea' style could be slightly more natural.

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 one-parameter generation tool, the description covers the essential trigger, input, and cost. However, it does not specify what a 'complete business plan' contains, how long the output is, or whether any additional context is expected from the user. It is minimally viable but leaves several agent-relevant details unclear.

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 only says 'service input' with 100% coverage, which is generic. The description compensates by specifying 'input=business idea,' giving the single parameter concrete meaning. That goes beyond what the raw schema alone provides.

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 clear verb-plus-resource pattern: 'complete business plan from an idea.' It states exactly what the tool produces and what input it needs. It doesn't explicitly differentiate from siblings like strategy-plan or investor-deck-copy, but the business-plan focus is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to prefer this tool over similar siblings such as strategy-plan, proposal-draft, or brand-strategy. The 'input=business idea' hint implies the use case, but no exclusions or alternative conditions are provided.

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

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

With no annotations provided, the description must carry the behavioral burden. It does add a pay-per-use cost indicator ([x402: 0.002 USDC on Base]) but doesn't disclose any classification behavior details such as return format, model label likelihoods, or limitation of persisting the request. The behavioral profile remains largely under-specified.

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-loads the central classification function. The pricing mention takes away a little focus but is functional, and 'input=text' repeats the schema slightly. It still remains useful and direct.

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 simplicity (one parameter, no output schema), the description covers the call's intent, and mention of cost provides an important consideration. However, it does not explain what kinds of categories are returned, how output is structured, or how a caller might use the response, leaving some ambiguity for the agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% because the input field is described as 'text'. The description itself only restates 'input=text', adding no new semantic beyond the schema. This meets the baseline for full schema coverage without adding meaningful enrichment.

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 and resource: 'Classify a text by topic/category.' This clearly identifies the tool as a classification model and distinguishes it from sibling tools like sentiment, entities, or translate, which address different text-processing tasks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'High volume for routing/tagging' gives a clear when-to-use context, but no explicit guidance about when not to use this tool or which sibling might be a better alternative for classification of other properties. Some inference is still required from the agent.

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?

No annotations are provided, so the description must carry behavioral disclosure. It adds pay-per-use pricing ('0.1 USDC on Base') which is useful, but it does not mention output format, limitations, side effects, or what 'basic improvements' looks like. With no annotations and no return schema, this is a significant gap.

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 extremely short and front-loaded with action, input, and pricing. There is no fluff. It is telegraphic but effective for a simple one-parameter tool, though a fully structured sentence might slightly improve readability.

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 one-parameter tool, the essentials are present: what it does, what to provide, and cost. However, it lacks output expectations and any disambiguation from several similar siblings, which makes it minimally viable 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 coverage is 100%, but the schema's parameter description is generic ('service input'). The description adds real meaning by specifying 'input=code,' clarifying that the single parameter expects code. This goes beyond the schema's vague placeholder.

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 states a specific verb+resource: 'explain code + basic improvements.' The 'CONSUMER:' prefix hints at intended audience, and 'input=code' reinforces the scope. It is clear enough though it does not explicitly differentiate from sibling tools like 'code-review' or 'explain.'

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 guidance on when to use this tool versus alternatives such as code-review, code-review-security, code-gen, or explain. The description implies usage ('input=code') but provides no context for selection or exclusions.

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

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

There are no annotations, so the description must carry the behavioral disclosure burden. It communicates the input and makes the generation intent clear, but it does not say whether the code is returned directly, whether it is executed, or what exact output format is expected. The pay-per-use pricing is useful operational context.

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, front-loaded with the core purpose, and avoids rambling. The pricing and target-user clauses are additional but compact. It is efficient though not as tightly minimal as it could be if pricing were considered pure metadata.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool, the description and schema together cover what is required to invoke it correctly. It specifies the intended input, the target audience, and the cost. The output format is not explicitly described, but the phrase 'generate code' strongly implies the result, so nothing necessary is 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?

The schema already documents the only parameter 'input' at 100% coverage, including a Spanish description of what the code should do. The description's 'input=what it should do' restates this meaning without adding examples, syntax, or additional semantic detail. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Uses a specific verb and resource: 'Generate code from a description.' This clearly differentiates it from siblings like code-review and text-to-sql, and the input explanation reinforces 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?

Provides practical context by saying it is 'for agents/devs' and defines the input as 'what it should do.' However, it does not explicitly state when to choose this tool over the many code-related sibling tools, nor does it list any exclusions.

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

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

The description discloses that this is a premium pay-per-use tool with pricing ([x402: 0.02 USDC on Base]) — a significant behavioral trait beyond the bare name. It also clarifies output scope (bugs, security, improvements). With no annotations at all, the description carries the behavioral burden and does substantial work.

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?

One compact sentence with scannable pricing info. It's structurally fine, but the Spanish/Portuguese parameter description ('código') and the mixed-language preamble 'PREMIUM' and x402 syntax add minor noise. Still overall compact.

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 provides enough to call it: input is code, output is review, pricing is disclosed. It doesn't say what format the output comes back in, what language constraints apply, or the max input size, but those are likely obvious from the tool's nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%; the single parameter 'input' is documented as 'código' in the schema. The description adds 'input=code' which reinforces the same information. Baseline 3 is appropriate since the schema already fully covers the parameter, and the description contributes redundancy rather than new 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 clearly states 'code review (bugs, security, improvements)' and the input is code. It's a specific verb-resource pair that distinguishes it from siblings like code-gen, explain, proofread, etc.

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 says 'For devs/companies' and 'input=code' which implies when to use it — when you have code to review. It doesn't explicitly exclude alternatives among the many siblings, but the code-review purpose is relatively clear given the sibling list.

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?

No annotations are provided, so the description carries the full burden. It does disclose useful behavioral traits like pay-per-use, cost in USDC on Base, and severity-based output ordering. However, it does not describe the response shape, processing behavior, or data-handling implications of sending code to the service.

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 main purpose and pricing. The 'PREMIUM' tag and bracketed payment details are slightly noisy, but nearly every token adds useful information for selection and invocation.

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 single-string input tool, the description conveys the core purpose and input type. However, there is no output schema, so the description should more explicitly state what the tool returns; 'by severity' only hints at the result structure. It is adequate but has clear 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?

The schema only describes the parameter as 'service input', which is generic. The description adds meaningful semantics with 'input=code', telling the agent what kind of content to supply. It does not specify language or format, but it substantially clarifies the otherwise vague parameter.

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: a security-focused code review, with output organized by severity. The 'security-focused' qualifier distinguishes it from the closely related sibling code-review tool, so an agent can select it without opening the schema.

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?

Usage context is implied: choose this tool when a security-oriented code review is needed. However, there is no explicit when-to-use or when-not-to-use guidance, and the sibling code-review tool is not mentioned as an alternative.

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

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

With no annotations provided, the description carries the full burden, and it does disclose the sequential chaining behavior and the pay-per-call cost ('0.01 USDC on Base, pay-per-use'). However, it does not address output format, side effects, failure behavior, or what happens at each stage beyond the chain order.

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-loads the pipeline chain before cost details. It slightly repeats the pay-per-call concept with both 'pay-per-call' and 'pay-per-use', so it is not entirely waste-free, but it is appropriately brief.

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 multi-stage composite tool with no output schema and no annotations, this description is too thin. It names the chained tools but does not explain what the pipeline input should be, what result is ultimately returned, or why language-detect is the final stage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already covers the single 'input' parameter at 100% with the phrase 'pipeline input', so the baseline is 3. The description confirms the input feeds into the chained pipeline but adds no detail about input format, expected content, or stage-specific usage.

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 pipeline of tools ('competitor-scan → market-data → email-campaign-pipeline → language-detect'), which makes it clear what the composite tool chains and distinguishes it from sibling combo tools. It lacks a true verb+outcome statement, so it is not a 5, but an agent can understand the tool's composition.

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 when-to-use or when-not-to-use guidance is given. The chain implies a competitor/market-analysis-to-email workflow, but the description never explains when to choose this combo over running the individual tools or selecting combo-gtm-email-crypto-2e0abb.

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

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

With no annotations, the description must carry behavioral disclosure. It usefully reveals the pay-per-call nature, x402 payment method, 0.01 USDC cost on Base, and the chaining order. However, it does not disclose side effects, failure behavior, whether the input is transformed, or what the composition returns.

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 very short and front-loaded: it states the pipeline composition first and the cost second, with no filler. It is concise, though it sacrifices necessary explanatory detail for brevity.

