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?

With no annotations, the description carries the full behavioral burden. It discloses a pay-per-use cost model, network, and price, which is useful context, but it says nothing about what the operation does, required permissions, side effects, or output.

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 but poorly structured: it leads with auto-creation metadata and payment details instead of the tool's purpose. The first sentence does not help an agent decide whether to invoke it.

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 unannotated tool with no output schema and an opaque 'input' parameter, the description is completely inadequate. It omits purpose, usage context, behavior, and return information.

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 parameter meaning, 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 never states what the tool does or what resource it acts on. 'Auto-created from real demand' and payment metadata do not distinguish this tool from any sibling.

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

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

No guidance is provided on when to use this tool, when not to use it, or which alternatives to consider. The agent has no basis for selecting it over any sibling.

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 behavioral burden. It discloses the core behavior (generates configurations/policy cages), the target domain, and the pay-per-use cost. It does not describe output structure, prerequisites, or side effects, but for a generation tool the basic risk profile is reasonably clear.

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 concise sentence with the main action clearly stated. The 'Auto-created from research:' prefix adds minor noise, and the pricing bracket is relevant but secondary, so the overall structure is still 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 generator, the description gives enough context to understand the tool's purpose and cost, and lets an agent infer what the input should contain. It is incomplete about expected return values and lacks explicit routing to validator siblings, but the low complexity reduces the severity of these gaps.

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

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

The single parameter 'input' is only described as 'entrada', which is semantically empty even though schema coverage is 100%. The description partially compensates by indicating the input should reflect agent capabilities and data-access requirements, but no format or expected structure 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 uses a specific verb ('Generates') and a specific resource ('zero-trust sandbox configurations and policy cages') for a clearly defined audience (healthcare AI agents). It is easy to tell this is a generation tool, though it does not explicitly contrast with sibling tools like agent-cage-validator.

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

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

The description implies when to use the tool: when zero-trust sandbox/policy cages are needed for healthcare AI agents, based on capabilities and data-access requirements. However, it does not state when not to use it or explicitly differentiate it from related tools such as agent-cage-validator or zero-trust-policy-check.

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 does mention pay-per-use via x402, which is useful, and states that it validates and detects, but it does not disclose whether the operation is read-only, what input formats are accepted, what side effects may occur, or what the response looks like.

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

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

The description is compact and front-loaded, with the core function in the first clause and the payment detail in a short bracketed note. The phrase 'Auto-created from research' adds provenance but is minor; overall there is little waste.

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 only a single 'input' parameter, the description should clarify what to pass in and what result to expect. It mentions validating agent configurations and detecting sandbox escapes, but does not explain input encoding, report format, or operational caveats such as data handling or authentication, so an agent would have to guess.

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 single 'input' parameter has a description ('entrada'), so the baseline is 3. The tool description also references 'agent configurations,' which loosely hints at what the input should be, but it does not specify the expected string format, structure, or examples beyond what the schema already offers.

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

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

The description states a specific verb and resource: it 'validates autonomous AI agent configurations against Zero Trust security architectures' and 'detects sandbox escape vectors.' This clearly conveys what the tool does, but it does not distinguish it from close siblings like zero-trust-policy-check or verifiable-auth-validator, which likely overlap in scope.

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. The description only says it was 'Auto-created from research' and describes its function, but provides no selection criteria, exclusions, or references to the many security-related siblings such as zero-trust-policy-check or injection-guard.

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 behavioral traits. It states the tool 'issues and verifies', implying mutating and read operations, but does not clarify side effects, permissions, reversibility, or the exact nature of the output. The cost note '[x402: 0.02 USDC on Base, pay-per-use]' is a pricing detail, not a behavioral disclosure. This is insufficient for a tool with no annotation support.

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, consisting of one functional sentence and a pricing note. It is front-loaded with the primary purpose. It could be slightly more structured, but it efficiently conveys the core function without 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?

Given the absence of annotations, output schema, and an opaque parameter, the description is incomplete. It does not explain what the input should be, what the return value looks like, or any prerequisites or security considerations. An agent would struggle to call this tool correctly without additional information.

