WebLens

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

Without annotations, the description carries the burden of disclosing behavior. It states the tool searches the web, fetches sources, and 'answers strictly from them,' which indicates a read-only, source-grounded behavior. It also discloses the cost ('Price: $0.05'), a key side effect. It does not mention rate limits or edge cases, but for a simple question-answering tool, this is 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 two sentences and every part earns its place: the main purpose, the process, and the price. It is front-loaded with the most important information ('Get a grounded answer with inline [n] citations') and contains 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?

Given the tool's simplicity (2 parameters, both documented) and lack of output schema, the description sufficiently conveys what the return value is: an answer with inline citations. It also explains the underlying process. However, it could be slightly more explicit about the exact output structure (e.g., answer text and citation list), so a 4 is appropriate.

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 documentation already covers 100% of the parameter meanings: query is 'The question to answer' and sources includes range and default. The description adds no additional parameter-specific details 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 clearly states the tool's action: 'Get a grounded answer with inline [n] citations.' It specifies the resource (web sources) and the process (searches, fetches, answers strictly from them). This distinguishes it from sibling search tools like search_web (which merely returns results) and deep_research (which implies broader 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?

The description provides clear context for when to use the tool: when a grounded, cited answer is needed. It implies the tool synthesizes information from multiple sources rather than just fetching or searching. However, it does not explicitly name alternatives or state when not to use it, 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?

With no annotations provided, the description carries the full burden for behavioral disclosure. It discloses parallel execution and per-URL pricing, which are useful traits, but it does not mention failure handling, response format, rate limits, or authorization needs. This leaves notable gaps for an unannotated 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 consists of three short, purposeful sentences. It starts with the core action, then the use case, then the cost. Every sentence earns its place and there is 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?

The tool is relatively simple with one parameter and no output schema, but the description does not explain return values or edge-case behavior. Given the absence of annotations and output schema, the description is adequate but not complete; an agent still lacks information about what is returned or how errors are surfaced.

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 'urls' parameter, including the 2-20 count constraint. The description adds context about parallel and bulk usage but does not add syntactic or format details 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?

Description clearly states the tool fetches multiple URLs in parallel, with a specific verb and resource. It distinguishes itself from single-URL siblings by emphasizing 'multiple' and 'bulk operations.'

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 'Efficient for bulk operations' provides clear context for when to use this tool, implying it is preferred for high-volume fetching. However, it does not explicitly name alternatives or state when not to use it, such as for single URLs or when resilience is needed.

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

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

With no annotations, the description must carry the full burden of behavioral disclosure. It mentions the cost ($0.05) and that it is AI-generated, but does not disclose how the tool handles invalid URLs, whether it fetches content directly, or any rate limits or side effects. It is not misleading but leaves significant behavioral context unstated.

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, comprising only two short sentences that front-load the core purpose and add the price as an important operational detail. There is no filler or redundant 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?

The tool has no output schema and no annotations, but the description only provides a high-level statement of output ('analysis of differences'). It does not clarify the output format, any constraints on webpages, or the role of the 'focus' parameter beyond the schema. For a simple tool with well-described parameters, this is acceptable but leaves some gaps in behavioral 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?

The input schema already provides descriptions for both parameters (urls and focus). The description adds no extra meaning beyond restating '2-3 webpages' for the urls parameter, so it does not improve on the schema's coverage.

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

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

The description uses a specific verb ('compare') and resource ('webpages'), and clearly states the AI-generated analysis output. It distinguishes itself from sibling tools like fetch_webpage or extract_data by focusing on comparison of 2-3 webpages and the intended difference analysis.

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

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

The description implies the tool should be used when comparing 2-3 webpages and wanting an analysis of differences. However, it provides no explicit guidance on when not to use it or how it relates to alternatives like smart_extract or fetch_webpage, so the usage context is only implied.

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 synchronous behavior (no polling), same-host BFS, depth/budget limits, robots.txt honoring, and a unique billing model where the requested budget is charged regardless of returned pages. This goes well beyond a simple 'crawl a site' statement, though it does not cover error handling or authentication needs.

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 front-loaded with the primary action. It conveys essential behavioral and pricing information in two sentences without redundancy. Every clause adds value.

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

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

For a tool with 8 parameters and no output schema, the description adequately covers core behavior, limits, billing, and robots.txt. It does not describe the output structure beyond 'clean markdown', but the overview is sufficient for an agent to select and invoke the tool correctly in most scenarios.