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 composed tool with pay-per-call implications, no annotations, and no output schema, the description is insufficiently complete. An agent cannot reliably know what input to provide, what the output looks like, when to choose this over its components, or what happens when one stage fails.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents the single parameter. The description adds no meaning beyond the schema's vague 'pipeline input' label, so the baseline of 3 applies.

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 composed pipeline ('discrepancy-detector → agtech-market') and indicates it is a chained tool, which is more than a tautology. However, it never states what the tool actually accomplishes or what output the agent should expect, so the purpose remains vague and not clearly distinguishable from calling the two sibling tools separately.

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 guidance is provided on when to use this composed tool versus the individual discrepancy-detector and agtech-market tools, or versus other combo siblings. The pay-per-call x402 note is pricing information, not usage 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?

With no annotations, the description carries the full burden of behavioral disclosure. It usefully reveals that the tool is pay-per-call via x402, costing 0.01 USDC on Base. However, it does not disclose side effects, authentication requirements, failure modes, or whether the underlying tools mutate data.

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 one compact sentence plus a pricing note, with no filler. The core composition information is front-loaded, and the critical pay-per-use cost is included without bloating the text.

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 having only one parameter and no output schema, the description leaves important gaps: it does not specify what the pipeline returns, what the input should contain, or when this combo should be selected over its component tools. The cost and chain are helpful but insufficient for confident 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 already describes the single parameter as 'pipeline input' with 100% coverage. The description adds no further meaning about what format or content the pipeline input should take, so it does not improve on 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 process—'chaining gtm-strategy → email-polish → crypto-payments-2026'—and clearly identifies the tool as a composed pipeline rather than a single operation. It distinguishes itself from individual sibling tools by naming its three components, though it does not describe the final deliverable.

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 guidance is given about when to use this composed tool versus running the individual sibling tools (gtm-strategy, email-polish, crypto-payments-2026) separately. The description implies a pipeline use case but does not state exclusions or alternatives.

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

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

With no annotations, the description carries the full behavioral burden. It does disclose that this is a PREMIUM pay-per-use tool with a fixed price of 0.15 USDC, which is useful context. It also indicates the output includes a recommendation, but it does not explain return structure, limits, data sources, or failure 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 packs purpose, input, output, and pricing into a single sentence. It is not perfectly front-loaded because 'PREMIUM (business)' appears before the main action, but there is minimal waste and no redundant expansion.

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, reasonably simple tool, the description gives the key information a caller needs: what it analyzes, what input it expects, and what kind of output it produces. However, there is no explicit guidance on output structure or how to interpret the recommendation, and no alternative routing; an agent might still need to discover behavior at call time.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents the single parameter. The description repeats the input value by saying 'sector/company', which adds context but no new schema-level semantics. The baseline of 3 is appropriate given full 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 clearly identifies the purpose: it performs a competitive analysis of a sector or company and provides a recommendation. There is enough specificity to distinguish it from generic research or reporting tools, though it does not explicitly contrast with siblings like risk-analysis or strategy-plan.

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 intended use case is implied by 'competitive analysis of a sector/company with a recommendation' and the input hint 'input=sector/company'. However, there is no explicit guidance on when to prefer this tool over similar siblings such as research, report, or strategy-plan, and no when-not-to-use 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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the tool is a 'WORKFLOW' and pay-per-use, and mentions output with sources, but it fails to state side effects, authentication needs, output format, or whether the operation is read-only. For a tool with no annotations, this is a significant gap.

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 workflow: 'weekly competitor movements + actions, with sources.' The B2B context and pricing note are relevant, though the 'WORKFLOW:' prefix and compressed 'input=competitors' phrasing read as jargon and could be clearer.

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 one generic string parameter, no annotations, and no output schema, the description leaves essential questions unanswered: how to format competitor input, what the report/output looks like, when this is preferred over sibling tools, and what the workflow steps are. It is not complete enough for an agent to invoke confidently without further clarification.

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 only has a generic 'service input' description, so the description's 'input=competitors' adds meaningful semantic guidance about what to pass. Still, it does not explain format, structure, or examples, so it is helpful but not fully specific.

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 identifies a specific resource ('competitor') and action ('weekly competitor movements + actions, with sources'), and it states the intended audience ('B2B: strategy teams stay ahead'). However, it does not differentiate this tool from sibling tools like competitor-scan or competitive-analysis, so it stops short of 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 implies use cases: weekly competitor monitoring with sources, useful for B2B strategy teams. It does not explicitly state when to use this tool over alternatives, nor does it provide exclusions or conditions, so guidance is only implied rather than precise.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It does disclose a meaningful behavioral trait: pay-per-use at 5.0 USDC on Base via x402, along with the expected output components. However, it does not mention output format, external data requirements, or whether this is an LLM-generated report, leaving partial 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?

The description is one compact line structured as purpose, input, and pricing. Every segment adds distinct information, and the core purpose is front-loaded before cost metadata. There is no 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?

The description covers the essential invocation contract: one input parameter, the major output categories, and the cost model. However, with no output schema and no sibling differentiation, an agent is left to infer the exact return format and when to choose this over similar-looking tools, so completeness is only moderate.

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 description for 'input' is essentially a tautology ('service input'), but the tool description adds real meaning by saying 'input=company', telling the agent exactly what entity to provide. It stops short of giving an example or value format, but for a single free-text parameter this is a meaningful improvement over 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 deliverable set ('competitor research + SWOT + positioning + how-to-win') and the input ('company'), so an agent can understand the core function. It does not explicitly contrast itself with siblings such as competitive-analysis or market-research-report, 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?

Usage must be inferred from the 'SMB:' prefix and the listed research outputs; there is no explicit 'when to use' or 'when not to use' guidance. Because several sibling tools (competitive-analysis, market-intelligence, deep-research-report) overlap significantly, the lack of routing direction is a noticeable gap.

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?

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the significant cost trait: '20.0 USDC on Base, pay-per-use.' It also indicates output is organized 'by severity.' However, it does not say anything about data handling, input limits, or whether the operation is strictly read-only.

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 one compact line with no filler: it opens with the premium/cost signal, states the purpose, and names the input type. Every segment contributes information, and the cost warning is placed prominently.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description covers the essential operational facts: what to pass, what kind of result to expect, and that it is a paid call. It lacks an explicit return-format description and alternative routing, but the low complexity keeps this close to sufficient.

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's only parameter is described merely as 'service input,' which is uninformative. The description repairs this by saying 'input=document/policy,' giving the agent the actual semantic expectation. This adds meaningful guidance beyond the schema despite the 100% schema description 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 function: 'compliance/legal-risk audit by severity.' This adds the legal-risk scope and severity output on top of the tool name. It is not a pure tautology, though it does not explicitly distinguish itself from the sibling 'risk-analysis' tool.

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 one usage hint, 'input=document/policy,' but does not state when to prefer this tool over alternatives like risk-analysis, contract-draft, or fact-check. There is no when-to-use or when-not-to-use guidance, so an agent must guess which sibling covers which scenario.

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

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

With no annotations provided, the description carries the behavioral transparency burden. It discloses that the tool compresses input, produces a fewer-token version, and has a pay-per-use cost of 0.001 USDC on Base. But it does not describe potential quality trade-offs, whether meaning can be lost for very short prompts, or what exactly happens in edge cases, so there is still a meaningful gap.

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 well-structured, front-loading the core purpose and expected savings, then giving the usage directive, then parameter note, then the fee note. Every sentence earns its place without unnecessary jargon.

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 simplicity of the tool (one required string parameter, no nested objects, no output schema), the description provides enough to invoke the tool correctly: the input is a prompt, the output is a compressed version, and the cost is explicit. The missing output schema is not fatal because the description explains the return shape. It slightly falls short by not clarifying input limits or language handling.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, since the sole `input` parameter is described as 'Texto/prompt a comprimir'. The description merely restates the same meaning ('input=text to compress') without adding syntax, expected format, or edge-case guidance, so the description adds little 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 action ('Compress a prompt'), a clear resource ('prompt'), and the expected outcome ('return version with fewer tokens'). It distinguishes itself from sibling tools like count-tokens (which measures tokens) and summarize (which produces a human summary) by focusing on lossless token reduction.

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 tells the agent when to use the tool: 'Use it BEFORE sending prompts to any LLM to cut token cost without losing meaning.' This is actionable and context-rich. However, it does not name alternative approaches or explain when not to use it, leaving some room for ambiguity.

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

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

With no annotations, the description carries the full disclosure burden and does it well: it specifies the compression target (<=50%), the preservation of meaning, and adds the pay-per-use cost of 0.001 USDC on Base. It does not contradict any annotations and provides meaningful behavioral context beyond a generic action.

