Agent Utility API

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

The description adds the per-call cost via x402, which is valuable behavioral context beyond the schema. However, it does not disclose error handling, domain format requirements, or whether results may be partial, and no annotations are provided.

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

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

The description is two sentences, with the purpose and key outputs front-loaded, and the pricing note efficiently appended. Every clause adds value.

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 simple one-parameter input and no output schema, the description covers the core function, output fields, and pricing. It lacks details on response structure or edge cases, but is largely sufficient for a straightforward lookup.

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 'domain' is not described beyond its name in the schema, and the description only says 'from a domain' without specifying expected format (e.g., bare domain vs URL). Since schema description coverage is 0%, the description fails to compensate.

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

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

The description states 'Company enrichment from a domain' and enumerates the specific data returned (name, description, logo, social links, contact emails/phones), clearly distinguishing it from sibling tools like profile_lookup or kdp_lookup.

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

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

The description implies usage for enriching a company's data given a domain but does not explicitly contrast with alternative tools or state when not to use it. No exclusions or alternatives are mentioned.

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 source, network, and cost ('FREE'), but does not mention behavioral aspects like rate limits, caching, or behavior for unsupported symbols. Not misleading but minimal.

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, front-loaded with the core purpose. 'No payment needed' is redundant with 'FREE', but overall concise and easy to scan.

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

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

For a single-parameter tool with no output schema, the description provides key context: source, network, and supported assets. It omits return format, but the purpose is obvious. Adequate for the tool's simplicity.

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 0%, but the description lists example values for the 'symbol' parameter (ETH, BTC, SOL, DOGE, AVAX, LINK, USDC, EUR vs USD), adding meaning beyond the bare schema. However, exact format (case, pair notation) is not specified.

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

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

The description clearly states a specific verb and resource: 'live crypto/FX price' from Chainlink oracles on Base. It lists supported assets, distinguishing it from siblings like company_lookup or email_verify.

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 clear context (live data, Base network, supported symbols) implying when to use. No explicit exclusions or alternatives, but siblings are unrelated, so the context suffices.

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 adds useful context including the verification methods, output categories, and a cost of $0.01/call via x402. This goes beyond a bare 'verify email' and informs the agent of side effects (cost) and result semantics.

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

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

The description is extremely concise—two short sentences—and front-loaded with the action verb 'Verify'. Every part provides value: function, method, output, and cost. There is no unnecessary filler.

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

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

Given the tool has only one parameter, no output schema, and no annotations, the description does well to cover the purpose, verification logic, result categories, and pricing. It could additionally mention error behavior or required input format, but for a straightforward verification tool, the coverage is reasonably 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 contains a single parameter 'email' with zero description coverage. The description compensates partially by implying the email parameter is the address to verify, but it does not add format constraints or clarify edge cases. For a single obvious parameter, this is sufficient but not exemplary.

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: verifying an email address using specific checks (syntax, MX records, disposable/role detection) and gives an output classification. This distinguishes it from sibling tools like company_lookup or web_read, which target different resources.

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 tool is used when email verification is needed, but it does not explicitly state when to use it over alternatives, nor does it mention any exclusions or complementary tools. There is no direct guidance for tool selection beyond the intrinsic purpose.

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

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

No annotations are provided, placing the transparency burden on the description. It discloses the cost per call via x402, which is useful, but it does not describe output format, error handling, file size limits, or required permissions. The phrase 'structured PDF extraction' gives a hint but remains vague.

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, two short sentences, with no filler. The key information is front-loaded.

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

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

Given its simplicity, the description covers the core purpose but lacks detail on return structure, error cases, and the role of doc_type. With no output schema or annotations, more completeness would be expected.

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 provides two parameters, and description coverage is 0%. The description does add meaning for file_base64 by specifying base64-encoded PDF bytes, but doc_type is entirely undocumented, leaving the second parameter unexplained.

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

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

The description clearly identifies the tool's function: extracting structured data from PDFs given base64 input. The phrase 'structured PDF extraction' distinguishes it from any sibling tools, none of which handle PDFs.

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 on when to use this tool versus alternatives is provided. However, since sibling tools serve unrelated purposes (lookups, crypto, transcription, web reading), usage is implied, but no exclusions or prerequisites are 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 of behavioral disclosure. It does reveal the cost per call and the type of data returned, which is useful. However, it does not disclose parameter requirements, error behavior, or access needs, leaving significant behavioral aspects unexplained.

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

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

The description is a single, concise sentence that front-loads the purpose and includes a cost note without unnecessary detail. It avoids fluff and is easy to parse.

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

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

Given the tool has 3 optional parameters, no annotations, and no output schema, the description is significantly incomplete. It fails to explain how to use the parameters, what format the output will take, or any constraints (e.g., need for at least one parameter). The cost and data type list offer some context, but not enough for a new agent to use 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?

The input schema has 0% description coverage, and the description provides no information about the three parameters (asin, keyword, category). It does not clarify which parameter is required, how they interact, or what each controls, so the description adds no value for parameter understanding.