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 a single parameter 'input' with description 'entrada' (Spanish for 'input'), which is essentially meaningless. The tool description provides no additional explanation of what the input should contain, its format, or expected structure. Even though schema coverage is 100%, the coverage is empty, and the description fails to compensate, leaving the agent without guidance on parameter 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 clearly states the tool's function: 'Issues and verifies cryptographically signed identity attestations for autonomous AI agents prior to financial or sensitive transactions.' This is a specific verb+resource combination. It doesn't explicitly differentiate from sibling tools like 'verifiable-auth-validator' or 'agent-trust-registry', but the purpose is clear and not a tautology.

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

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

The description implies usage context with 'prior to financial or sensitive transactions', giving some guidance on when to use it. However, it does not mention alternatives or explicitly state when not to use it, leaving the agent to infer selection among siblings. This is clear but lacks explicit exclusions or comparisons.

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 one important behavioral trait: the tool is pay-per-use at 0.02 USDC on Base, and it returns a report. It does not describe side effects, failure modes, authentication requirements, or whether the call is read-only, but the cost disclosure is meaningful and relevant.

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 but poorly structured and appears auto-generated/truncated, with 'Auto-created from research' as filler that does not help an agent. The key pricing detail is buried in a bracketed suffix rather than integrated clearly. It is concise in length but not well front-loaded with actionable 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?

Given one opaque parameter, no output schema, and no annotations, the description leaves critical invocation details unresolved. It names report topics and pricing but does not specify how to pass the agent identity or what the raw output looks like. An agent could not reliably call this tool correctly without additional external knowledge.

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

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

The schema has one required parameter 'input' described only as 'entrada', which is a tautological label rather than semantic guidance. Even though schema coverage is 100% by count, the description does not explain what the input should contain—e.g., an agent ID, URL, or structured payload. The tool description implies the input is the autonomous agent to verify, but provides no concrete format or expected value.

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

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

The description clearly states the tool is an identity and trust verification service that returns a due-diligence report on an autonomous agent, listing report contents (identity proof, operator, reputation, authorization scope). It distinguishes itself from generic due-diligence or research siblings by focusing on agent trust verification. However, the sentence is visibly truncated ('enteri') and does not explicitly name sibling alternatives, so it falls 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?

There is no explicit when-to-use or when-not-to-use guidance, and no alternatives are named. The phrase 'before entering' hints at a pre-integration use case, but an agent cannot infer why this tool should be chosen over siblings such as 'due-diligence' or 'verifiable-auth-validator'. This leaves usage selection largely 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, 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, the description must disclose behavioral traits. It mentions 'deep, cited' (indicating comprehensive output with references) and pay-per-use pricing, but it does not describe the output format, the exact behavior when input is omitted, or any limitations. Critically, it states 'input=optional scope' while the input schema marks input as required, which is a direct contradiction that undermines behavioral transparency. Thus, the description provides insufficient and inconsistent behavioral information.

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, consisting of a few short sentences. It is reasonably front-loaded with the core purpose ('INDUSTRY REPORT') and includes additional context like target audience and pricing. While the inclusion of pricing and the cryptic 'input=optional scope' add some noise, the overall length is appropriate and the key information is presented early. I give it 4 for being efficient but not perfectly 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 tool with one parameter and no output schema, the description should clarify what the input means and what the output will look like. It only vaguely mentions 'input=optional scope' and says the report is 'deep, cited,' but does not describe the report's structure, length, format, or how to interpret the result. The contradiction about optionality further confuses. This is a significant gap for an agent needing to call the tool correctly, so it scores 2.

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 the only parameter is generic ('service input'), so the description must add meaning. It does clarify that input is meant to specify 'scope' and suggests it is optional. However, this directly conflicts with the schema's required flag, making the parameter semantics misleading. The description adds minimal value and introduces a contradiction, so it scores 2.

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

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

The description clearly states the tool's purpose: it produces an industry report on the AI agents market 2026, described as 'deep, cited.' It mentions the target audience (VCs and strategy teams) and the B2B context, which makes the intent clear. However, it does not explicitly differentiate from sibling market report tools (e.g., agtech-market, market-research-report), so it loses a point for lack of explicit sibling distinction.

