Agent Utility API

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

No annotations provided, so description carries full burden. It discloses cost ($0.01/call) and implies it's a data enrichment operation. However, it lacks details on rate limits, authentication needs, or behavior for invalid domains.

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?

Single sentence with key info front-loaded. Lists what the tool returns and cost. No wasted words.

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

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

For a simple tool with one param and no output schema, the description covers the primary purpose and return data. Missing details on error handling, pricing clarity (via x402?), and prerequisites.

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 described as the source for enrichment, adding context beyond the schema (which has 0% description coverage). However, it does not specify expected format (e.g., 'example.com' vs full URL), leaving some ambiguity.

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

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

The description clearly states it performs company enrichment from a domain, listing specific data points (name, description, logo, social links, contact info). It is specific but does not explicitly differentiate from sibling tools like profile_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?

No guidance on when to use this tool versus alternatives. Sibling tool names differ but no comparison or conditions provided. The cost mention is present but not as a usage criterion.

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 provides some behavioral context: it mentions 'Live' indicating real-time data and the cost '$0.005/call via x402'. However, it does not explicitly state that the operation is read-only or other important traits like idempotency or side effects.

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

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

The description is a single sentence that efficiently conveys the tool's purpose, supported assets, source, and cost. It is front-loaded with the core functionality and contains no wasted words.

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 (one required parameter, no output schema), the description lists accepted symbols and cost but omits the return format (e.g., a number, string, object). An agent might need to infer the response structure, which is a gap.

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

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

The input schema has one 'symbol' parameter with no description (0% schema coverage). The description compensates by listing all valid symbols: ETH, BTC, SOL, DOGE, AVAX, LINK, USDC, EUR vs USD. This adds significant meaning beyond the schema, though it does not specify case sensitivity or format.

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

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

The description clearly states the tool provides 'Live crypto/FX price from Chainlink oracles on Base' with a specific list of supported assets. The verb (get/fetch) is implied, and the resource is unambiguous. The listed siblings (e.g., company_lookup, email_verify) are in different domains, so no confusion.

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 is provided. There is no mention of when not to use it or what other tools might be preferable for different scenarios (e.g., historical data).

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 verification steps (syntax, MX, disposable/role) and the three possible outcomes (valid, risky, invalid), as well as the pricing model. This gives good behavioral insight beyond what annotations would typically cover.

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 consists of two concise sentences. The first front-loads the purpose and key checks, the second adds cost information. Every sentence adds value without 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?

Despite no output schema, the description explicitly states the return categories (valid|risky|invalid). It also covers the verification process and cost. For a single-parameter tool with no nested objects, the description is sufficiently 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?

There is only one parameter ('email') with no schema description (0% coverage). The description indirectly explains its purpose by stating it verifies an email address, but does not add format specifics (e.g., expected format, example). However, the parameter is simple and self-evident, so the description provides adequate context.

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 specific verbs ('Verify') and resource ('email address'), lists the verification checks (syntax, MX records, disposable/role detection), and specifies the output categories (valid|risky|invalid). It clearly distinguishes this tool from siblings 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 mentions a cost model ($0.01/call via x402), implying it's a paid service, but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or context for selection among siblings 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 burden. It discloses cost ($0.05/call via x402) and the input format (base64-encoded PDF). However, it does not mention mutability, error handling, size limits, or output format, leaving significant transparency gaps.

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

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

The description is extremely concise, using a single informative sentence front-loading the purpose and appending cost. No redundancy, but could include more detail without losing 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?

Given 2 parameters, no annotations, no output schema, the description is incomplete. It lacks details about what 'structured' means, expected output, constraints (e.g., file size), and error scenarios. Significant gaps remain for an agent to use correctly.

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

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

Schema description coverage is 0%, so the description must compensate. It mentions 'base64-encoded PDF bytes' relating to file_base64 but does not explain doc_type or its default behavior. This provides 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 states the tool extracts structured data from base64-encoded PDF bytes, using specific verb 'extraction' and resource 'PDF'. There are no sibling tools performing PDF extraction, so it distinguishes well.