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 already has 100% parameter coverage, but the description adds meaningful interpretation: 'page-budget limits' connects to limit and billing, 'depth' relates to maxDepth, and 'robots.txt honoured by default' clarifies respectRobots. It also explains the cost implication of limit, which is not evident from the schema alone.

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 crawls a site and returns clean markdown for every page, with specific details like synchronous execution, same-host BFS, depth and page-budget limits, and robots.txt handling. This distinguishes it from sibling tools like fetch_webpage (single page) or map_site (URL discovery only).

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 on when to use the tool (for crawling an entire site in one synchronous call) and mentions constraints like page budget and robots.txt. It does not explicitly name alternative tools or state when not to use it, but the scope (site-wide crawl) implicitly differentiates it from single-page fetchers.

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 performance (30-60 seconds), cost tiers ($0.20/$0.35), the internal steps (sub-questions, dedupe), and a clear caution about timeout behavior. This is rich behavioral context that substantially helps an agent anticipate 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 three sentences, front-loaded with the core purpose, and every sentence earns its place: the first explains what it does, the second differentiates from `research`, and the third provides essential operational and pricing details. It is information-dense without being wordy.

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 complex, but the description covers the full workflow, the return value's shape (inline citations, key findings, gaps), the time and cost behavior, and the differentiation from siblings. No output schema exists, yet the description adequately explains what the agent will receive.

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%, so the baseline is 3. The description does not add parameter-level meaning beyond what the schema already provides; the depth enum details and default are fully captured in the schema. Thus no extra value is added here.

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 phrase 'Multi-step cited research' and details the process: plans sub-questions, searches each, fetches/dedupes sources, and synthesizes an answer with citations, key findings, and gaps. It explicitly contrasts with the sibling tool `research`, making its distinct purpose unmistakable.

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 directly names the alternative `research` and explains when that tool is appropriate (one search + summary) versus `deep_research` (multi-step decomposition, citations per claim). It also gives explicit operational guidance: SLOW, use generous timeout, do not retry on timeout. This effectively covers when to use and when not to use.

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

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

With no annotations, the description carries the burden. It discloses that the tool makes a single fetch, reports multiple technology types, and provides evidence for each detection. It also discloses pricing. This is meaningful behavioral context beyond the tool name and schema.

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, information-dense sentence listing the detected technologies and evidence, followed by use cases and price. Every element earns its place; 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?

For a simple one-parameter tool with no output schema, the description covers what it does, what it detects, how results are evidenced, when to use it, and pricing. This is complete enough for an agent to invoke it correctly.

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

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

The input schema already describes the only parameter ('url' as 'Site URL to fingerprint') with 100% coverage. The description adds context about what the URL is used for and the output categories, but does not add parameter-level details 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?

The description clearly identifies the tool's purpose: detecting the technology stack of a website (framework, CMS, CDN, etc.) from a single fetch. It differentiates from sibling tools by listing specific categories and noting the evidence (response header/HTML marker) that proves each 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?

Explicit use cases are provided: competitive research, sales prospecting, and vendor due diligence. It does not explicitly mention when not to use it or name alternatives, but the use-case framing gives clear context for 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?

With no annotations, the description carries the burden of explaining behavior. It details what data is returned (whois, DNS, providers, SPF/DMARC, risk flags) and even includes pricing. However, it does not disclose data freshness, rate limits, or whether any active scanning occurs, leaving some 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 a single, information-dense sentence followed by use cases and price. It is well-organized and front-loaded with the core purpose. The enumerated data types are arguably more detailed than necessary, but each adds value, making it appropriately concise without being terse.

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 no annotations, the description compensates well by listing the categories of returned information and the intended use cases. However, it does not describe the output structure, error behavior, or data source provenance, so it is not fully complete for an agent needing precise invocation expectations.

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 is fully covered (100%) by the parameter description, which already explains the domain field and URL-to-hostname reduction. The tool description adds no new parameter-level semantics beyond reinforcing that the domain is the input, so it earns the baseline score of 3.

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 aggregates comprehensive domain intelligence in one call, listing specific data types (registration, expiry, DNS, providers, SaaS vendors, SPF/DMARC, risk flags). It distinguishes itself from sibling tools like intel_company (company-focused) and intel_site_audit (site-focused) by focusing on domain-level 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?