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

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

The description gives some usage context by stating 'B2B: VCs and strategy teams size the AI agents market,' which suggests when the tool is relevant. However, it does not explicitly state when to use this tool versus alternatives, nor does it mention exclusions or alternative tools. The guidance is implied rather than explicit, so it scores 3.

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 of behavioral disclosure. It does disclose pay-per-use pricing and claims the report is 'deep, cited', but it does not explain the return format, any side effects, or authentication needs. Most critically, it states input is optional while the schema requires it, which is a factual contradiction with the structured definition, undermining trust in the 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 concise (one sentence), but it packs in extraneous metadata like the pricing bracket '[x402: 150.0 USDC on Base, pay-per-use]' which, while potentially useful, is not about tool functionality. The phrasing 'input=optional scope' is cryptic and could be clearer. It is not poorly structured, but it mixes business/pricing info with functional info without clear separation.

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 an industry report generator, but the description does not specify the output format (e.g., PDF, markdown, length, structure) nor the expected input format beyond a vague 'scope'. The contradiction about optional input further reduces clarity. Given the tool's complexity and lack of an output schema, the description is insufficient for an agent to invoke it correctly without guessing.

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 parameter description is only 'service input', which is vague. The description adds the phrase 'input=optional scope', giving some meaning (that input is a scope) but it directly contradicts the schema's required flag. This misleading information is worse than no additional semantics, so the score is below the baseline of 3.

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 an industry report generator for global AI compliance, with a specific target audience (legal/compliance teams at AI companies). It uses a specific verb ('report') and resource ('global AI compliance'), which is distinct from vague alternatives. However, it does not explicitly differentiate itself from sibling tools like compliance-audit or enterprise-compliance, though 'global AI compliance' is reasonably specific.

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 context on the intended use case (B2B legal/compliance teams planning global compliance) but does not explicitly state when to use this tool versus alternatives like compliance-audit or legal-review. It lacks exclusions or 'when not to use' guidance. Additionally, the statement 'input=optional scope' contradicts the schema that marks input as required, which could mislead 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?

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?

With no annotations, the description carries the full behavioral burden, and it does disclose a genuinely useful non-schema fact: pay-per-use cost of 0.01 USDC on Base via x402. Beyond that it says nothing about what the tool returns, its latency, side effects, or authentication needs.

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?

It is short, but the only sentence is off-topic metadata rather than a front-loaded statement of purpose. Brevity here reflects under-specification, not economical writing.

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

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

For a tool with a generic single 'input' parameter, no output schema, and no annotations, the description should at minimum state the task and expected input format. It provides none of that, leaving an agent unable to invoke the tool correctly.

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

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

There is a single required parameter, and the description says nothing about it. Although schema coverage is nominally 100%, the schema's own description is the meaningless placeholder 'entrada', so neither structured data nor prose conveys what input is expected (a company name? a URL? raw 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 never states what the tool does; it only reports provenance ('Auto-created from real demand (97 requests)') and pricing. The name 'analisis-competencia' implies competitive analysis, but the text offers no verb+resource and no differentiation from siblings like 'competitive-analysis', 'competitor-scan', and 'competitor-monitoring'.

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 when-to-use, when-not-to-use, prerequisites, or alternatives are given. The mention of a price does not tell an agent under what conditions this tool should be selected over its near-duplicate 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?

No annotations exist, so the description carries the full burden of disclosing behavior. It does not say what operation is performed, what side effects occur, what is required, or what the output is. Mentioning cost and payment method is not behavioral transparency.

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

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

The text is very short, but it is under-specified rather than concise: the only content is irrelevant to how the tool works. It lacks any functional structure or front-loaded purpose.

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 definition is incomplete. An agent has no way to know what input format is expected, what kind of risk analysis is performed, what the response looks like, or when this tool is preferable to similar siblings.