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 action, the constraint, the input type, and the token-saving use case in a few short clauses. The pricing line is additional but useful, and every part 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 simple one-parameter text transformation tool with no output schema, the description covers the operation, input, output constraint, and commercial context. It is sufficiently complete, though it could briefly clarify what the caller receives in 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?

The schema covers 100% of the parameter structure, so the baseline is 3. The description adds only a minimal clarification that the input is text, while the schema's own description is generic ('service input'). This is helpful but not substantial enough to raise the 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 a specific operation: compressing a prompt into dense language while keeping meaning and reducing length to at most 50%. It is clear and actionable, but it does not distinguish this tool from the similarly named sibling 'compress' or related tools like 'summarize' and 'destilar'.

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 two usage contexts: consumer prompt compression and B2B token cost savings in pipelines. This implies when the tool is useful, but it provides no explicit guidance on when to prefer comprimir over alternatives such as 'compress', 'summarize', or 'destilar'.

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

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

With no annotations, the description carries responsibility for behavioral disclosure. It usefully reveals tiktoken exactness, optional model, pay-per-use pricing, and cost (0.001 USDC on Base), but it does not state the output format, behavior without a model, or error/auth requirements. The optional 'modelo' mention also conflicts with the input schema, reducing trust.

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, label-segmented, and front-loads the main purpose. Each chunk adds at least some information, though the B2B clause is somewhat cryptic and the 'modelo=optional' note is unreliable.

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 gives the core behavior and cost, but it lacks a clear return-value statement, default-model behavior, and resolution of the missing 'modelo' parameter. It is adequate for a simple count 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?

The schema only documents 'input' as 'service input'; the description clarifies it is text and mentions an optional 'modelo' parameter. However, 'modelo' is not present in the input schema, so an agent following the description would likely try to pass a parameter the tool may not accept. That misleading extra detail outweighs the minor clarification of 'input' as text.

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 this tool computes an exact token count using tiktoken for a model, which is a specific verb + resource. It does not, however, differentiate itself from the sibling tool 'count-tokens', and the B2B 'budget LLM calls' phrase 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 gives some use-case context by splitting into CONSUMER (exact token count) and B2B (budget LLM calls), implying when to use it. But it offers no exclusions, no comparison to alternatives like count-tokens, and no guidance on when this tool would be inappropriate.

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?

No annotations are provided, so the description carries the burden of behavioral disclosure. It usefully discloses that this is a PREMIUM pay-per-use service with a specific cost (5.0 USDC on Base), which is important operational context. It does not describe output format, delivery behavior, or side effects, but the tool appears to be a straightforward content generator.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence with no wasted words. It front-loads the tool's purpose, then gives the input expectation and payment terms. Everything included 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 one-parameter tool with no output schema, the description adequately explains what the tool produces, what input to provide, and the cost. It is slightly thin on the exact output shape or delivery mechanism, but the core information needed to invoke and understand the tool is 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?

The schema coverage is 100%, but the schema's parameter description is generic ('service input'). The description adds critical meaning by specifying that the input should be a brief, which tells the agent exactly what to pass. This exceeds what the schema alone provides.

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 identifies the tool as a full content pipeline with explicit deliverables: post, variants, and hashtags. It also specifies the required input type (brief), which distinguishes it from narrower sibling tools like headlines or keywords. However, it lacks a clear verb like 'generate' or 'create', so it is slightly less direct than an ideal definition.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the usage context: use this tool when you need a complete content package (post + variants + hashtags) from a brief. It does not explicitly mention when not to use it or name alternative sibling tools, so the agent must infer the differentiation from the sibling list.

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

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

With no annotations, the description carries the full transparency burden. It discloses the pay-per-use behavior and cost ('0.15 USDC on Base'), marks it as premium/business-only, and mentions that a legal notice accompanies the draft. It does not detail rate limits, error behavior, or whether the operation is irreversible, but the core behavioral facts are surprisingly clear given the short description.

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 highly compact and front-loads the purpose before specifying the input requirement and pricing. Every segment serves a distinct functional purpose: target audience, output, input format, and cost. A small amount of marketing noise ('PREMIUM') is present, but it does not undermine the description's 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?

Given a single simple input parameter and no output schema, the description adequately explains what the agent needs: a contract type plus terms, and the tool produces a draft with a legal notice. It even flags the pay-per-use cost. The only missing contextual detail is the exact output format/structure, but the core call is not impeded.

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 already describes the parameter as 'tipo de contrato y términos clave', and the description repeats this as 'input=type + terms'. Since schema coverage is 100%, the description adds little genuinely new semantic value, though it does reinforce the expected structure of the input.

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 drafts a contract/agreement from the user's terms and includes a legal notice. It names a specific verb and resource ('contract/agreement draft from your terms') and the 'PREMIUM (business)' label indicates target context. It could more explicitly differentiate from sibling tools like smart-document or traductor-juridico, but the core purpose is unmistakable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides a usage formula: 'input=type + terms.' This implies the input should contain the contract type and key terms, giving some guidance. However, it does not state when to prefer this tool over any of the many sibling tools nor when not to use it, leaving the agent to infer suitability.

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?

There are no annotations, so the description carries the full burden, and it does add genuine behavioral context: 'High volume' signals scale expectations, the x402 note discloses a pay-per-use cost of 0.001 USDC on Base, and 'Estimate' frames the result as approximate. Yet it omits which tokenizer or tokenization standard the estimate uses, input-size limits, and what the response contains. With zero annotation coverage, these gaps are material.

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 purpose-first: the verdict comes first, then a terse set of separate clauses for volume, input, and pricing. Each fragment earns its place, with the sole waste being 'input=text' which partially duplicates the schema. Overall, it is efficient, appropriately sized, and well organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no annotations and no output schema, the description covers the essentials: purpose, input, volume, and cost. However, it never says what the tool returns (a plain number? a breakdown?) or which tokenizer the estimate is based on, both relevant for the stated budgeting use case. Because there is no output schema to compensate, these omissions are noticeable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% — the only parameter has a description, 'texto a medir', so the schema already documents it. The description's 'input=text' merely restates what the schema conveys without adding tokenizer or format details. The baseline score of 3 applies because the description neither harms nor meaningfully enriches the 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 and resource: 'Estimate the token count of a text' is a clear verb+object pairing, and the parenthetical '(to budget LLM calls)' grounds the purpose. It naturally distinguishes itself from the many sibling text-processing tools because it is the only one dedicated to counting/estimation.

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 purpose clause 'to budget LLM calls' implies when the tool should be used, which provides some contextual guidance. However, the description never names alternatives, exclusions, or explicitly says when not to use it. For a tool with no obvious sibling competitor this is adequate, but it leaves the decision to inference.

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

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

With no annotations provided, the description is expected to carry behavioral information. It does disclose a pay-per-use cost structure and the nature of the transform (natural language to cron expression). This is useful, but it omits other traits like output format expectations, potential validation behavior, or error handling. It adds some value but not enough for a higher score.

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, clearly stating the action and input first. The pricing note is secondary but relevant. No filler exists, and each element earns its place. It could be slightly improved by removing the bracketed metadata, but it is still efficiently 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?

For a simple one-parameter tool with no output schema, the description is mostly complete: it names the transformation and the nature of the input. However, it does not clarify what the return value looks like (beyond being a cron expression) or what happens with malformed inputs. This is a minor but meaningful gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description goes slightly beyond the schema by explicitly stating 'input=frequency', which directly communicates the intended semantic role of the parameter. The schema description 'cada cuánto ejecutar' already indicates frequency, but the description's explicit mapping reinforces the 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 clearly states the tool's function: generating a cron expression from natural language. The verb 'Generate' is specific and identifies the resource (cron expression). It does not explicitly differentiate from sibling tools, but none of the siblings are cron-related, so the distinction is not critical. The purpose is clear and actionable.

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 does not provide any guidance on when to use this tool versus alternatives, nor does it mention exclusions or prerequisites. The usage context is only implied by the purpose itself. The pricing note gives cost context but not usage-directional 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?

With no annotations, the description carries the burden. It discloses 'INFORMATION ONLY', signalling no trade execution, and the pay-per-use cost model. However, it doesn't describe limitations like data source, period, or whether fees/slippage are included, leaving important behavioral traits hidden.

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, then adds cost and parameter hints. The cryptic 'TRADING(info)' prefix and telegraphic style are minor warts, but there is no redundant content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no output schema, the description omits any indication of the return format (e.g., performance metrics, chart data, trade list). It also lacks defaults for 'dias' and data source, so an agent cannot fully predict the outcome.

