Web Intel

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds value by specifying exactly what is audited (DNS, SSL, SPF/DMARC) and by disclosing the cost and payment method ($0.01/call, x402 USDC on base). This goes beyond the annotations without contradicting them.

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 three sentences: the first states the core function, the second conveys the value proposition, and the third provides essential cost information. It is front-loaded and every sentence earns its place with 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?

Given the tool's complexity (bulk domain auditing with multiple checks) and lack of an output schema, the description adequately conveys the purpose, input scope, and cost. It does not describe the response format or error handling, but the description is sufficient for an agent to decide whether to invoke it.

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

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

The input schema has 100% description coverage for both parameters (domains and maxConcurrency). The description does not add any additional meaning to these parameters, only indirectly implying the domain list via 'Bulk-audit domains.' Per the rubric, the baseline of 3 applies when schema already documents parameters 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 opens with a specific verb and resource: "Bulk-audit domains: DNS records, SSL certificate expiry, SPF & DMARC email authentication." This clearly distinguishes it from sibling tools like tech-stack-detector or url-to-markdown, which focus on different aspects.

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

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

It provides clear context for when to use: "Find domains that cannot receive email, are easy to spoof, or have expiring certificates — before your clients do." However, it does not explicitly name alternatives or state when not to use, so it falls short of a 5.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful context: the tool is free, requires no wallet, and enumerates the response fields (price, payTo, network). This goes beyond the bare safety annotation profile.

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 short sentences, front-loaded with 'Free — list...' and no filler. Every word earns its place, making it highly scannable 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?

Given zero parameters and no output schema, the description adequately states what will be returned (paid tools with price, payTo address, network) and when to invoke it first. There is no unresolved ambiguity for tool selection or 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 tool has zero parameters and schema coverage is 100% (empty properties object). With no parameters to explain, the baseline of 4 applies, and the description does not need to add parameter-level 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 uses a specific verb ('list') and clearly identifies the resource ('every paid tool in the 'web-intel' bundle') plus the exact details returned (price, payTo address, network). This makes it easy to distinguish from the sibling content-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?

Provides an explicit trigger: 'Call this first if you don't have a wallet ready yet.' This tells the agent when to use the tool, though it does not name alternative tools or explicitly state when not to use it.

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

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

The description adds meaningful behavioral details beyond annotations: it mentions baseline creation on first run, the cost ($0.05/call), and the USDC on base payment. This is useful context not present in the annotations (readOnlyHint=false, openWorldHint=true). No contradiction found.

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 the core purpose, followed by the baseline note and pricing. Every phrase earns its place; 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 tool with moderate complexity, the description adequately covers the change-detection flow, baseline behavior, and cost. While no output schema exists, the core behavior is explained clearly; edge cases like store removal are not covered but are not essential for basic 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 input schema's descriptions already cover all three parameters thoroughly (websites, max_items, baseline_key), so the tool description does not need to repeat them. The description adds no extra parameter-level semantics beyond what the schema 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 states a specific verb ('Watches') and resource ('Shopify store's catalogue'), and precisely defines the output ('only the products whose price or availability changed'). It clearly distinguishes from siblings like 'shopify-store-intelligence' or 'pricing_info' by focusing on change detection over time.

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

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

The description provides clear context for use (monitoring changes since the previous check), but does not explicitly name alternatives or state when not to use it. It implies a recurring-watch scenario without excluding other monitoring 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?

The description discloses important behavioral traits beyond annotations: uses public feeds (non-invasive), requires no authentication, costs $0.01/call (payment method), and mentions the 'rough revenue-band heuristic' indicating approximate results. This adds significant context to the readOnlyHint and destructiveHint 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 extremely concise, with two sentences that front-load the main action and outputs, followed by constraints (no login, cost). Every sentence adds value, and the structure 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?

For a tool with 2 params and no output schema, the description lists expected outputs and key constraints, making it reasonably complete. However, it does not explain behavior for non-Shopify sites or the response format, leaving minor 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 input schema already provides detailed descriptions for both parameters (websites list and maxConcurrency). The description does not add parameter-specific semantics beyond restating the overall purpose, so the baseline 3 is appropriate since schema coverage is 100%.