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

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

Schema description coverage is 100%, so the baseline is 3 even though the description itself adds no parameter meaning. The single 'input' parameter is documented only as 'entrada', which provides little semantic guidance, but the description does not reduce or clarify it further.

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 does not state what the tool does; it only contains provenance and pricing details ('Auto-created from real demand (77 requests)' and 'pay-per-use'). The tool's purpose must be inferred solely from its Spanish name 'analisis-riesgo', which is not reliable.

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 the sibling 'risk-analysis'. No context, prerequisites, or exclusions 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?

No annotations are provided, so the description carries the full behavioral burden. It does disclose a genuinely useful trait not present in the schema — that the tool is paid, pay-per-use, at 10.0 USDC on Base via x402 — which tells the agent about cost and payment rail. It says nothing, though, about what the analysis produces, its depth, or error/limit 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?

Three short fragments, purpose front-loaded, no filler. The "input=detalle de la cartera" clause is somewhat redundant with the schema but is compact enough not to be a real defect.

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 annotations, no output schema, and a paid invocation, the definition should explain what a portfolio analysis returns and what portfolio details are expected. Instead it provides only a one-line purpose and a payment note, leaving the agent under-informed about the actual output and input 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?

With one parameter and 100% schema coverage, the baseline is 3. The schema description ("service input") is essentially meaningless, so the description's "input=detalle de la cartera" does add a little meaning by indicating the expected content, but it supplies no format, structure, or example.

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 clear verb and resource ("Análisis de una cartera de inversión"), so the general purpose is understandable. However, it offers no differentiation from the near-identical sibling "portfolio-analysis", nor from adjacent finance tools like "calcular-volatilidad" or "valor-consolidado", leaving the agent unable to tell which analyzer to pick.

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 when-to-use guidance, no conditions or prerequisites, and no reference to any alternative. The only hint is "input=detalle de la cartera", which describes the payload, not the invocation 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 behavioral burden. It does add genuine value by disclosing the payment model (x402: 0.02 USDC on Base, pay-per-use), which an agent needs to know before invoking. However, it says nothing about what the analysis returns, error-handling, or whether the code is sent externally, leaving meaningful 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?

Two terse fragments with the purpose front-loaded and no filler. The input format and payment note are compact, though the bracketed pricing tag sits awkwardly after the format hint rather than being separated structurally.

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 purpose, input format, and cost, which is a reasonable minimum. It omits any indication of what the analysis returns and which languages are supported, both of which an agent would benefit from given the absent output schema.

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

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

The schema only labels the single parameter as 'service input', which is uninformative, so the description's format spec input='<lenguaje>||<codigo>' is essential — it reveals the required double-pipe delimiter and the language/code concatenation. This compensates well for a vague schema, though it could clarify escaping or edge cases.

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

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

States a specific verb (Analiza) and resource (errores de sintaxis en código), so the agent knows this is a syntax-error analysis tool. It does not explicitly differentiate itself from adjacent code siblings like formatear-codigo, corregir, or revisar-seguridad, but the resource is distinct enough to be recognizable.

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 never says when to use this tool versus alternatives (corregir, formatear-codigo, generar-tests), nor any preconditions. It only supplies the input encoding format, which is a parameter concern rather than usage 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?

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?

With no annotations, the description carries the behavioral burden. It adds 'deep, cited,' audience, and pay-per-use pricing, which are useful. However, it does not disclose output format, response structure, or how the scope input affects 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 short, front-loaded with the report type, and includes important context like B2B audience and pricing. The 'input=optional scope' fragment is terse but slightly ambiguous.

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

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

For a single-parameter tool with no output schema and no annotations, the description is too thin to support confident invocation. The required/optional contradiction and undefined 'scope' format are material gaps, and the response contents are only vaguely promised as 'deep, cited.'

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,' so 'input=optional scope' adds real meaning. But it directly conflicts with the schema's required array, which could mislead an agent into omitting a required parameter. This misleading optionality outweighs the added 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 names the artifact ('INDUSTRY REPORT') and the resource ('The Base (L2) ecosystem'), adding depth and citation promises. This distinguishes it from generic report siblings, though it does not explicitly name an 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?