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's single 'input' parameter has only a generic 'service input' description, so the tool description adds the crucial mapping 'input=symbol, dias=optional'. This gives the agent the actual semantic content, though it doesn't specify types or defaults.

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 'backtest' with a clear resource 'SMA-crossover on historical data', and the 'INFORMATION ONLY' tag further distinguishes it from execution-style trading tools like trading-signal. An agent can tell this from siblings without opening the schema.

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 when-to-use or alternatives are provided. The description implies a use case (SMA crossover historical analysis) but doesn't state when to prefer this over trading-signal or market-data, nor any exclusions. 'INFORMATION ONLY' is the only usage hint.

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

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

With no annotations, the description carries the full behavioral disclosure burden. It discloses that the report is deep and cited and includes pay-per-use pricing, which is useful. However, it claims 'input=optional scope' while the schema marks the input parameter as required, and it does not describe the response format, citation mechanism, or any operational side effects. This contradiction undermines trust.

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 report type and subject, followed by audience and billing metadata. There is almost no wasted text, though the dense mixed-language formatting and embedded pricing metadata make it slightly less clean than ideal.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter report tool, the description gives enough to understand the topic, depth, citation quality, audience, and cost. But without an output schema, it does not specify the return structure, and the optional/required input mismatch leaves the invocation contract unclear. It is minimally complete but not fully dependable.

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 only says 'service input', which is generic, but the description adds that input is an optional scope selector, giving it semantic meaning. However, 'optional' conflicts with the schema's required flag, so the added meaning is partially unreliable. Schema coverage is high, so the baseline of 3 applies despite the limited parameter detail.

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 this as an industry report on the state of crypto payments in 2026, with explicit depth and citation markers. It names a specific resource and verb ('INDUSTRY REPORT') and states the B2B audience. It does not explicitly differentiate itself from overlapping siblings like x402-payments-landscape or latam-fintech-report, 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 an implied use case: B2B PSPs and merchants evaluating crypto payment integration. However, it does not say when to prefer this tool over siblings, nor does it provide exclusions or alternative tool guidance. The usage context is present but not explicit enough to route an agent reliably.

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

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

With no annotations provided, the description carries the full burden. It transparently mentions the pay-per-use x402 billing (0.005 USDC) and 'real market data' nature, plus 'LIVE price' and '24h change' scope. It doesn't disclose potential failure modes like rate limits, stale-data incidents, or invalid symbol handling, but the core behavior is reasonably clear for this simple read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loads the key purpose (live price), then scope (USD/COP, 24h change), audience, and cost. The [x402] note is useful but slightly interrupts the natural reading flow; still, every sentence serves a purpose and there's no wasteful padding.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter, read-only price lookup with no output schema, the description covers the essential inputs, output currencies, and cost. Minor gaps include no explanation of the return value structure (JSON keys) and no examples of valid IDs beyond tickers, but these are acceptable for a simple 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 coverage is 100% and the schema describes the input as a symbol or id. The description reinforces this with examples and specifies the exact output currency (USD/COP). It doesn't add detail about accepted symbol normalization or syntax beyond examples, so it stays at the baseline for fully-covered 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's function: returning live cryptocurrency prices in USD and COP with 24h change, and specifies example inputs (btc, eth, sol, usdc). It distinguishes itself from broad financial tools like market-data and fx by specifying the crypto focus and live data nature, though it doesn't explicitly name a sibling alternative.

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 notes this is for 'agents that need prices' and indicates the input format (symbol or id), giving clear context for when to use it. However, it does not explicitly state when not to use it (e.g., for historical data or fiat conversions) nor name alternative tools like market-data or fx for such 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?

With no annotations, the description must carry behavioral disclosure. It communicates that this is a workflow, specifies its content dimensions, and discloses payment/rate information ('15.0 USDC on Base, pay-per-use'), which is useful context. However, it does not describe side effects, authentication, or what is actually returned beyond the inferred sequence.

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, front-loaded with the tool's type and object, and uses labels ('WORKFLOW', 'input=product', 'B2B') to keep information scannable. It includes the parenthetical detail, audience, and pricing without wasted prose.

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-input tool with no output schema, the description gives enough to understand what the tool produces (activation onboarding sequence across channels, timing, messages) and for whom. The main gap is the absence of explicit return-format or output-delivery information, but the low complexity and clear content dimensions keep this from being a serious omission.

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 only describes the parameter as 'service input,' so the description's 'input=product' adds meaningful semantics that tell an agent what value to supply. Although schema coverage is 100%, the field name and description are generic, and this guidance materially improves invocation accuracy.

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 frames the tool as a workflow that produces an activation onboarding sequence, specifying the covered dimensions (channels, timing, messages) and the input (product). It is clearly distinguished from generic workflow/pipeline siblings by its onboarding-specific scope, though it lacks an explicit verb such as 'generate' or 'design.'

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 states the target use case ('B2B: PLG teams boost activation') and the required input ('input=product'), giving an agent clear conditions for selection. It does not explicitly mention when not to use it or name alternatives, but the audience and goal narrow the choice among many 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?

With no annotations provided, the description carries the behavioral burden. It discloses meaningful traits: the operation is pay-per-use at 1.0 USDC on Base, and 'draft' implies it does not send the reply. However, it does not describe the return format, classification categories, or any side effects.

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, moving from purpose to input to cost. The 'tono' reference and the x402 pricing bracket are cryptic and add some friction, but overall the structure is 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?

For a one-parameter generative tool, the description is mostly adequate: it states the task, the input, an optional tone adjustment, and the cost. The main gaps are the lack of any output schema or return-value explanation and the undefined 'tono' behavior, which leave room for agent uncertainty.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3, but the description introduces 'tono=optional' as a parameter that does not exist in the input schema. This is misleading and could cause an agent to send an invalid parameter. The only genuinely useful addition is 'input=ticket', which clarifies what the 'input' string 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 two-part action: classify a support ticket and draft the reply. It also scopes the tool to SMB support tickets, which helps distinguish it from generic tools like 'reply' or 'classify-topic'. However, it does not explicitly name or contrast 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 clear context: this is for SMB support ticket classification and reply drafting. An agent can infer when it applies, though it does not provide exclusions or explicitly point to alternatives.

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?

No annotations are provided, so the description carries the disclosure burden. It usefully reveals the pay-per-use cost of 120 USDC on Base and indicates that the result is a design plus ROI. It does not mention output format, execution side effects, or limitations, but the pricing disclosure adds meaningful behavioral context.

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 packs purpose, input contract, and pricing into a single compact line. The main purpose is front-loaded, and there is no filler or redundant restatement of the tool name.

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 an open-ended design tool with no output schema and no annotations, this description gives enough to construct an input but does not clarify the output format, depth of the design, or how ROI will be presented. It is minimally adequate but leaves room for ambiguity.

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 only says 'service input', which is generic. The description adds actual meaning by specifying that the input should describe process and systems. Since schema coverage is 100%, the baseline is 3, and this extra semantic guidance raises it to 4.

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 concrete deliverable: end-to-end enterprise process automation design plus ROI. This moves beyond the tool name and adds a distinguishing angle, though it does not explicitly contrast with siblings like autonomous-workflow.

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 implies the intended context with 'B2B' and 'enterprise process automation' and tells the caller what input should contain ('input=process+systems'). However, it does not explicitly state when to choose this over related alternatives or when not to use it.

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

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

With no annotations, the description must carry behavioral disclosure, and it does reveal a real-world consequence: this is a pay-per-use tool costing 5.0 USDC on Base and producing a report with cited web sources. However, it does not describe output format, latency, side effects, or any limitations, and 'PREMIUM' is a label rather than a behavioral explanation.

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 very short and front-loads the primary purpose before cost details. The phrasing is fragmentary ('input=topic.', '[x402: 5.0 USDC on Base, pay-per-use]') but each portion earns its place. The 'PREMIUM:' prefix is mostly marketign labeling rather than functional 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 single-parameter paid tool, the description covers the critical invocation facts: pass a topic, receive a cited research report, and pay 5 USDC. Missing are output format details, explicit warning that spending money requires user consent, and guidance about which sibling tools are cheaper, free, or more appropriate for lighter research needs. It is adequate for invocation but not rich enough for confident routing among similar report tools.

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's parameter description is only 'service input,' so the description's 'input=topic' is the meaningful explanation. Since there is exactly one parameter, this is sufficient for an agent to construct a valid call. It does not add formatting constraints, but none are needed for a free-form topic string.

