x402-vend — WebIntel

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 transparently discloses the per-call fee, payment method, input (domain), and a detailed description of the output structure. It does not mention any side effects, rate limits, or authorization needs, but the tool appears to be a read-only assessment with no destructive actions.

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 moderately concise, covering cost, input, output details, and differentiation from siblings in a few sentences. It is front-loaded with the most important info (cost and payment). Could be slightly more concise, but every sentence serves a 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?

Given that there is no output schema, the description does an excellent job explaining the return value: a structured judgment with specific fields (company description, category, audience, business model, market, language, analyst take, sales hook, confidence ratings, evidence quotes). It also covers cost and payment method completely.

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?

Only one parameter 'domain' exists, and the schema already provides a description and example. The tool description adds context: the domain is from a company's homepage, and the output is a structured judgment. Schema coverage is 100%, so baseline is 3; the description adds a bit more meaning by explaining what the domain is used for.

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: 'Get an AI analyst's read on a company from its homepage.' It specifies the verb (assess), the resource (company via domain), and the output format (structured judgment). It also distinguishes from sibling tools like /enrich and /report by calling itself the 'qualitative complement.'

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

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

The description provides clear usage context: cost ($0.10 per call), payment method (x402 USDC on Base), and that no account is needed. It briefly contrasts with siblings ('qualitative complement to /enrich and /report'). However, it does not explicitly state when not to use this tool or list prerequisites beyond having a domain.

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 provided, but description discloses pricing ($0.02 per call), payment method (x402), no account needed, and specific data returned. Does not mention limitations or destructive 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?

Description is informative but slightly verbose. Front-loaded with purpose and pricing. Each sentence adds value, though could be more concise.

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, description covers input usage, output types, and pricing. Output schema missing, but explanation of returned records compensates. Adequately 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 has 100% coverage with description for 'domain'. Main description adds example and context but does not significantly extend schema's meaning. 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?

Clearly states it provides DNS and email intelligence for a domain, listing specific record types and email provider detection. Distinct from sibling tools like whois or assess.

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

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

Explicitly says 'Give it a domain' and mentions use cases like deliverability checks and lead qualifying. Lacks explicit when-not-to-use but is clear enough.

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, so description carries full burden. Discloses pricing ($0.10 per call via x402), sources (live from homepage/legal pages, VIES), and what it confirms (real EU business). Could add limitations like non-EU domains, but still strong.

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?

Detailed single paragraph, but front-loads pricing and USDC mention which may not be primary. Could be more structured with bullet points. Adequate but not exemplary.

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 parameter, no output schema, and no annotations, the description covers inputs, outputs, target audience, pricing, and unique value. Fully adequate for 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 coverage is 100% with one parameter 'domain'. Description adds context by specifying domain should be for EU/NL targets and explains what the tool returns, adding value beyond 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?

Clearly states it enriches companies from domain, specifying EU/NL focus and listing detailed outputs. Distinguishes itself from generic enrichers by mentioning VIES validation and KvK/BTW numbers.

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

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

Explicitly states ideal use case: qualifying EU/NL leads. Mentions pay-per-call pricing. Does not explicitly contrast with siblings or state when not to use, but context is 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, the description carries full behavioral disclosure. It states the tool is a paid call ($0.15 via x402), no account or API key needed, and describes the data sources used (EU company enrichment, domain age). It does not mention side effects or rate limits, but for a read-only scoring API, this is sufficient.

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, front-loaded with purpose and cost. Every sentence provides essential information with no redundancy. It is efficient and well-structured.

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

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

For a simple tool with one parameter and no output schema, the description covers purpose, input, output format, cost, and target audience. It lacks details on error handling, but overall it is adequate for an agent 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 single parameter 'domain' is fully covered by the schema description. The tool description adds context that the domain is used for lead scoring, but this is already implied by the purpose. The value added is minimal beyond the schema.