It specifies a clear use case: B2B builders and investors evaluating opportunities on Base. It gives context but no explicit exclusions or named sibling alternatives, so some inference is required.

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?

No annotations are provided, so the description must carry behavioral disclosure. It reveals a payment model (x402, 0.003 USDC on Base, pay-per-use), which is genuinely useful non-obvious context. However, it does not state whether ranking is deterministic, what the return format is, or 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?

Very short and front-loaded: purpose then input format then payment note. No wasted words, though the payment bracket is somewhat cryptic and could confuse without explanation of when payment is charged.

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 semantic ranking tool with no output schema, the description gives purpose, input syntax, and payment model, which covers the basics. But it omits return shape, ranking direction, limits on number of documents, and how it differs from similar siblings.

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

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

Schema coverage is 100%, and the schema describes input as 'service input'. The description adds the concrete delimiter syntax 'consulta || doc1 || doc2', which is necessary for correct invocation since the schema description does not. That is meaningful value beyond the schema, but only for the single 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?

States a specific verb and resource: ranking documents by meaning against a query. That is a clear semantic-search purpose. It does not explicitly distinguish itself from sibling tools like semantic-search or embeddings-texto, which is held against it, but the 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?

The input format example implies the tool expects a query followed by documents. But there is no guidance on when to use this versus semantic-search, embeddings, or latency/accuracy tradeoffs. Usage is inferred from the input syntax rather than explicitly stated.

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 usefully discloses the commercial model (x402 pay-per-use, 0.02 USDC on Base), which an agent needs before invoking, but says nothing about authentication, error cases, rate limits, or what the computation 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?

Three compact clauses, front-loaded with the purpose, then the input example, then the price tag. No padding, though the mix of Spanish and English framing is slightly inconsistent for a compact spec.

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 definition covers input syntax and cost but omits what comes back (a decimal? percent? over which window?) — a meaningful gap when the return value is the whole point of the tool.

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

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

The schema declares the single parameter only as the generic "service input", so the description is what actually defines the interface: input='btc,30' (moneda,dias). That concretely conveys format, ordering, and the units (coin, days) an agent would otherwise have to guess.

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?

"Volatilidad histórica de una cripto" states the resource (crypto volatility, historical) specifically enough to separate it from siblings like crypto-price, precio-cripto or historial-precios, which return prices rather than a volatility metric. The verb is implied by the tool name rather than spelled out in the description, so it falls just 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?

No when-to-use guidance and no routing to alternatives: it never says how this differs from the many crypto/price tools in the sibling list (precio-cripto, crypto-price, historial-precios, trading-signal). The only context is the input format and cost, not applicability.

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 add genuine behavioral context that structured fields lack: local execution and a pay-per-use x402 charge of 0.03 USDC on Base. However, it omits anything about latency, limits, failure behavior, or whether the payment is settled per call.

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

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

Three compact segments — purpose, input shape, pricing — with the purpose front-loaded and no filler. The bracket notation for pricing is slightly telegraphic but readable.

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

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

With no output schema, the description usefully names the three output classes, and it discloses the x402 cost an agent must budget for. For a single-parameter classifier this is close to complete; only language handling and error behavior are unaddressed.

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 there is a single 'input' parameter, so the schema already documents the contract. The description's 'input: {texto}' merely restates that the field takes text, adding no 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?

States a specific verb+resource ('Análisis de sentimiento') and enumerates the output space (positivo/negativo/neutral), so an agent knows exactly what it produces. The 'local' qualifier hints at differentiation from the sibling 'sentiment', but the relationship to that sibling is never made explicit.

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 when-to-use guidance and no routing against the many overlapping siblings (sentiment, clasificar-texto, clasificar-tema, classify-topic). The only contextual note is the payment model, which is about cost, not 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 full burden. It usefully discloses that classification is zero-shot via embeddings and, importantly, that invocation is paid (0.03 USDC on Base via x402), which an agent needs before calling. It says nothing about output format, latency, 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?

Very compact and front-loaded: purpose first, then input shape, then the pricing tag. Nothing is padded, though the input-shape fragment is too terse to be unambiguous.