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 communicates the deliverable: a full research report with cited web sources, and tells the agent the input is a topic. It is distinct from generic siblings like 'research' or 'report' by emphasizing 'deep' and 'cited web sources,' though it lacks an explicit verb like 'generates.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to choose this tool over siblings such as 'research,' 'report,' 'fact-check,' or 'market-intelligence.' The only usage cue is 'input=topic,' which tells what to pass but not which scenario warrants the premium tool. It also does not mention exclusions, cheaper alternatives, or when a non-preMIUM tool would suffice.

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?

No annotations are provided, so the description carries the behavioral transparency burden. It discloses the pay-per-use cost ('0.01 USDC on Base, pay-per-use'), which is useful operational context. However, it does not describe output format, side effects, or any limitations beyond pricing.

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 purpose stated first. The usage instruction and pricing note are useful. The 'input=concepto' fragment is slightly redundant with the schema, but it does not significantly 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?

For a simple, single-parameter conceptual tool, the description adequately covers purpose, how to call it, and cost. It does not specify the output shape, but no output schema exists and the tool's output is naturally a text explanation of a concept's essence.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description adds 'input=concepto,' which only restates the schema's 'concepto/cosa a destilar.' Since the schema already defines the parameter well, the description adds limited additional semantic value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb and resource: 'Destila la esencia irreductible de un concepto (su principio de fondo).' This conveys what the tool does and its outcome. However, it does not explicitly differentiate it from siblings like summarize, explain, or interpret, which could overlap conceptually.

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 clear guidance on when to use the tool: 'Úsalo para obtener el núcleo conceptual de algo.' This is enough context for the intended use case. It does not state when not to use it or name alternative tools, but the usage intent is reasonably clear.

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

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

With no annotations provided, the description carries the full behavioral disclosure burden. It does disclose the pay-per-use cost and payment rail ('x402: 0.01 USDC on Base'), which is genuinely useful. However, it says nothing about processing semantics, limits, batching format, or result behavior, leaving substantial behavioral information undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a short fragment rather than a complete sentence. While it avoids verbosity, it sacrifices necessary clarity: the core action is undefined and the cost detail takes up most of the space. This is under-specification rather than effective conciseness.

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 no annotations, the description is incomplete. It mentions billing and batch mode but omits what 'destilar' produces, what the pipeline input should contain, and what the return value looks like. An agent could invoke it with random text and have no idea what to expect back.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the single 'input' parameter is already documented as 'pipeline input', establishing the baseline of 3. The description adds only the 'batch' hint, which may imply the input should contain multiple items, but it does not specify delimiters, array format, or size limits. It neither fully compensates for nor conflicts with 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 says 'destilar specialized for batch', which identifies the tool as the batch variant of the destilar family and distinguishes it from siblings like destilar, destilar-multi-language, and destilar-structured-json. However, 'destilar' itself is never defined in English, so an agent unfamiliar with the term cannot tell what action the tool performs. It is a recognizable mode label rather than a clear verb+resource 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?

No explicit guidance is given about when to use this tool versus alternatives. 'Specialized for batch' implies it is for batched inputs, but there is no mention of when not to use it, how it differs from destilar-tables or destilar-structured-json, or any prerequisites. The agent has to infer usage from the name alone.

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

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

With no annotations, the description carries the full behavioral burden, but it only discloses a payment detail ('x402: 0.01 USDC on Base, pay-per-use'). It says nothing about execution behavior, required authorization, whether the operation is read-only or mutating, or what the agent can expect in return.

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 text is short and front-loaded, with no redundant phrasing. However, it is under-specified; its brevity reflects absence of information rather than efficient packaging of a complete definition.

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 annotations and no output schema, the description leaves the core operation undefined and fails to distinguish this multi-language variant from a dozen related siblings. Pricing alone does not make this a complete, callable tool description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already covers 100% of parameters with 'pipeline input,' so the baseline is 3. The description adds no parameter-level detail, but it does not need to compensate because the single parameter is already documented.

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 'destilar specialized for multi language' restates the tool name without defining what destilar actually does; it offers no concrete verb or resource. The multi-language qualification mirrors the name, making it near-tautological, and although it is clearly a variant of destilar, the core function remains undefined.

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 guidance is provided on when to use this tool versus sibling alternatives such as destilar, destilar-tables, translate, or vision-multi-language. The only contextual hint is the x402 pricing, which does not help an agent choose this tool over its 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?

With no annotations provided, the description carries the full burden of disclosing behavior. It does mention pay-per-use and the cost on Base, which is a useful operational detail, but it entirely omits what the tool does to the input, what the output looks like, whether it has side effects, or what limitations exist. The behavioral disclosure is minimal and insufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence and is front-loaded with the purpose phrase, with the pricing information appended. There is no wasted wording, but the main clause is a near-verbatim restatement of the tool name, so it is under-specified rather than elegantly concise. The pricing note is useful and earns its place, but the rest does not.

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 an agent choosing among many destilar and JSON-related siblings, this description is incomplete. It lacks the expected input format, the output structure, and any indication of when this variant is preferable. The single parameter and no output schema increase the need for rich description, yet the description only offers a name restatement and a payment detail.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single 'input' parameter, so the baseline is 3. The description adds no parameter-level detail beyond what the schema already states, and both the description and schema leave the expected format of 'pipeline input' vague (e.g., raw text, JSON string, object). It does not actively mislead, but it also does not enrich 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 'destilar specialized for structured json' is essentially a restatement of the tool name with no action verb. It does not state what the tool does with structured JSON—whether it extracts, generates, transforms, or validates it. It only distinguishes itself from siblings like destilar-tables or destilar-batch by naming 'structured json', but still fails to define the actual function.

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 offers no guidance on when to use this tool instead of destilar, destilar-batch, ocr-structured-json, extract-json, or json-fix. There are no usage conditions, no exclusions, and no mention of input type or intended use case. The only additional context is the pricing note, which does not help with tool 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?

No annotations are provided, so the description carries the full disclosure burden. It adds useful cost/pricing context via 'x402: 0.01 USDC on Base, pay-per-use,' but says nothing about input handling, output format, side effects, or limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is very short and front-loaded: the key qualifier 'tables' appears immediately, and the pricing detail is cleanly bracketed at the end. It contains no wasted words, though it is terse to the point of under-specification.

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 only one vague string parameter, no output schema, no annotations, and many closely related siblings, the description leaves out essential selection and invocation details. An agent cannot tell what output to expect or how this differs from destilar, destilar-structured-json, or vision-tables.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% coverage for its single parameter, but the schema description is only the generic 'pipeline input.' The tool description adds the context that the input should relate to tables, which is helpful but still leaves the actual expected format unclear.

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 only restates the tool's name as 'destilar specialized for tables' without stating a concrete action, input, or output. It signals the table domain but does not explain what distilling tables means or what an agent should expect the tool to do.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The phrase 'specialized for tables' implies the tool should be used when the input involves tables, but there is no explicit guidance, exclusion, or mention of alternatives. With siblings like destilar, destilar-structured-json, vision-tables, and ocr-tables, the description does not help an agent choose between 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?

There are no annotations, so the description carries the behavioral transparency burden. It discloses cost per use, positioning as 'cheap' and 'high-volume,' and the ISO-code output behavior, which goes beyond the bare schema. It could add a clearer read-only/no-side-effects statement, but the non-mutating verb and cost disclosure make intent reasonably transparent.

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 main purpose. The usage and pricing notes are useful, but the 'input=text' fragment is partly redundant with the schema, keeping this from being a perfect 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?

For a simple one-parameter, no-nested-object tool, the description is largely complete: it states the input, the purpose, the cost profile, and the output format. It does not specify the exact ISO format variant or how the result is wrapped, but this is a minor gap given the simplicity of the operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the parameter is already documented and the description doesn't need to compensate heavily. The description adds the useful clue that 'input=text,' reinforcing that the single argument is plain text, but it doesn't add substantive detail such as encoding, length limits, or supported language 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 clearly identifies the tool as detecting the language of a text and specifies the output as an ISO code. This verb+resource pairing is specific and distinct from sibling translation tools, since it addresses detection rather than translation.

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 mild usage context by noting that the tool is 'cheap, high-volume for agents,' which implies it is suitable for bulk detection tasks. However, it does not explicitly state when to use it versus alternatives, nor does it name any excluded or preferred 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?

No annotations are provided, so the description must disclose behavior itself. It does mention depth, citations, and a pay-per-use cost of 100 USDC on Base via x402, which is useful. However, it does not describe return format, latency, or side effects, and the 'input=optional' statement directly contradicts the schema's required field.

