Web Intel

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

The description lists what the tool checks (DNS, SSL, SPF/DMARC) and the types of issues found (email unreachable, spoofable, expiring certs). However, it does not disclose side effects, authentication needs, or rate limits, and annotations are absent, leaving the burden partially unmet.

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 plus a pricing note. It is front-loaded with the core verb and resource, and the second sentence adds value. The pricing line is not essential but does not detract significantly.

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 two simple parameters and no output schema, the description provides sufficient context about what the tool returns (identified issues). It is mostly complete given 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 description coverage is 100% for both parameters. The description adds no additional meaning beyond the schema, 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?

The description clearly states the tool's purpose: bulk-audit domains for DNS, SSL expiry, SPF/DMARC. It distinguishes from sibling tools by focusing specifically on domain health and email authentication issues.

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

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

The phrase 'before your clients do' implies proactive monitoring, providing some usage context. However, no explicit alternatives or when-not-to-use guidance is given, slightly limiting clarity.

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?

Discloses that the tool is free and lists the output contents. Without annotations, it effectively communicates that this is a read-only operation. No behavioral traits are hidden, though it could mention if any authentication is required.

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 plus a usage hint, no wasted words. Highly concise and front-loaded with essential information.

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

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

Despite no output schema, the description explains what the output contains (list of tools with price, address, network) and provides usage context. Complete for a param-free tool.

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

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

With zero parameters and 100% schema coverage, the description correctly does not need to add parameter details. Baseline of 4 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 it lists all paid tools in the 'web-intel' bundle with price, payTo address, and network. It uses specific verbs and resources, and the tool's purpose is distinct from sibling tools which focus on other tasks.

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

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

Explicitly advises to call this first if the user does not have a wallet ready, providing clear context. However, it does not specify when not to use it or mention alternative 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 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?

No annotations provided, so description must fully disclose behavior. It notes public feeds and no authentication, but omits details like error handling (if site is not Shopify), rate limits, data freshness, or accuracy of revenue heuristic. Pricing is business, not behavioral.

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 efficient sentence with key actions and outputs front-loaded. Pricing appended via dash, no redundant words. Could be slightly more structured but overall 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?

No output schema or annotations; description lists data points but not their format or structure. Missing error scenarios and return type. For a tool with two parameters and no output schema, description should provide more completeness.

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

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

Schema coverage is 100%, so baseline 3 applies. Description does not add meaning beyond schema for the two parameters; the purpose description indirectly helps but does not elaborate on parameter syntax or constraints.

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

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

Description clearly states it confirms Shopify sites and pulls specific store intelligence (product count, price range, etc.), using specific verbs and listing deliverables. Distinguishes from sibling tools like domain-health-checker or tech-stack-detector by focusing on Shopify-specific data.

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?

Implies usage for Shopify intelligence, mentions no login/API key required, but does not explicitly state when to use versus alternatives or when not to use it. Lacks exclusions or comparative guidance.

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

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

No annotations provided, so description carries full burden. It explains key behaviors: reads sitemap, fetches, strips, splits into chunks, no browser/LLM/API key, and pricing. Could mention error handling or missing sitemap behavior, but overall transparent.

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

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

Two concise sentences, front-loaded with action, no wasted words. Every sentence adds value: domain input, process steps, output format, and pricing.

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 output (one row per chunk, ready for embedding) and chunk size. It covers purpose, process, and pricing. Minor gaps like limits (max 10 items) are in schema. Overall fairly complete for a simple tool.

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

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

Schema coverage is 100% with clear parameter descriptions. The tool description adds context but does not significantly enhance parameter understanding beyond the schema. 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 clearly states the tool's purpose: taking a domain, reading its sitemap, fetching pages, stripping to clean text, and splitting into ~1000-char chunks for embedding. It uses specific verbs and resources, distinguishing it from sibling tools like url-to-markdown or structured-extract.

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 implicitly indicates when to use (for converting website content into chunks) and notes no browser, LLM, or API key needed. It provides pricing but lacks explicit when-not-to-use or direct comparisons to sibling tools.

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

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

No annotations are provided, so the description bears full responsibility. It discloses the extraction method (regex), cost ($0.01/call), and payment method (x402 USDC on base). However, it omits behavioral details such as error handling, URL accessibility requirements, rate limits, and whether the tool can handle non-public or dynamic pages.

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, starting with the primary function and listing extracted fields. It includes relevant cost and implementation details without unnecessary fluff. Minor points: the pricing and tech details could be separate, but overall it's efficient.

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

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

For a tool with 3 well-documented parameters and no output schema, the description covers the main purpose and extraction fields. However, it lacks detail on the JSON output structure, error behavior, and limitations (e.g., URL types not supported). Slightly below complete for a tool with no annotations or output schema.