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

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

The description clearly states the tool's purpose: scoring and qualifying B2B leads from a domain. It specifies the output (0-100 score with hot/warm/cold band and reasons) and target audience (EU/NL companies). This distinguishes it from sibling tools like 'enrich' or 'assess'.

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

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

The description provides explicit usage context: for sales and lead-gen agents prioritizing EU/NL companies. It mentions pricing and payment method. However, it does not explicitly contrast when to use this tool versus alternatives like 'enrich' or 'assess', though the context implies use for lead scoring.

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?

Describes the extracted metadata fields comprehensively and mentions pricing model. No annotations exist, so description carries full burden; missing details on error handling or rate limits, but overall sufficient.

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 concise yet informative. Front-loaded with name and pricing, then lists capabilities. Every sentence adds value with no fluff.

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

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

For a tool with one parameter and no output schema, the description fully explains input, output, and constraints. No significant gaps.

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

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

Schema coverage is 100% with one parameter 'url'. Description adds meaning by listing what metadata will be extracted from the URL, which is beyond schema's basic 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?

Clear verb-resource pair ('extract structured metadata'), specific about what it extracts (page title, meta description, OG tags, etc.), and distinguishes itself from sibling tools like assess or read by focusing on metadata extraction.

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

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

Explicitly states when to use (give it a URL), that no account or API key is needed, and that payment is per call with x402. Provides clear context for usage.

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 takes full responsibility for behavioral disclosure. It reveals pricing ('$0.01 per call'), the output format (markdown with preserved headings, links, code blocks, blockquotes), and that ads and boilerplate are stripped. It also states 'No account, no API key,' which sets correct expectations. However, it omits details about error handling or timeouts.

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

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

The description is concise and well-structured. It starts with the tool name and pricing, then explains the action and output, and ends with the use case and no-account policy. 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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description covers the essential aspects: purpose, input, output format, pricing, and use case. It could mention error cases (e.g., invalid URL) but is otherwise complete for a straightforward fetching 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% with one parameter (url) described as 'Full http(s) URL.' The description reinforces that the URL should be an article page ('Give it a URL and get back the article text') but adds no new semantic detail beyond what the schema provides. Baseline 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 clearly states the tool's purpose: 'Turn any web page into clean, readable markdown.' It specifies the action (read a page), the resource (web page URL), and the output format (markdown). It also distinguishes itself from sibling tools by focusing on page reading, while siblings like dns and whois serve different domains.

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 clear use case: 'Ideal for feeding page content to an LLM.' It implies when to use the tool (when you need clean text from a URL) but does not explicitly state when not to use it or provide alternatives. Given the sibling tools are distinctly different, the guidance is sufficient.

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 cost ($0.25, x402 on Base), live computation, and the types of data returned. It does not mention failure modes or rate limits, but provides substantial 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 is a concise paragraph with no wasted words. It front-loads the name and cost, then lists capabilities. Could be slightly more structured, but overall 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 no output schema and moderate complexity, the description covers the main return categories (tech stack, DNS, contacts, etc.) and pricing. It lacks detail on output format or pagination, but is sufficient for understanding the tool's value.

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% for the single parameter 'domain'. The description adds minimal extra meaning beyond the schema, simply restating that a domain is provided. Baseline of 3 applies because the schema already documents the parameter well.

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 producing a 'full intelligence dossier on a domain' with specific deliverables. It distinguishes from sibling tools by positioning itself as an all-in-one alternative to chaining multiple calls (e.g., dns, meta, whois).

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 indicates when to use the tool: when you need a complete company profile and would otherwise chain multiple calls. It implicitly advises against using it for simpler single-purpose queries, but does not explicitly state when not to use it or list 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 fully discloses behavior: method (robots.txt, sitemap.xml, indexes), limit (500 pages), cost ($0.01 per call via x402), and no account needed. This is comprehensive.

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

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

Three sentences covering purpose, method, use cases, and pricing. No wasted words; each sentence adds distinct 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?