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 text is compact and puts the main topic first, but it mixes English and Spanish, uses telegraphic fragments, and hides pricing in a bracketed code-like string. It is not well-structured for an agent to parse quickly, though it contains no obvious 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?

Given a single free-text input and no output schema, the description should explain expected output shape or provide an example; it does neither. The pricing and audience are stated, but the schema contradiction and lack of sibling routing leave the agent under-equipped to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only describes input as 'service input', so the description's 'optional scope' is the only real attempt to explain the parameter. That attempt is vague and inaccurate because the schema marks input as required. No format, language, or example is provided.

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 deliverable: a deep, cited industry report on developer tools 2026. The topic distinguishes it from the many sibling market/landscape reports such as ai-agents-market-2026 or mcp-servers-landscape. However, it is phrased as a label rather than a verb-driven action, and 'input=optional' conflicts with the required input schema.

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 audience context ('B2B: inversores y equipos de DevRel') and notes that input provides optional scope. It does not explicitly state when to use this tool over sibling research tools like deep-research-report or market-research-report, nor does it mention exclusions or prerequisites.

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

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

With no annotations, the description carries the burden and it does state two important traits: 'INFORMATION ONLY' signals a read-only operation and the x402 note discloses pay-per-use. It omits deeper behavioral details such as data sources, freshness, auth flow, or output behavior, but the core safety and cost traits are 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 compact and front-loads the main function, then adds the mode, asset examples, and payment note without redundant wording. The terse fragments are efficient, though slightly cryptic.

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 and the input schema is a generic string, so the description is the only source of invocation guidance. It fails to clarify what the input string should contain, how the discrepancy output is presented, or what asset list is actually supported, leaving an agent with meaningful uncertainty.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline of 3 applies even though 'service input' is a generic placeholder. The 'assets=[bitcoin,ethereum,...]' hint in the description adds some meaning about the input value, but it is informal and not mapped to the actual parameter contract.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific action ('scans') and a concrete output concept ('narrative-vs-data discrepancies') across assets, so an agent can infer the core function. However, it doesn't differentiate from closely related siblings like osint-signal or fact-check, and 'several assets' is left partially unspecified.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit when-to-use guidance, exclusion criteria, or named alternatives among the many OSINT/fact-checking siblings. The 'INFORMATION ONLY' tag and asset list give context but do not tell an agent when to choose this over related 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?

With no annotations, the description carries the full burden of behavioral disclosure. It does mention '0.1 USDC on Base, pay-per-use', a useful cost trait, but it does not state whether the call is read-only, what the response shape is, error behavior, or what happens if 'campos' is omitted.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence with the core operation front-loaded and cost/input details appended. Every token 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 one-parameter tool with no output schema, the description states the output is structured JSON and the input is text, which is minimally sufficient. But it leaves 'campos' undefined and does not clarify how the agent should provide fields or what the returned JSON will contain.

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 only defines 'input' with the generic description 'service input'. The description adds 'input=text' and 'campos=optional', but 'campos' has no corresponding schema property, which is confusing and could lead the agent to pass an unsupported parameter. This reduces the semantic value beyond the already limited schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific operation ('extract structured JSON') on a specific resource ('document') and states the output format. It does not differentiate from the closely named sibling 'extract-json', which prevents a perfect score for tool-selection 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?

It gives input-level details ('input=text', 'campos=optional') but no conditions for when to use this tool over alternatives like extract-json, ocr, or smart-document. There is no when-to-use or when-not-to-use 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?

With no annotations, the description carries the full burden and does disclose a critical non-obvious behavior: a 50 USDC pay-per-use fee on Base via x402. It also notes reliance on public sources, but omits auth requirements, read-only status, return format, and failure 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 a single compact line that front-loads the tool's purpose and packs usage and pricing into short tags. The 'ENTERPRISE:' prefix is minor noise, but otherwise every element 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 one-parameter tool, it covers the core return concept, the input type, and the significant pay-per-use cost. Still, with no output schema, it leaves the input format, report contents, and payment/auth flow implicit, creating some uncertainty for an agent.

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 only documents the parameter as 'service input', which carries no real meaning. The description adds 'input=company', giving the parameter some semantics, but it remains vague about whether a company name, ticker, URL, or jurisdiction is expected.

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 deliverable: a company due-diligence dossier with red flags from public sources. It is semantically distinct from generic research or report tools, though it lacks an explicit verb and does not name sibling tools for 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?

It gives usable context: input is a company and the call is pay-per-use. However, it does not explain when to prefer this over overlapping siblings like risk-analysis, compliance-audit, or competitive-analysis, and it provides no explicit exclusions.

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

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

No annotations are present, so the description bears the full burden of behavioral disclosure. It mentions that the tool accepts a catalog and goal and produces pricing strategy/rules, but it does not describe the output format, whether it returns a document or rules, any prerequisites, or side effects. The payment note about x402 is operational, not behavioral.

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 telegraphic and packs vertical, purpose, input, use case, and pricing into a few fragments. Every segment adds information, though the internal codes 'VERTICAL' and 'x402' may be cryptic to an agent. It is concise rather than overly 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 single-parameter tool with no output schema or annotations, the description gives the core idea, input semantics, and target audience. However, it omits the expected output shape and gives no example input. It is adequate but leaves an agent uncertain about what the returned pricing strategy/rules will look like.

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 only provides the generic parameter description 'service input', but the tool description clarifies that the input should contain a catalog and a goal. This adds meaningful semantics beyond the schema and helps an agent construct the input correctly, even though it does not specify the exact 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 identifies the tool as an ecommerce vertical tool providing 'dynamic pricing strategy + rules' and states the goal of optimizing margin/volume for online stores. While there is no explicit imperative verb like 'generate' or 'create', the resource and outcome are clear. The 'VERTICAL(ecommerce)' tag differentiates it from related pricing siblings such as global-saas-pricing.

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 clear context: use this for B2B online stores seeking to optimize margin or volume. It implies a domain-specific use case but does not explicitly list exclusions or alternative tools. The context is specific enough to guide an agent, though it could be improved by explicitly naming the SaaS pricing sibling as the alternative.

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

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

With no annotations, the description carries the full behavioral burden. It discloses pricing and payment model (10.0 USDC on Base, pay-per-use) and describes the output shape, which is useful. It does not mention authentication, rate limits, or failure behavior, but for a content-generation tool the disclosed cost and output are reasonable.

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 no wasted words. The use of shorthand like 'VERTICAL(edu)' and 'B2B' creates some ambiguity, but every segment earns its place by covering purpose, input, audience, and pricing.

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 one simple string parameter and no output schema, the description covers the essential contract: input shape, output content, target user, and cost. It could benefit from an example or clarification of what 'level' encompasses, but it is complete enough for a competent agent to attempt 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?

The schema only describes 'input' as 'service input', which is unhelpful. The description adds real semantics by specifying 'input=topic+level', telling the agent exactly what to supply. It does not detail the expected formatting or enumeration of levels, but it meaningfully compensates for the schema's vagueness.

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's output — a full course curriculum with modules and rubric — and indicates the key inputs (topic and level). It stops short of explicitly naming sibling tools or differentiating itself from them, but it is unambiguous in its core 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 'B2B: edutechs generate curricula at scale' sentence gives a clear intended use case and audience, so an agent can infer when to select this tool. It does not explicitly list exclusions or alternatives, but the context is strong enough 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?

The description discloses useful behavioral details such as the campaign components and a pay-per-use cost of 20.0 USDC on Base. However, with no annotations, it does not clarify whether emails are actually sent, how payment is triggered, or what the return format will be.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded line that covers output, input, use case, and pricing in order of importance. Each phrase carries distinct information, 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?

For a one-input generative tool with no output schema, the description gives enough context: input brief, output components, target users, and cost. The main gaps are the exact return structure and payment prerequisites, but these are not critical for basic invocation decisions.

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 only describes the input as 'service input,' which is generic. The description adds meaning by specifying that the input should be a brief and that it feeds into subjects, bodies, CTAs, and scheduling. Still, the expected structure or length of the brief is not detailed.

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 deliverable: a 5-email campaign with subjects, bodies, CTAs, and a schedule, taking a brief as input. This makes it distinguishable from single-email sibling tools like email-polish or sales-email, though it lacks an explicit action verb like 'generates' or 'creates'.

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 a clear intended context: B2B growth teams launching nurture flows. This helps an agent decide when this tool fits, but it does not provide explicit exclusions or name specific alternative tools for when not to use it.