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

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

Schema coverage is 100%, and the schema descriptions already adequately explain each parameter (URLs, fields, max concurrency). The description adds no additional meaning beyond stating the extracted fields in the main description, which matches the schema's 'fields' parameter. Baseline of 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 clearly states the verb ('turn any URL into clean structured JSON') and the specific resource (URLs), listing the exact fields extracted (title, description, image, etc.). This differentiates it from siblings like 'url-to-markdown' (markdown output) and 'tech-stack-detector' (technology 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 explicitly states the tool is 'built for AI agents that need one page turned into typed data, cheaply' and highlights zero LLM calls and zero API keys. It implies appropriate use cases (cheap, structured extraction) but does not explicitly contrast with sibling tools or 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?

Despite no annotations, the description discloses key behavioral traits: no browser, no proxies, no login, and a cost of $0.01/call. This conveys safety and non-destructiveness. It does not cover rate limits or auth, but the disclosed traits are adequate.

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 paragraph that front-loads the purpose and key selling points. It is efficient but could be slightly more structured (e.g., bullet points for parameters). No wasted sentences.

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 (detect and diff), the description adequately covers what it does and the input format. It does not describe the output format, but since there is no output schema, it is acceptable. The mention of 'additions/removed' gives enough context.

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

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

Schema coverage is 100%, but the description adds significant value by explaining the diff syntax (pipe-separated previous stack) and providing a prefill example. It clarifies the maxItems constraint and the meaning of the 'maxConcurrency' 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 detects current technologies and diffs against a previously-seen stack. It lists specific technology categories and explicitly distinguishes from siblings by emphasizing the change detection feature, which the sibling 'tech-stack-detector' likely lacks.

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 explicit guidance on when to use the tool (to get additions/removals) and includes examples of input formats (bare vs. diff form). However, it does not explicitly mention when not to use it or compare directly with sibling tools.

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

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

No annotations are provided, so the description must disclose behavior. It mentions 'No browser, no proxies, no login', indicating a lightweight HTTP call. However, it doesn't discuss failure modes, return format, or rate limits beyond the maxConcurrency parameter. The pricing info ($0.01/call) adds cost 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 a single paragraph that front-loads the purpose and method. The pricing info, while not critical for tool selection, does not detract. It could be slightly more concise by removing the pricing details, but overall it's 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?

No output schema is provided, so the description should hint at the output structure. It strongly implies a list of detected technologies per site but doesn't explicitly describe the return format. Given the specificity of the task, the description is adequate but not complete.

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

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

Schema coverage is 100% for both parameters ('websites' and 'maxConcurrency'), so the schema already describes them well. The description adds no additional parameter-specific information beyond the schema. 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 clearly states 'Detect the technologies a website runs' and lists specific categories (CMS, ecommerce, analytics, etc.), with the method (HTML/HTTP headers). It distinguishes itself from siblings like 'domain-health-checker' and 'tech-stack-change-detector' by focusing on tech fingerprinting for sales and competitive research.

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?

Description states 'Built for sales targeting and competitive research', providing clear context. It doesn't explicitly mention when not to use or alternatives, but the purpose is specific enough that an agent can infer appropriate usage. Missing explicit exclusions.

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

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

With no annotations, the description carries full burden. It notes scripts/nav/ads are stripped and no browser/LLM calls are needed, but omits details on rate limits, error handling, or output format specifics.

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 paragraph that efficiently conveys core functionality, though it includes extraneous pricing info. It is front-loaded with key action verbs.

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 explains the tool's purpose and basic behavior but lacks detail on return format and error handling. Without an output schema, this is a notable 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% (each parameter described). The description adds overall context but does not enhance parameter semantics 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 fetches a URL and converts it to LLM-ready Markdown. It distinguishes from sibling tools like domain-health-checker or tech-stack-detector by focusing on URL-to-markdown conversion.

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 it's built for RAG pipelines and AI agents, implying suitable use cases. However, it does not explicitly state when not to use or provide alternative 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 the data source (public JSON feed), authentication requirement (none), and cost. However, with no annotations, it should provide more behavioral details like rate limits, error handling, or edge cases (e.g., unreachable stores). It partially meets the burden but has 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 succinct (three sentences) and front-loaded with the primary function. The first sentence clearly states the tool's action and output; the second adds context; the third mentions cost. The cost detail is slightly tangential 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?

The description lists the data fields returned but does not describe the response structure (e.g., JSON format, arrays, errors). Since there is no output schema, the description should compensate more fully. The tool is relatively simple (public feed), but completeness is adequate with 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 has 100% coverage with clear descriptions for both parameters ('items' and 'maxConcurrency'). The tool description adds context about Zid being a Saudi/Gulf e-commerce SaaS but does not enhance parameter understanding beyond what the schema already provides. 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 clearly states the tool's purpose: pulling live product catalogs from Zid storefronts, specifying the data fields (name, price, sale price, category, image) and the source (public JSON feed). It distinguishes from siblings like 'shopify-store-intelligence' (different platform) and 'structured-extract' (generic 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?

The description implies ease of use ('No login, no browser, no proxies') and mentions cost, but lacks explicit guidance on when to use this tool versus alternatives. Sibling tools exist (e.g., shopify-store-intelligence, structured-extract) but no direct comparison or exclusion criteria are provided.

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