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?

No output schema exists, so the description should explain what a classification returns (labels, scores, top-k), but it does not. It also leaves the input contract contradictory rather than resolved, so an agent lacks what it needs to call and interpret the tool reliably.

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

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

The schema declares a single string parameter described only as 'service input', yet the description claims the input is an object {texto, categorias?}. That is a shape conflict rather than added clarity, and the optional 'categorias' is not reflected in the schema at all, which could mislead an agent about how to call the 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?

States a specific verb+resource ('Clasifica un texto por tema') and adds the method (zero-shot, embeddings), so the agent knows this is a zero-shot topic classifier. However it offers no differentiation from near-identical siblings such as topic-classify, classify-topic, or clasificar-texto.

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 when-to-use guidance, no conditions, and no named alternative among the many sibling classifiers. The only extra context is pricing, which is not usage 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?

No annotations are provided, so the description carries the full behavioral burden. It usefully discloses the payment model ("x402: 0.002 USDC on Base, pay-per-use"), which is real value beyond the schema, but says nothing about the classification taxonomy, output shape, latency, or whether categories are fixed or inferred.

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

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

Three short fragments, front-loaded with the core action and no wasted prose. The pricing bracket is compact and clearly set apart.

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 what a classification call returns (label only? candidate set? confidence?) and its constraints. It omits all of that, leaving the return contract undefined.

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 there is a single parameter, so the baseline is 3. The description's "input=texto" merely restates what the schema already defines and adds no format, length, or language 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?

States a specific verb ("Clasifica") and resource ("un texto") plus the classification axis ("por tema/categoría"), so the agent knows exactly what the tool does. However, it gives no differentiation from the near-identical siblings classify-topic, topic-classify, or sentiment, all of which overlap this space.

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 the many overlapping classification siblings, nor any stated preconditions or exclusions. The agent must guess whether this differs from classify-topic or topic-classify.

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, the description carries the full burden of behavioral disclosure, yet it only exposes the payment aspect (0.02 USDC on Base, pay-per-use). It does not state what the tool does, what side effects (if any) occur, what auth is needed, or what the response looks like.

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, but it is a fragment rather than a well-structured tool description, and it fails to front-load any purpose. The only substantive content is pricing, which does not earn its place in a tool 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?

The tool is extremely simple (one required parameter, no output schema), yet the description provides no functional context whatsoever. An agent cannot infer what 'codigo' accepts, produces, or why to call 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?

Schema description coverage is 100% (one required 'input' string, described only as 'entrada'), so the baseline is 3. The tool description adds no extra meaning to the parameter, but the high coverage keeps this at the 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 'Auto-created from real demand (75 requests) [x402: 0.02 USDC on Base, pay-per-use]' contains no verb, no resource, and no action. It only describes provenance and pricing, leaving the tool's function completely unspecified and indistinguishable from sibling code tools like code-gen and code-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 is provided about when to use this tool or which alternatives might be preferable. The mention of 'real demand' hints at popularity but offers no context for selecting this tool over the many code-related 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, 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?

No annotations are provided, so the description carries the full burden. It mentions a pay-per-use cost and the input format, but does not disclose behavior beyond that: no details on output format, language of the draft, jurisdiction, or any limitations. The '[x402: 0.15 USDC on Base, pay-per-use]' is a pricing note, not behavioral transparency about the operation itself.

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, two sentences with no wasted words. The key information—what it does and the input—is front-loaded TV, and the pricing note is appended. It 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?

Given the tool generates legal contracts, there is a high need for context: contract type, jurisdiction, parties, etc. The description provides only the input field and pricing. With no annotations or output schema, the description is insufficient to guide an agent on how to formulate a complete prompt or what to expect in the 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 description coverage is 100%, so the parameter 'input' is already documented as 'service input', which is vague. The description adds that it is a 'descripción del contrato', providing a bit more meaning than the schema, but it does not clarify expected format, language, or length, so it only partially adds value.