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

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

With no annotations provided, the description carries the behavioral disclosure burden. It usefully reveals the pay-per-use cost model ('0.05 USDC on Base') and the optional tone input, but it does not explain the output format, whether proofreading results are separate from the rewritten text, or any input 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 concise and front-loaded with the core operation, followed by input and cost information. Minor style issues such as abbreviations and mixed-language terms slightly reduce clarity, but there is no wasted content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple one-parameter tool, the core operation is stated, but the lack of an output schema or return-format explanation leaves the agent uncertain about what to expect. The phantom 'tono' parameter and absence of sibling differentiation further reduce the description's completeness.

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 100% coverage for 'input', so the baseline is 3. The description clarifies that input is a draft, but it also introduces an optional 'tono' parameter that is absent from the schema, which is misleading and could lead an agent to send an invalid parameter.

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 concrete action ('rewrite email formal + proofread') and identifies the resource as an email draft. It is clear, but it does not explicitly differentiate this tool from sibling tools like rewrite-formal or proofread, 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?

No guidance is given for when to use this tool versus alternatives such as rewrite-formal, proofread, customer-reply, or sales-email. The 'CONSUMER:' label hints at an intended audience but provides no selection criteria or exclusions.

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

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

No annotations are present, so the description carries the full behavioral burden. It communicates that the core behavior is returning a vector and mentions 'High volume' and pay-per-use pricing, but it does not describe the output format, possible limits, or failure modes.

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 first sentence is front-loaded and precise. However, 'The building block every AI agent needs' is promotional filler, and 'High volume. input=text' is a fragmented addition that partly repeats the 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 one-parameter, stateless generation tool the description is reasonable, but it lacks any note about the shape of the returned vector or how it is delivered. It also does not reference sibling tools, leaving some ambiguity about when to use this versus semantic-search.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the input property already has a description and type. The description's 'input=text' merely duplicates the schema and adds no further semantic meaning about formatting or constraints.

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 and resource: 'Turn text into a vector (embedding)'. The use cases (semantic search, RAG, clustering, memory) clarify what the embedding is for and distinguish it from the sibling semantic-search tool, which performs search rather than producing embeddings.

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 through the listed use cases, but it does not explicitly state when to choose this tool over similar siblings like semantic-search or inference. No when-to-use, when-not-to-use, or alternative routing is provided.

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?

No annotations are provided, so the description carries the full burden. It discloses that the tool produces a deep, cited report and charges 200.0 USDC on Base, which is useful. However, it does not explain the report format, how citations are generated, or any side effects beyond the pay-per-use model.

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-loads the report type, with no padding. Each fragment adds something: content type, depth, input role, audience, evidence focus, and pricing. The telegraphic style is slightly fragmented but not wasteful.

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 report-generation tool with no output schema, the description gives a reasonable high-level picture but leaves important gaps: it never says what values the scope input can take, what the returned report looks like, or how the x402 payment affects invocation. An agent could call it, but not with full confidence about expected input and output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the schema's description is just 'service input,' adding no meaning. The description adds that input is an optional scope for the report, which helps. Yet this conflicts with the schema declaring input as required, and 'scope' remains 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 identifies the deliverable as an industry report on enterprise AI adoption, with depth and citations. It distinguishes this tool from siblings like ai-agents-market-2026 or deep-research-report by naming the specific enterprise adoption angle and B2B use case, though it lacks an explicit verb.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: the report is for B2B enterprise leaders building an AI roadmap, and the input is described as an optional scope. It does not explicitly name alternatives or exclusion conditions, so it stops short of 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?

With no annotations, the description carries the full burden of behavioral disclosure. It does mention scope, remediation horizon, and pay-per-use pricing, but it does not disclose what the tool returns, whether it performs writes or side effects, or what 'remediation' means in terms of execution.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single compact sentence that front-loads the core purpose and scope. There is no filler, though the `[2: 75.0 USD on Base, pay-per-use]` pricing notation is cryptic and assumes domain familiarity.

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 paid, non-trivial audit/remediation tool with no output schema and no annotations, the description omits the return value, side effects, and how it differs from the similar `compliance-audit` sibling. The input hint and pricing are helpful, but an agent still cannot fully predict the tool's behavior or route to the right alternative.

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 describes `input` only as 'service input', which is uninformative. The description adds real meaning by specifying `input=policies+framework`, so an agent knows what content to provide. It still lacks format or encoding details, but it substantially improves on 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 clear action ('compliance audit'), resource ('polcies + framework'), scope ('company-wide'), and a time-bound outcome ('90d remediation'). It gives the tool an identifiable purpose, though it does not explicitly contrast itself with the sibling `compliance-audit`.

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 case: B2B, company-wide compliance audits with a 90-day remediation follow-up. However, it provides no exclusion criteria or alternative guidance, and the sibling `compliance-audit` is close enough that an agent could easily pick the wrong tool.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does usefully note that output is JSON and that the service is pay-per-use with a cost in USDC on Base. Yet it does not mention whether the operation is safe/read-only, whether costly surprises or rate limits exist, or any other consequential 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 front-loaded: the main purpose ('Named-entity recognition') occupies the beginning, followed by the output type and then a usage hint plus payment note. Every element contributes to either knowing what it does or how to call it, with no fluff.

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 simple—one input parameter, no nested objects, no output schema—so the description does not need much to invoke it correctly. Still, it only says 'as JSON'; it fails to indicate the structure of the returned entities (e.g., an array of entity names with types and offsets). For a tool with no output schema, this is a notable 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 coverage is 100% because the only parameter 'input' has a description 'texto'—the description's 'input=text' merely repeats the same concept. No deeper parameter semantics, such as encoding, language support, or size limits, are added.

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 the specific task 'Named-entity recognition (NER)' and the input/output ('input=text', 'as JSON'). This makes it clearly distinguishable from sibling tools like sentiment or keywords, since NER is a unique, well-defined functionality.

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 shows the basic expected input ('input=text') and the output format, so an agent can infer when to use it. However, it gives no explicit conditions, alternatives, exclusion scenarios, or prerequisite details beyond the trivial 'provide text.'

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?

There are no annotations, so the description carries the behavioral burden. It adds useful details like 'High volume' and 'pay-per-use' with a specific price and network, which informs the agent about cost and scale. However, it does not describe output shape, length, limitations, or side effects.

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 very concise and front-loaded with the core function. The extra fragments about volume and price are useful, though 'input=concept' is repetitive with the 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 tool with no output schema and no annotations, the description could do more. It includes cost and parameter hints, but it doesn't say what kind of answer is returned, how long it might be, or when to use a sibling instead.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the only parameter with 100% coverage ('concepto a explicar'). The description's 'input=concept' adds minimal semantic weight beyond the schema, which is already sufficient for a single-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 states the specific action ('Explain a concept simply') and the resource (a concept), making the purpose clear. It does not explicitly differentiate between the many siblings like summarize or interpret, but the core function is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided about when to use this tool versus alternatives. 'High volume' hints at cost/scale but does not give explicit selection criteria or exclusions.

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

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

With no annotations provided, the description carries the responsibility for behavioral disclosure. It does mention the pay-per-use cost (x402: 0.004 USDC) and clarifies that the input is free text, which is useful. However, it doesn't disclose potential failure behaviors, whether output is a raw JSON result or a wrapper, or any constraints on input length or content.

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 to-the-point, with the core action stated first and the parameter and cost notes appended. It has no irrelevant filler, though the inline 'input=text' is slightly informal compared to a structured explanation. Overall, it is easy to read and quick to parse.

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 tool with one parameter and no output schema, the description covers the essential basics: what it does, what the input is, and that it costs money. However, it lacks guidance on when to pick this tool over its many siblings and doesn't describe the shape or container of the returned JSON. Given the absence of annotations, a bit more context would help an agent confidently select and invoke it.

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 fully covers the single parameter 'input' with a description ('texto del que extraer datos'). The description adds only 'input=text', which is redundant and lacks extra context such as an example or specific formatting. Because schema coverage is 100%, the description doesn't need to do much, but it also adds little 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's function: 'Extract structured data from free text and return it as JSON.' This specifies a distinct verb and resource with a concrete output format. It is not explicitly differentiated from sibling tools, and there is no note about when to prefer it over json-fix or other extractive tools, but the core purpose is unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given about when to use this tool compared to alternatives like json-fix, entities, or ai-inference. The description simply states the action and mentions the input field; it does not provide exclusionary criteria or example scenarios, leaving the agent to infer the appropriate 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 are absent, so the description carries the full behavioral burden. It does disclose the output contract ('a verdict and reason') and mentions pay-per-use cost on Base, which is useful. Still, it does not describe the source of the fact-check, confidence, evidence, or any limitations, leaving the agent uncertain about what the verdict will be based on.