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 ('Confirm', 'pull') and clearly identifies the resource ('site runs on Shopify') and output types (product count, price range, top vendors/categories, newest listing, revenue-band heuristic). It distinguishes itself from sibling tools like tech-stack-detector by focusing on Shopify-specific intelligence.

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

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

The description provides clear context: it works on public feeds with no login/API key, implying low-friction usage for store intelligence. However, it does not explicitly mention when not to use it or name alternative tools, so it falls short of a full 5.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, and the description is consistent with these. It adds useful context beyond annotations: the tool does not use a browser, LLM, or API key, and it includes pricing ($0.02/call). It does not detail failure modes (e.g., missing sitemap, rate limits), but for a read-only tool the safety profile is already 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 three sentences and front-loaded with the core action ('Give it a domain'). Every sentence earns its place: pipeline explanation, constraints (no browser/LLM/API), and pricing. No fluff 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?

With no output schema, the description explains the return value (one dataset row per chunk, ready for vector store) and the processing pipeline. It covers cost and constraints. Missing edge-case details (e.g., what happens without a sitemap) are not critical given the simple read-only nature and good annotation coverage.

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% description coverage for both parameters (items and maxConcurrency), so the schema itself carries the parameter semantics. The description only reiterates 'Give it a domain' without adding new meaning about parameter formats or relationships. Baseline 3 is appropriate because the schema does the heavy lifting.

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 its function: 'reads the sitemap, fetches the pages, strips them to clean text and splits everything into ~1000-char chunks — one dataset row per chunk.' This is a specific verb+resource (sitemap) that produces a RAG-ready dataset, and it distinguishes itself from siblings like url-to-markdown by targeting whole-site crawling via sitemap.

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

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

The description provides clear context with 'Give it a domain' and emphasizes its lightweight nature ('No browser, no LLM, no API key'), implying it is ideal for building RAG datasets quickly and cheaply. However, it does not explicitly mention exclusions or alternatives (e.g., 'for a single page, use url-to-markdown'), so it lacks explicit when-not-to-use guidance.

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

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

Annotations already mark the tool as read-only and non-destructive. The description adds valuable behavioral context: extraction uses regex deterministically, makes zero LLM calls, requires no API keys, and incurs a fixed cost of $0.01/call via x402. This enriches the safety profile with cost and methodology details.

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 in the first clause, with supporting details on cost and method in subsequent clauses. It is slightly promotional but every piece of information (fields, regex, zero LLM, cost) is 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?

The description covers what fields are extracted, the extraction method, the cost model, and the target user. It does not elaborate on error handling or output structure beyond 'clean structured JSON', but given the tool's simplicity and the schema's parameter documentation, it is sufficiently complete for decision-making.

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 provides complete descriptions for all three parameters, including the valid field names and concurrency range. The description adds no parameter-specific semantics beyond the schema, 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 uses a specific verb 'Turn any URL into clean structured JSON' and enumerates exact extracted fields (title, description, image, JSON-LD, headings, links, emails, prices). It distinguishes from siblings by noting deterministic regex extraction with zero LLM calls, which differentiates it from similar URL-processing 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 clearly states the use case ('Built for AI agents that need one page turned into typed data, cheaply') and highlights cost benefits ($0.01/call, zero LLM calls). However, it does not explicitly name alternative tools or state when not to use it, so it stops short of a 5.

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

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

Annotations already include readOnlyHint=true and destructiveHint=false. The description adds valuable context beyond these: 'No browser, no proxies, no login' (direct HTTP), pricing of $0.01/call paid via x402 (USDC on base), and the diff semantics that rely on a user-supplied previous stack.

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 concise sentences deliver the core function, behavioral constraints, and cost. The description is front-loaded with the main purpose and every sentence provides distinct value with no 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?

With only 2 parameters, no nested objects, and a detailed schema, the description is sufficiently complete. It conceptually covers the return value ('exactly what was added or removed'), though with no output schema, a bit more detail on the response format could be beneficial, but it is not critical.

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 descriptions cover both parameters fully, including the pipe-separated syntax for items and the default/max for maxConcurrency. The tool description only restates the diff concept without adding parameter-level detail, so it stays 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 uses specific verbs and resources: 'Detect a website's current technologies' and 'diff it against a previously-seen stack you supply'. It enumerates tech categories (CMS, ecommerce, analytics, etc.) and clearly distinguishes from the sibling tech-stack-detector by the diff capability.