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

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

The description clearly states the tool generates a contract draft and specifies the input as a contract description. It uses a specific verb ('genera un borrador') and resource ('contrato'), which distinguishes it from related tools like 'contract-draft' in the sibling list, though it doesn't explicitly name that sibling.

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

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

There is no guidance on when to use this tool versus alternatives like 'contract-draft' or 'legal-review'. It implies usage by the description itself, but lacks any explicit context or exclusion of other 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 full burden, and it discloses only that the call costs 0.01 USDC on Base on a pay-per-use basis — a genuine cost/auth detail. It says nothing about what the operation actually does, whether it mutates anything, or what it 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?

It is a single short line, so it is not bloated, but every word is spent on provenance and pricing rather than on the tool's function. Concise but not front-loaded with anything actionable.

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 task tool with no annotations, no output schema, and a contentless parameter, the description supplies none of the missing context. An agent cannot determine what the tool will do with 'input' or how to use the result.

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

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

Schema coverage is nominally 100%, but the sole parameter's description is just 'entrada' ("input"), which is a tautological restatement of the field name and conveys no semantics. The tool description adds nothing further, so the agent learns neither format nor expected content.

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 never states what the tool does. It only reports provenance ('Auto-created from real demand') and a payment model, leaving the agent to guess from the name 'corregir' that it corrects/proofreads text. There is no verb+resource and no differentiation from siblings like 'proofread' 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?

No when-to-use, when-not-to-use, or alternative is given. The agent receives no signal about what kind of input this tool expects or when it should be preferred over the many closely related text tools in 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 burden. It usefully discloses the payment model (x402, 0.02 USDC on Base, pay-per-use), which is real behavioral context an agent needs. However it says nothing about output shape, minimum row/column requirements, or failure modes for non-numeric or malformed CSV.

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

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

Three compact clauses front-load the purpose, then input format, then cost. Nothing is wasted, though the bracketed pricing token is slightly dense relative to the otherwise terse phrasing.

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

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

For a one-parameter tool with no output schema, the essentials are present: what it computes, what it accepts, and what it costs. It stops short of describing the returned matrix or any size constraints, but the low complexity keeps this from being a serious 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 nominally 100%, but the schema description for the single parameter is only the generic 'service input'. The description compensates by stating 'input=URL o CSV crudo', which clarifies the accepted source formats beyond the schema. Baseline 3 is exceeded because this is genuine added 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?

States a specific verb+resource: 'Matriz de correlación de un CSV' is unambiguous about the computed artifact and the input type. It does not, however, distinguish itself from near-neighbors like estadisticas-descriptivas or detectar-outliers in the sibling list.

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

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

There is no when-to-use guidance, no prerequisites, and no named alternative among the many analytics siblings (estadisticas-descriptivas, detectar-outliers, pivotar-datos). The agent must infer usage entirely from the purpose phrase.

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 behavioral burden. It adds useful context like 'deep, cited', optional scope, and pay-per-use cost, but it does not describe output format, citation style, data sources, or side effects. For a read-only report tool, the disclosed traits are moderately helpful but not fully 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-loads the report topic before providing scope and audience. The trailing '[x402: 120.0 USDC on Base, pay-per-use]' is somewhat cryptic, but nearly every clause 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 research tool, the description covers topic, depth, citation, audience, and scope. It is missing output expectations since there is no output schema, and it does not help an agent choose among adjacent crypto/market-report siblings like x402-payments-landscape.

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

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

The description adds meaning to the otherwise generic schema property 'service input' by interpreting it as an optional scope. However, this conflicts with the schema marking input as required, and there is no guidance on how the scope should be phrased.

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 a deep, cited industry report on the state of crypto payments in 2026 and names the target B2B audience (PSPs and merchants). It does not use an explicit verb like 'generate', but the intent is clear and the topic is specific enough to distinguish it from many unrelated 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?

It gives concrete usage context: PSPs and merchants evaluating crypto payment integration, with optional scoping via input. It does not list exclusions or explicitly name alternative report tools, but the audience and use case are 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 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 full burden. It discloses the pay-per-use cost ('[x402: 0.01 USDC on Base, pay-per-use]') and implies a generative operation. It does not mention potential side effects, response format, or performance characteristics. For a simple distillation tool this is acceptable but not thorough.