The description provides explicit use cases: 'Use for vendor due diligence, security triage, phishing checks and prospect research.' This gives clear context for when to use it, though it does not mention alternative tools or when-not-to-use conditions, 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?

With no annotations, the description must disclose behavior. It only mentions 'AI-powered extraction' and a price, which adds minimal context. No details about rate limits, error handling, or limitations are given. This is insufficient for a web extraction 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 three short sentences with no waste. It front-loads the main purpose and includes value-added details (AI-powered, price).

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 complexity of web extraction and the absence of annotations or output schema, the description is too sparse. It does not explain what the return data looks like, how to handle dynamic pages, or when this tool is preferable to similar siblings. The price mention is useful but not enough.

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 fully describes all parameters with 100% coverage, so the baseline is 3. The description adds no extra meaning beyond implying the JSON schema is central to the extraction process.

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 a webpage using a JSON schema, which is a specific verb+resource. It does not explicitly distinguish itself from siblings like smart_extract or fetch_webpage, but the JSON schema focus implies a unique 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?

No guidance is provided on when to use this tool versus alternatives such as smart_extract or fetch_webpage. There is no mention of preferred use cases, exclusions, or prerequisites.

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 responsibility for behavioral disclosure. It states the tool extracts text and metadata and provides pricing, but does not explain limitations (e.g., file size, password-protected PDFs) or return format. It is not misleading but is sparse on behavior 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 a single, concise sentence that clearly communicates the core function and pricing. No wasted words or irrelevant 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?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is somewhat complete but fails to describe the output structure or any edge-case behavior. It provides the essentials but lacks depth users might need for robust selection.

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 only a URL parameter described accurately. The description adds no additional meaning beyond the schema, merely reinforcing the extraction purpose. No extra parameter details or usage constraints are provided.

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 text and metadata from a PDF document, using a specific verb ('Extract') with a defined resource ('PDF document'). It distinguishes itself from sibling tools, as no other sibling explicitly targets PDF 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 provides no guidance on when to use this tool versus alternatives like extract_data, smart_extract, or fetch_webpage. It lacks any mention of prerequisites, exclusions, or preferred use cases.

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 of behavioral disclosure. It reveals the automatic fallback sequence (native scraper then headless Chromium) and also discloses the price. It does not describe the return format or error handling, but the core behavior is clearly explained.

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: one sentence covering the purpose and fallback behavior, and a second sentence for pricing. Every piece of information earns its place, with the core functionality 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?

For a tool with only two simple parameters fully described in the schema, the description covers the key behavioral differentiator (fallback) and the use case. The lack of an output schema means the return format is not specified, but this is a minor gap given the simplicity of 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?

The input schema already fully describes both parameters (url and timeout, including default, min, and max). The description adds no additional parameter-specific meaning, so the baseline of 3 applies for high schema coverage.

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

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

The description clearly states what the tool does: it fetches web pages resiliently, using a native scraper first and headless Chromium for JS-rendered or bot-walled pages. This goes beyond a simple verb+noun, specifying the fallback strategy and distinguishing it from sibling tools like fetch_webpage or batch_fetch.

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 gives clear context for when to use this tool: for JS-rendered or bot-walled pages that may require a headless browser. It implies that for simple static pages, a regular fetch might suffice, though it does not explicitly name alternatives. The conditional 'for JS-rendered or bot-walled pages' serves as a usage trigger.

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

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

With no annotations, the description carries the full transparency burden. It does disclose a key behavioral trait: 'no JavaScript rendering', which sets expectations for dynamic pages. However, it does not mention caching behavior, error handling, or rate limits, which are relevant for a fetch tool. The cache parameter exists in the schema but is not mentioned in the description.

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, front-loading the core purpose in the first sentence. The second sentence adds a key limitation ('no JavaScript rendering') and cost information. Every clause earns its place 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?

For a simple fetch tool, the description is adequate but not complete. It explains what the tool does and a key limitation, and the schema covers parameters. However, given the large number of sibling tools (batch_fetch, fetch_resilient, fetch_webpage_pro), the description lacks enough contextual differentiation to help an agent choose correctly. No output schema exists, but the description's mention of 'clean markdown' partially covers return format.