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 explains that diffing requires a previously-seen stack supplied by the user, and indicates that bare domains just detect the current stack. However, it does not explicitly name alternatives like tech-stack-detector for when no previous stack exists, so the guidance is clear but not exhaustive.

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?

Adds valuable behavioral context beyond annotations: 'No browser, no proxies, no login' clarifies operational mode, and '$0.01/call' discloses the cost model. Annotations already declare read-only, and the description is consistent with them.

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 efficiently cover the core function, operational traits, use case, and pricing. Each sentence adds distinct value, and the main purpose 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?

Lists the expected detection categories (CMS, ecommerce, analytics, etc.), giving a good sense of the output. However, no output schema exists, and the description does not specify the exact return format or whether results include confidence scores, leaving a minor 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 description coverage is 100%, with both 'websites' and 'maxConcurrency' fully described in the schema. The description adds no extra parameter semantics beyond what the schema already provides, so 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 describes the tool as detecting website technologies (CMS, ecommerce, analytics, etc.) from HTML and HTTP headers. However, it does not explicitly differentiate from the sibling 'tech-stack-change-detector', which likely provides change tracking over time.

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?

States 'Built for sales targeting and competitive research', giving a clear use context. No explicit exclusions or alternatives are mentioned, but the use case is specific enough to guide selection.

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

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

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description adds significant behavioral detail: it strips scripts/nav/ads, preserves headings/links/lists/emphasis, requires no browser/LLM/API key, and mentions cost. This fully sets expectations about what happens during conversion and what the output will look 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 short and front-loaded with the core function, followed by supporting details. The pricing/method detail ('$0.01/call, x402') is arguably extra but still useful context. A small redundancy exists ('cheaply' vs. pricing), so it is not perfectly 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?

Given no output schema, the description explains the return format conceptually (clean Markdown) but does not explicitly state how multiple URLs' results are returned (e.g., an array of markdown strings). It also omits failure/error behavior. However, the tool is simple, and the schema's 'One row per URL' partially covers this, so it is still fairly 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 input schema already provides 100% coverage with clear descriptions for all three parameters. The tool description adds overall context (what 'convert' means) but does not add new parameter-specific semantics. Since the schema carries the load, the 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?

The description uses a specific verb+resource: 'Fetch any URL and convert it into clean, LLM-ready Markdown', and enumerates the preserved/stripped elements. This clearly distinguishes it from sibling tools like sitemap-to-knowledge or structured-extract, which have different outputs and use cases.

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 it is 'Built for RAG pipelines and AI agents that need one page turned into readable text, cheaply', giving a clear target use case. It also implicitly contrasts with alternatives by noting 'No browser, no LLM calls, no API key', but does not explicitly name alternative tools or say when not to use it, so it's not a full 5.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds value by explaining the method ('via their own public JSON feed'), explicitly stating no login/browser/proxies, and disclosing the cost per call ($0.02/call). This goes beyond the safety profile provided by 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?

A single, information-dense sentence followed by a brief cost/access note. Every element serves a purpose: what it does, data fields, platform context, auth requirements, and pricing. 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?

Despite no output schema, the description lists the return fields (name, price, sale price, category, image), enough for an agent to interpret results. It explains the data source (public JSON feed) and operational constraints (max 20 shops, concurrency). With strong annotations and full schema coverage, the description is complete for effective selection and 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?

Schema description coverage is 100%, with both items and maxConcurrency parameters well documented in the schema. The description does not add parameter details beyond the schema, so it meets the baseline for high schema coverage. The schema itself is informative about subdomain formats and concurrency limits.

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 ('Pull live product catalogs') with a clear resource ('Zid storefronts'), explicitly listing the data fields (name, price, sale price, category, image). It clearly distinguishes this tool from siblings by targeting Zid specifically, whereas sibling tools focus on Shopify or generic e-commerce.

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

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

The description provides clear context for when to use this tool: when needing product data from Zid storefronts, noting it's a common Saudi/Gulf e-commerce SaaS. It also implies no authentication is needed ('No login, no browser, no proxies'). However, it does not explicitly mention alternatives or exclusions, such as 'use Shopify tools for Shopify stores'.

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