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, with only two sentences and a cost tag. It front-loads the purpose and usage, and every word contributes. There is no wasted 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?

For a tool with a single parameter and no output schema, the description covers the purpose, usage, and cost. It lacks details about the output format or potential limitations, but given the simplicity of the tool, it is sufficiently complete for an agent 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 already describes the parameter as 'concepto/cosa a destilar' with 100% coverage. The description adds 'input=concept' and clarifies the output is a core principle, but this is largely redundant. The baseline of 3 is appropriate since the schema covers the meaning; the description offers marginal extra clarity.

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 action: 'Distill the irreducible essence of a concept (its core principle).' It identifies the specific verb and resource, and is not a tautology. It clearly distinguishes the core purpose from generic summarization or extraction 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 says 'Use it to get the conceptual core of something,' providing clear context on when to apply it. However, it does not mention alternatives or exclusions, even though sibling tools like destilar-batch, destilar-structured-json, and destilar-tables exist. It stops short of explicit routing guidance.

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

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

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?

No annotations are provided, so the description carries the full burden. It usefully discloses the payment model (x402, 0.001 USDC on Base, pay-per-use), which is real behavioral context, but says nothing about the return format, supported languages, or accuracy 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?

Three short sentences, purpose front-loaded, zero filler. Efficient, though the terse fragment style leaves room for more substance.

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-param tool this is minimal but workable. There is no output schema, and the description never indicates what the detection returns (e.g., a language code or name), which is a real gap for an otherwise trivial 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?

With a single parameter at 100% schema coverage, the baseline is 3. The description's 'input=texto' clarifies that the input is the text to analyze, but adds little beyond what the schema already implies.

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

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

States a specific verb+resource (detect the language of a text), which is unambiguous. However, it offers no differentiation from near-identical siblings like detect-language and language-detect, so the agent cannot tell which detector to pick.

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 the many sibling language-detection and translation tools. No prerequisites, no context, no alternatives are named.

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 usefully discloses the paid model ('x402: 0.02 USDC on Base, pay-per-use'), which is real behavioral context an agent needs before invoking. However, it omits failure behavior, size/type constraints on the CSV, and what happens for columns without numeric 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?

Three tight components: purpose, input grammar, and pricing. Zero filler, and the primary purpose is front-loaded before the format 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 the coverage is close to adequate, but with no output schema the description should say what is returned (outlier values, indices, a flag, or a modified CSV). The payment note is present but return shape is left entirely unspecified.

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 nominally 100% but the schema description is only 'service input', adding no meaning. The description compensates by defining the actual payload grammar: '<url_o_csv>||<columna>', including the pipe delimiter and the order of URL then column name. This is genuine 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?

States a specific verb and algorithm ('Detecta outliers (IQR)') applied to a precise resource ('una columna de un CSV'). The IQR qualifier distinguishes it from generic stats tools like estadisticas-descriptivas or limpiar-csv, though no sibling is named to make the routing explicit.

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 when-to-use, when-not-to-use, or alternative routing is given. The only guidance is the input string format, which is a parameter concern rather than usage guidance. An agent gets no signal about prerequisites (valid CSV, column existence) 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?

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 carries the full burden. It mentions pay-per-use pricing and a specific blockchain payment rail, which is useful, but it does not disclose what happens with the input, whether the report is generated synchronously, how citations are handled, or any side effects. The 'x402' payment detail is a behavioral trait but is cryptically presented.

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 core purpose, but the 'x402: 100.0 USDC on Base, pay-per-use' fragment is dense and may confuse an agent. It earns its place as pricing info but 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 no output schema and no annotations, the description leaves significant gaps: what the report contains, how the optional scope is used, what the output format is, and how payment is triggered. The audience hint helps but is not enough for an agent to invoke 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?

Schema coverage is 100%, but the only parameter description is 'service input', which is generic. The description adds that input is 'optional scope', giving some meaning beyond the schema. However, it does not explain what format the scope should take or how it influences the report.