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%, so the input schema already documents all four parameters clearly. The description adds no parameter-specific semantics beyond what the schema provides. The cost note ('Price: $0.002') is not parameter-related. Baseline 3 is appropriate since the schema handles parameter meaning.

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 action ('Fetch') and the resource ('a webpage') and the output format ('clean markdown'). It is specific about the conversion, but it does not explicitly distinguish itself from sibling tools like fetch_webpage_pro or batch_fetch, which is a minor gap.

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 no explicit guidance on when to use this tool versus alternatives. Phrases like 'Fast, no JavaScript rendering' imply suitability for simple static pages, but there is no stated when-not-to-use or comparison with sibling tools such as fetch_resilient or smart_extract.

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

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

With no annotations, the description carries the full burden. It discloses the price ($0.006) and full JS rendering, which are useful behavior traits. However, it does not explain side effects, return format, or whether execution is read-only—a gap for a rendering 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?

Extremely concise: two short sentences and a price tag. Every word adds value, and the core purpose and usage are 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?

No output schema exists, so the description should clarify what is returned, but it does not. While the purpose and cost are clear, the lack of return-format or error-behavior context leaves a notable gap for a dynamic-rendering fetch 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%, so the baseline is 3. The description adds no extra parameter-specific meaning, but the schema already documents all three parameters adequately.

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?

States clearly the action ('Fetch webpage') and a distinguishing feature ('full JavaScript rendering'), explicitly targeting SPAs and dynamic content. This sets it apart from sibling tools like fetch_webpage and fetch_resilient.

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 direct guidance to use for SPAs and dynamic content, giving a clear context of applicability. It does not explicitly mention alternatives or exclusions, but the instruction implies when this tool is appropriate.

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

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

With no annotations, the description carries the full burden. It discloses output format (clean markdown), truncation behavior (character cap), and cost (price per URL), which are meaningful behavioral traits. However, it omits error handling, redirect behavior, and what 'clean' means in edge cases, so it is not exhaustive.

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

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

The description is a single, front-loaded sentence that states the action, constraints, output, and pricing. Every word earns its place 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?

For a simple read-only fetch tool, the description covers the core semantics: batch size, output format, and truncation. It lacks details on failures or partial results, but given the simplicity and presence of a full schema, it is sufficiently complete for an agent to invoke correctly in most cases.

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 the baseline is 3. The description mentions '1-20 URLs' and 'character cap,' but these are already described in the schema (urls and maxChars). It adds no new parameter-level meaning beyond restating schema 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?

The description clearly states the tool fetches 1-20 URLs and returns clean markdown per page, truncated to a character cap. This specific verb+resource+output combination distinguishes it from siblings like fetch_webpage (likely single URL) and batch_fetch (may not produce markdown).

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 batch-fetching multiple URLs into markdown but does not explicitly contrast it with alternatives like fetch_webpage or batch_fetch. No 'use this when' or 'instead of' guidance is provided, leaving the decision to inference.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It only mentions the price ($1.00) and the vague term 'deep dive,' without stating whether the operation is read-only, what the output format is, or any side effects. This is minimal guidance.

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

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

The description is a single, front-loaded sentence listing the key content areas plus the cost. Every piece of information is useful and there is no redundancy or 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?

The description is adequate for a simple single-parameter tool, but it lacks any indication of the return format or result structure. It also does not differentiate from sibling tools or note any constraints. Since there is no output schema, more context would be expected.

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

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

The schema fully describes the sole parameter 'target' as 'Company name or domain to research,' providing 100% coverage. The description adds no additional parameter context, 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 clearly states it is a company intelligence deep dive covering tech stack, funding, team, competitors, and news. This distinguishes it from sibling tools like intel_competitive and intel_market by explicitly listing company-specific dimensions. The purpose is specific and action-oriented.

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 use for in-depth company research but provides no explicit guidance on when to choose this tool over alternatives like intel_competitive, intel_market, or detect_tech. It does not mention any exclusions or complementary 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?

With no annotations, the description carries the full burden. It does disclose the $8.00 price and the output components (feature matrix, pricing, SWOT), which is useful. However, it does not mention whether the tool is read-only, how results are returned, or if there are any rate limits or additional costs, leaving some behavioral aspects opaque.

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, efficient sentence that leads with the core purpose, lists the output types, and ends with the price. Every word adds value and there is no redundant 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?