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: 'KDP/Amazon competitive data: BSR, pricing, review counts, niche score.' It identifies the specific resource (KDP/Amazon) and the data types returned, which distinguishes it from sibling tools like company_lookup or crypto_price.

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

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

The description implies usage for KDP/Amazon market research but provides no explicit when-to-use guidance or alternatives. The cost note ('$0.05/call via x402') is a usage consideration, but there is no mention of when to choose this tool over siblings 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 carries the full burden of behavioral disclosure. It states the tool is a 'public profile lookup,' which implies read-only behavior and a limitation to public profiles. It also mentions the cost ('$0.05/call via x402') and lists the return fields. While it does not describe failure modes or auth requirements, it provides meaningful behavioral context beyond a generic lookup.

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

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

The description is a single sentence that efficiently conveys the core function, supported platforms, returned data, and cost. Every phrase adds value with no redundancy, making it easy for an agent to parse quickly.

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

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

Given the tool is a simple 2-parameter lookup with no output schema, the description is largely complete: it specifies the platforms, the type of data returned, and the cost. Minor gaps include response format and exact platform parameter values, but these are not critical for a straightforward profile fetch. It is adequate for the tool's complexity.

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

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

The input schema has zero descriptions, so the description must compensate. It does clarify that 'platform' likely accepts values like 'X' or 'LinkedIn' and that 'handle' is the profile username. However, it does not provide explicit parameter definitions, format examples, or constraints. The description adds some meaning (platform options, handle implication) but not comprehensive 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 states the tool performs 'X (Twitter) or LinkedIn public profile lookup' and lists the specific data points returned (name, bio, followers, company, experience). This distinguishes it from sibling tools like company_lookup, which focuses on companies. The verb 'lookup' and resource 'profile' are specific and 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 description implies the tool is for fetching public profiles on X/Twitter or LinkedIn by specifying these platforms and the data fields. It does not explicitly exclude alternatives or name a competing tool, but the context is clear enough that an agent would understand when to use it. No exclusions or alternative references are given, but the purpose is sufficiently distinct from 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, the description must carry the burden of behavioral disclosure. It does mention a cost ('$0.10/call via x402') and the output format ('structured transcript + timestamped summary'), which are useful. However, it omits details about potential side effects, rate limits, error conditions (e.g., video unavailable), or authentication requirements, leaving gaps given the absence of annotations.

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

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

The description is a single, front-loaded sentence that efficiently conveys the tool's primary function and a key cost detail. There is no wasted verbiage, and the structure is clear and scannable.

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

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

Given the tool has two parameters, no output schema, and no annotations, the description is too thin to be fully effective. It omits critical context such as what the 'format' parameter does, possible output structures, and any limitations or prerequisites. While the basic purpose is clear, the missing parameter semantics and absence of behavioral details leave the description incomplete for an agent to confidently 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?

The input schema has two parameters (url and format) with no descriptions, and schema description coverage is 0%. The description mentions 'YouTube video' (implicitly the url) and 'transcript + timestamped summary' (likely related to format), but it fails to explain the 'format' parameter's allowed values or behavior. The url parameter is at least somewhat inferred, but format remains ambiguous, so the description does not adequately compensate for the schema's lack of detail.

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

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

The description clearly states the core function: 'YouTube video -> structured transcript + timestamped summary.' This specifies the resource (YouTube video) and the output (transcript and summary), making it distinct from sibling tools like web_read or extract_pdf. The arrow effectively conveys the transformation, and the tool name reinforces the verb.

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

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

The description implies usage for transcribing YouTube videos, but it doesn't explicitly state when to use this tool vs alternatives (e.g., web_read for non-transcription web content). It also doesn't mention exclusions or prerequisites like video length limits or age restrictions. The guidance is solely implicit through the purpose.

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

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

With no annotations provided, the description carries the full burden. It does disclose that the output is clean, boilerplate-stripped text and mentions a cost of $0.02/call via x402, which adds useful behavioral context. However, it omits potential failure modes, rate limits, or handling of dynamic content, leaving gaps.

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

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

The description is a single sentence that conveys the core function, output quality, and pricing with no fluff. It is front-loaded and concise, earning the highest score.

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

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

The tool is simple (one parameter, no output schema), and the description covers purpose, output characteristics, and cost. It lacks details on error behavior or edge cases, but these are less critical for a straightforward read operation; thus it is mostly 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 single parameter 'url' has no schema description (0% coverage), and the description does not explicitly define it beyond implying the tool reads webpages. However, the parameter is self-evident from the tool's purpose, so the description provides minimal but adequate context for a straightforward 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 clearly states the tool extracts clean readable text from webpages and strips boilerplate, which is a specific verb-resource pair. This distinguishes it from sibling tools like extract_pdf or transcribe_youtube, which target different content types.

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 on when to use this tool versus alternatives is provided. The description implies it is for reading webpages, but there is no mention of exclusions, such as not for PDFs or videos, nor does it reference sibling tools.

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