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 exceptionally compact and front-loaded. It communicates the action, output, input, and pricing in one short sentence with no wasted filler or redundant detail.

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 essential context: it tells the agent what input to provide and what output to expect (a verdict and reason). It would be stronger if it specified the possible verdict format or language, but it is otherwise complete for practical 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 schema already provides 100% coverage for the single parameter, describing 'input' as 'afirmación a verificar.' The description only restates this as 'input=claim' and adds no extra format, length, or language guidance 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 opens with a specific verb and resource: 'Fact-check a claim with a verdict and reason.' This clearly identifies the action, the input kind, and the expected result. It is also distinct from the sibling tools such as code-gen, sentiment, or translate because the task is unambiguously verification-oriented.

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?

Usage is implied rather than explicit: when an agent has a claim to verify, this tool seems appropriate. However, the description never says when not to use it or names an alternative such as research, explain, or inference, so an agent must infer the choice among the many 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?

No annotations are provided, so the description carries the behavioral disclosure burden. It does disclose the pay-per-use cost ('0.1 USDC on Base') and that it uses web sources, which is useful. However, it does not mention the output shape, failure modes, or whether this is a read-only operation, leaving meaningful behavioral gaps.

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 extremely concise, front-loaded with the purpose, and includes the necessary input hint and cost information without any redundancy. Every element 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?

Given the tool's low complexity—one input parameter and no nested schema—the description provides enough context: purpose, input type, source type, and cost. It does not describe the response format, but for a simple fact-check tool the expected output is reasonably inferable. The absence of an output schema slightly lowers completeness but is not critical here.

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 describes the single parameter only as 'service input,' which is generic. The description adds meaningful semantics by stating 'input=claim', clarifying that the string should be a factual claim to verify. With schema coverage at 100%, this added clarification raises the score above baseline.

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 operation as a 'quick fact-check with web sources' and specifies the input as a claim. The 'quick' qualifier suggests a lighter-weight alternative to the sibling 'fact-check' tool, though it does not explicitly name the distinction. Overall the purpose is clear and operational.

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 'CONSUMER:' prefix and 'quick' descriptor indicate this is meant for lightweight, general-audience fact-checking, and the pay-per-use cost clarifies the commercial context. It does not explicitly say when not to use it or name an alternative, but the implied usage context is clear enough for an agent to select it for quick verification tasks.

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?

No annotations exist, so the description carries the behavioral burden. It adequately states the operation, accepts text or topic, and includes pay-per-use cost info, but it omits details like expected Q&A count, output format, or handling of edge cases.

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 primary action first, then the use case and input. The cost note is extra but relevant. There is a minor redundancy between 'text/topic' and 'input=text or topic'.

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 tool with one string parameter and no output schema, this description covers essential operational context: input type, intended domain, result type, and cost. It doesn't specify the number of Q&As or formatting, but that is not essential for basic 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 schema already describes the single input parameter as 'texto/tema', and the description mostly restates this as 'input=text or topic'. With 100% schema coverage, the description adds little beyond redundancy.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Generate an FAQ'), the output form (questions + answers), and the source material ('text/topic'). It is distinct enough from summary or explanation tools, though it does not explicitly differentiate itself from 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 phrase 'For docs/support' provides a clear context for when this tool is appropriate. However, it doesn't name any alternative tools or say when not to use it, so it stops short of 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?

With no annotations, the description carries the full burden. It adds useful behavioral context such as the pay-per-use cost (0.05 USDC on Base) and the translate+proofread behavior. However, it does not describe the output format, payment prerequisites, auth requirements, or how the proofreading behaves, leaving important operational behavior undisclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is extremely tight and front-loaded, stating the action, inputs, and pricing in a single line. Every piece of information is useful, and there is no filler or repetition of the 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 pay-per-use tool with only one input parameter and no output schema, the description is too incomplete. It does not explain how target language is expressed, what the response contains, or what prerequisites exist for payment on Base. An agent would likely need to guess or inspect sibling tools to call this correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has only 'input' with the generic description 'service input'. The description says 'input=text, to=lang', which clarifies that input is text but introduces a 'to' parameter that does not exist in the schema. This leaves ambiguity about how the agent should actually specify the target language, so the added semantic value is undermined.

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 translates and proofreads to any language, which gives a concrete verb and resource. It is not fully differentiated from sibling tools like translate-to or traducir, but the combined translate+proofread action narrows it well.

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 usage guidance is provided about when to choose this tool over translate-to, translate-doc, traducir, or proofread. The 'to any language' phrasing implies broad use, but the crowded sibling list makes the lack of explicit routing or exclusion criteria a clear gap.

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

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

With no annotations, the description carries the burden of behavioral disclosure. It does add useful context such as pay-per-use pricing on Base and the pre-launch validation scenario, which helps an agent reason about cost and use. However, it does not describe the output format, scope of coverage, or any limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and each segment contributes value: vertical, function, input format, use case, and pricing. It is not overly verbose, though the bracketed pricing and VERTICAL tags are somewhat cryptic and could be streamlined.

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 domain, input, use case, and pricing, which is a good start. However, since there is no output schema, the agent is left without any description of what the tool returns or how results are presented, which is a notable gap for a compliance-analysis tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema's single 'input' parameter is described only as 'service input', so the description adds meaningful semantics by specifying that the input should contain product and jurisdiction. This is a significant improvement over the generic schema field.

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's domain and function: evaluating KYC/AML compliance gaps for fintech products, with an explicit vertical tag 'VERTICAL(fintech)' and target use case. It distinguishes from broad sibling tools like compliance-audit or enterprise-compliance, though it uses a noun phrase rather than a strong standalone verb.

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 concrete selection context: B2B fintechs validating a product before launch, and specifies the required input shape as product+jurisdiction. It does not explicitly list exclusions or alternatives, but the fintech and KYC/AML focus makes routing reasonably clear.

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?

Since no annotations are provided, the description carries the full behavioral burden. It discloses that quotes include a spread and that each call costs 0.001 USDC on Base on a pay-per-use basis. It also uses 'Quote', implying a read-only quote rather than an executed trade, even though it does not explicitly state the absence of transaction execution.

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 tight and front-loaded: the core purpose comes first, followed by the precise input syntax and a compact cost/network note. Every sentence earns its place 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?

For a one-parameter tool with no output schema, this description is largely sufficient for invoking the tool correctly: it gives the input format, example values, behavior, and network/payment context. It does not describe the return payload or precise currency codes, but an agent can confidently call the tool with the provided template.

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 already covers 100% of the parameter, but the description adds practical nuance: it shows the exact string format and clarifies that the destination is effectively USDC (via examples like COP->USDC). The spread mention also affects how the quote result should be interpreted.

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 ('Quote') and resource (currency conversion into USDC), with concrete examples (COP->USDC, USD->USDC) that make the operation unambiguous. It clearly says the tool returns exchange rates with a spread, which differentiates it from generic sibling tools like market-data or crypto-price.

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 useful input template ('<amount> <from> <to>') and examples, implying when the tool should be used. However, it does not explicitly compare this tool to alternatives or state cases where a sibling such as crypto-price or wallet-balance would be preferable.

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

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

With no annotations, the description carries the full burden. It discloses that the tool produces a cited, deep report and mentions a pay-per-use side effect ('x402: 150.0 USDC on Base'), which is useful. However, it does not describe important execution behavior beyond output, and the phrase 'input=optional scope' conflicts with the schema's required parameter, creating potential confusion.

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 very compact and front-loaded with the key purpose ('INDUSTRY REPORT'), followed by input, audience, and cost. It contains no filler, though the compressed notation like 'x402: 150.0 USDC on Base' is somewhat cryptic.

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 simplicity, the description covers the core purpose, target audience, and usage cost. However, it is incomplete about what the input scope should be, what the report output structure looks like, and how this tool differs from neighboring report-generation 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?

The schema description for 'input' is generic ('service input'), so the description's 'input=optional scope' adds some meaning by suggesting the input is a scope filter. However, it is vague about what a valid scope looks like and 'optional' contradicts the required flag 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 clearly identifies the tool as generating an industry report of global SaaS pricing benchmarks, described as 'deep, cited.' It is specific about the resource (global SaaS pricing) and the report nature, though it does not explicitly distinguish itself from similar siblings like ecommerce-pricing or deep-research-report.

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 clear usage context: B2B product and monetization teams calibrating their pricing. It implies when this tool is relevant, but does not explicitly name alternatives or state when not to use it.

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