The description provides the output deliverables and the price, and the schema covers all parameters, giving an agent enough to correctly invoke the tool. It lacks explicit guidance on when to choose this over sibling tools and deeper detail about output formatting or billing, but these are not critical for 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%, so the schema already fully documents the parameters (company, focus, maxCompetitors). The description adds no extra meaning to these parameters, thus it matches the baseline for high schema coverage.

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

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

The description clearly states the tool performs competitive analysis and lists the key outputs: feature matrix, pricing, and SWOT analysis. This distinguishes it from sibling tools like intel_company (company intelligence) and intel_market (market 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?

No guidance is provided on when to use this tool versus alternatives such as deep_research or intel_market. The description simply labels the tool without offering context or 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?

No annotations are provided, so the description carries full responsibility. It discloses the price ($5.00) and that it is AI-powered, but does not reveal whether the operation is read-only, any side effects, rate limits, or response structure. For a paid tool, cost is useful, but other behavioral aspects are absent.

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 one sentence plus a price, which is highly concise. Every word contributes meaning: 'AI-powered,' 'market research report,' 'trends, key players, and data points,' and the price. No filler or redundancy is present.

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

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

Given the tool has no output schema and is a paid research report generator, the description does not adequately explain what the report structure looks like or how the 'depth' parameter changes the output. It also lacks usage prerequisites or instructions. While it lists content elements, the overall context is incomplete for an agent to fully understand the tool's behavior.

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 each parameter having a description (e.g., 'Research depth (default standard)'), so the baseline is 3. The tool description does not add extra meaning to the parameters, such as how 'depth' affects the report, but it does not need to since the schema already covers them.

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 produces an 'AI-powered market research report with trends, key players, and data points,' which identifies the core function. It distinguishes from siblings like intel_company and deep_research by focusing on market topics, though the verb is implicit rather than explicit.

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 market research via the tool name and mention of 'market research report,' but provides no explicit guidance on when to choose this over alternative research tools. No exclusions or alternatives are mentioned, leaving the agent to infer context from the name alone.

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

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

With no annotations, the description must fully disclose behavioral traits. It mentions 'scoring' and 'comprehensive' but does not state whether the audit is read-only, what the output format is, or any limitations or side effects. The price is a commercial detail, not a behavioral trait.

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

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

The description is a single, front-loaded sentence that is easy to parse. However, its brevity omits important details and includes price information that, while useful, does not contribute to tool selection. It is concise but not comprehensive.

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 only one parameter and no output schema, so the description must explain what the audit returns and how it behaves. It only indicates 'scoring', which is vague. For a paid tool, it also lacks details on delivery, turn-around, or any constraints. This is insufficient for an agent to fully understand 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?

The schema provides full coverage for the single 'url' parameter with 'URL to audit'. The description adds no extra meaning to the parameter, so it does not improve on the schema. 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 tool performs a 'comprehensive SEO, performance, and security audit with scoring', using a specific verb and scope. However, it does not explicitly distinguish this from sibling tools like crawl_site or detect_tech, so it is clear but lacks differentiation.

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 is provided on when to use this tool versus alternatives. The only additional context is the price, which does not help an agent decide between this and similar audit or research 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?

With no annotations, the description carries the full burden. It discloses the sitemap-reading strategy, fallback to homepage link extraction, and cost. This gives meaningful behavioral insight beyond the name, though it doesn't cover error handling or return format.

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

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

The description is a single, front-loaded sentence that states purpose and method, plus the price. Every word adds 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?

No output schema exists, so the description should explain the return value. It says 'Discover a site's URLs' but does not specify the output format or error behavior. Moderate complexity with 5 parameters and a fallback strategy warrants a bit more detail.

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 is 3. The description adds no parameter-specific context; it does not elaborate on url, limit, exclude, include, or timeout. The schema descriptions already cover parameter meaning fully.

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 discovers a site's URLs without fetching page content, with specific method details (robots.txt, sitemap.xml, nested indexes, fallback). This distinguishes it from content-fetching siblings like fetch_webpage and crawl_site.

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 a clear use case: when you need a site's URL inventory without content. It notes 'without fetching page content' but does not explicitly mention alternatives or exclusions. Context is sufficient for an agent to decide when to use it.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does mention persistence and price, which are helpful, but it does not disclose potential overwrite behavior, session scoping, or any side effects beyond storage. The schema covers TTL and key length, but the description adds only persistence and cost context, which is useful but not comprehensive for an unannotated 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 a single succinct sentence that immediately conveys the tool's purpose, with an added price note. There is no filler, and the most important information is front-loaded. It earns a perfect score for 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?