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

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

The description implies usage for PDF extraction but provides no explicit guidance on when to use this tool versus alternatives. It mentions cost and payment method, which inform usage conditions but not exclusions or when-not scenarios.

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 full burden, but it only mentions cost and data returned, omitting behavioral traits like rate limits, data freshness, or side effects.

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

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

The description is concise with one sentence for purpose and another for cost, and it is front-loaded with the main function, though it could be more structured.

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

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

With no output schema, no parameter descriptions, and no behavioral annotations, the description lacks critical context for a tool that has three parameters and unclear usage modes.

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

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

Schema description coverage is 0%, and the description does not explain the meaning or usage of the three parameters (asin, keyword, category), failing to add value beyond the schema field names.

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

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

The description clearly states the tool provides Amazon KDP competitive data including BSR, pricing, review counts, and niche score, distinguishing it from siblings 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 mentions cost and data type but provides no explicit guidance on when to use this tool versus alternatives or when to use specific parameter combinations.

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 the cost per call and that it's a public lookup, but does not disclose potential rate limits, authentication requirements, error handling, or what happens if the profile doesn't exist.

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, front-loading the purpose and key output fields. However, it could be improved with a more structured approach, such as separating the output fields into a list.

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 and lack of annotations, the description covers the basic purpose and output fields but omits parameter descriptions, error scenarios, and rate limits. It is adequate but not thorough.

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. The description implies that platform values are likely 'X' or 'LinkedIn' and handle is the username, but it does not explicitly link these to the parameters. Some meaning is added, but not sufficiently detailed.

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

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

The description clearly states the tool performs a public profile lookup for X (Twitter) or LinkedIn, and lists specific fields retrieved (name, bio, followers, company, experience). This is specific and distinguishes it from sibling tools like company_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 does not explicitly say when to use or avoid this tool. It implies usage for individual social media profiles, but lacks explicit guidance on alternatives or context where it might not be appropriate, such as when needing email verification or company data.

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 cost but fails to disclose key behaviors like supported languages, video length limits, processing time, or authentication requirements.

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, which is concise but lacks structure. It could benefit from separating the purpose, cost, and parameter details.

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

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

The tool has moderate complexity (transcription) but the description is too sparse. It omits output format, error handling, and usage examples. No output schema exists, so the description should compensate.

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

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

Schema description coverage is 0%, so the description should add meaning to the parameters. It does not explain the 'format' parameter options (e.g., what 'both' entails) or the structure of 'url'. This leaves the agent guessing.

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

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

The description clearly states the tool's function: converting a YouTube video into a structured transcript and timestamped summary. It uses a specific verb ('transcribe') and resource ('YouTube video'), distinguishing it from siblings like web_read or extract_pdf.

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. While the purpose is clear, there is no explicit context for when to choose this over other tools, nor any mention of prerequisites or limitations.

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 discloses core behavior (text extraction, boilerplate removal) and cost ($0.02/call). However, it does not mention error handling, rate limits, authorization requirements, or what happens with invalid URLs. The cost disclosure is a plus but insufficient for full transparency.

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

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

The description is extremely concise: two sentences that state the core purpose and a key behavioral detail (cost). Every word serves a purpose, with no fluff. It is front-loaded with the main action, which aids quick understanding.

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

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

For a simple single-parameter tool, the description provides the essential purpose and cost. However, it lacks details about output format, expected input format, limitations (e.g., max URL length, supported protocols), and error behavior. Given no output schema and no annotations, more context would be beneficial for correct invocation.

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

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

The sole parameter 'url' has 0% schema description coverage. The description says 'from any webpage' which implies the URL is the input, but it adds no format, validation, or contextual meaning beyond the schema. The agent gains no additional insight into parameter semantics from the description.

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

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

The description clearly states 'Extract clean readable text from any webpage, boilerplate stripped.' The verb 'extract' and resource 'webpage' are specific, and the mention of boilerplate stripping adds precision. The purpose is distinct from sibling tools which focus on different domains (e.g., company_lookup, crypto_price), so no ambiguity.

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. While the purpose is clear, there is no mention of prerequisites, limitations (e.g., dynamic content, authentication), or scenarios where it might fail. The description lacks any when-not-to-use or alternative tool suggestions.

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