For a simple tool with one parameter and no output schema, the description provides sufficient detail: input, output (list of URLs), limits, and payment. No 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 'domain' is described in schema as 'Domain name, e.g. example.nl'. The description adds context ('Give it a domain'), reinforcing its usage. Schema coverage is 100%, and the description adds meaningful 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 tool's purpose: 'Discover every page on a website' by scanning robots.txt and sitemap.xml, up to 500 pages. It differentiates from sibling tools like 'enrich' or 'meta' by specifying its role in site structure mapping.

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?

Explicit use cases provided: 'Use it to map a site's structure before crawling or to find which pages are worth reading.' However, no guidance on when not to use or alternatives among siblings.

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

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

No annotations are provided, so the description carries the full burden. It discloses cost ($0.05 per call), payment method (x402 USDC on Base), and authentication requirements ('no account, no API key'). However, it does not disclose failure modes, error handling, rate limits, or response format, leaving gaps in behavioral expectations.

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 front-loaded with the core purpose, then expands on detected categories and use cases. While it includes pricing and marketing details, these are relevant for usage context. It is well-structured but slightly verbose.

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

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

The tool is simple with one parameter and no output schema. The description explains inputs and use cases but does not describe the output format or structure, leaving the agent unsure what to expect from the tool's response.

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

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

Schema description coverage is 100% with a clear description for the 'domain' parameter. The tool description adds context by specifying 'Give it a domain' and providing example format, but adds no significant meaning beyond the schema.

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

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

The description clearly states the tool's purpose: 'Detect the technology stack behind any website.' It lists specific technologies detected (CMS, e-commerce, etc.) and differentiates from sibling tools like 'dns' or 'whois' by focusing on web tech stack detection.

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

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

The description provides clear use cases: 'Great for technographics, competitor research and enriching sales leads.' It implies when to use this tool but does not explicitly exclude alternatives among siblings, though the focus on tech stack is distinctive.

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 fully discloses behavior: cost per call, x402 payment, no account needed, and the return of validity, company name, and address. This is comprehensive for a simple tool.

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 slightly verbose but front-loaded with key information (cost, purpose). Every sentence contributes value, and it is well-structured without 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 tool's simplicity (one parameter, no output schema), the description covers purpose, usage, cost, and result format completely. It adequately equips an agent to select and invoke 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?

Schema description coverage is 100% for the single parameter 'vat'. The description adds value by providing example formats (e.g., NL857081876B01), which enhances understanding 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 validates EU VAT numbers against the VIES service, with specific verb 'validate' and resource 'EU VAT number'. It provides examples and distinguishes from sibling tools by focusing on a unique 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 explicitly says it's built for invoicing, onboarding, KYB, and compliance agents, giving clear context. It does not mention when not to use or alternatives, but the use case is well-defined.

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 the cost ($0.02 per call via x402), and states it resolves from authoritative registries, making it reliable for .nl and EU ccTLDs. It does not mention rate limits or auth, but the behavior is well-described for a 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 concise and well-structured, with key information front-loaded (purpose, cost, what it returns). Every sentence adds value; 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?

Given the simple single-parameter tool with no output schema or annotations, the description covers the core functionality, data returned, cost, and use case. It lacks error handling or example responses, but is sufficiently complete for an agent to understand the tool's purpose and output.

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

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

The input schema has one parameter 'domain' with a description. Schema coverage is 100%, so the description adds little beyond what the schema already provides. The description restates the parameter's purpose but does not add new semantic information.

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: to look up a domain's registration and age via RDAP. It lists the specific data returned (registration date, age, registrar, etc.) and distinguishes it from sibling tools like dns or enrich by focusing on registration info.

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 the tool is useful for lead qualification and trust scoring, implying when to use it, but it does not explicitly state when not to use it or compare it to alternatives like dns or enrich. No exclusions or alternative guidance is provided.

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