For a simple storage tool with a fully described schema, the description is nearly complete. It covers the core operation and persistence behavior, though it could be enhanced with a note on usage scenarios (e.g., when to set a TTL) or return value expectations. However, given the tool's simplicity and the schema's richness, it is 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?

The schema covers 100% of parameters with descriptions ('Storage key (max 256 chars)', 'Time to live in hours', 'Value to store (any JSON)'), so the baseline is 3. The tool description does not add any parameter-specific semantics beyond what the schema already provides, so the baseline score 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 function: 'Store key-value data in persistent agent memory.' This is a specific verb+resource combination that unambiguously distinguishes it from the sibling tools, which are all research, fetch, or search operations. The purpose is immediately obvious.

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 by mentioning 'persistent agent memory,' implying this tool should be used when data needs to be retained across operations or sessions. While there are no explicit exclusions or mention of alternatives, the context is evident for a memory storage tool given the sibling set contains no similar 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 is the primary source of behavioral info. It mentions the price ($0.01) which is useful, but it does not disclose what happens upon creation (e.g., returned monitor ID), monitoring frequency, or any side effects. This is a significant gap for a paid, state-changing 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 a single sentence that includes all essential information (action, resource, notification method, price) without unnecessary words. It is well-structured and 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?

The tool is simple with 2 parameters and no output schema, but the description does not mention the return value or any setup requirements. While not overly complex, the lack of behavioral details (like what a monitor ID is) leaves gaps. The price is mentioned, but overall completeness is moderate.

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 fully describes both parameters ('URL to monitor for changes' and 'Webhook URL for change notifications') with 100% coverage. The description adds no additional parameter semantics beyond the schema, so a 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 'Create a URL change detection monitor with webhook notifications', specifying the action (create) and resource (monitor). It also adds the price, which is unique to this tool and distinguishes it from siblings.

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 when to use it (when you want to monitor a URL for changes and receive webhook notifications), but it does not explicitly mention situations where it should not be used or alternatives. However, given the tool's unique purpose among siblings, the 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?

No annotations are provided, so the description bears the full burden. It discloses a wide range of output details, including health signals and the monetary price ($0.003), which is a valuable behavioral trait. While it doesn't explicitly state 'read-only,' the phrasing 'One call returns' strongly implies a non-mutating lookup.

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

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

The description is two sentences: a purpose-setting question and a comprehensive but efficient list of returned data. Each clause adds value, and the price is appended succinctly. There is no unnecessary verbosity.

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 effectively enumerates the return data (version, license, downloads, scores, health signals), covering the tool's core value. It omits response structure and error behavior, but the list is detailed enough for an agent to understand what the tool returns.

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?

Both parameters (name and registry) are already described in the input schema, providing 100% coverage and a baseline of 3. The description's statement 'Supports npm and PyPI' simply echoes the schema's enum without adding new semantic 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 opens with a purpose-driven question ('Should you depend on this package?') and specifies concrete outputs (version, license, health signals), clearly defining it as a package evaluation tool. It explicitly supports npm and PyPI, distinguishing it from sibling intel tools like domain_intel or intel_company.

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 question 'Should you depend on this package?' establishes a clear decision-making context, and the mention of npm and PyPI indicates applicable registries. It lacks explicit alternatives or exclusions compared to sibling tools, so it doesn't reach 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It is highly transparent: it states the tool is free, what it returns (price, summary, sample), that some endpoints run a real truncated live preview when a URL is provided, and that others only return a recorded sample due to upstream credit costs. This fully informs the agent about what to expect.

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 relatively concise for the amount of detailed behavior it conveys. It is front-loaded with 'FREE' and the main purpose in the first sentence, then explains conditional behavior. The final 'Price: free' is redundant with the opening but not overly harmful. Overall, every sentence earns its place without unnecessary 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?

Given the tool's moderate complexity (conditional behavior based on endpoint type) and lack of output schema, the description is complete enough. It explains what is returned (price, summary, sample) and under what conditions a live preview occurs versus a recorded sample. An agent has sufficient information to select and invoke the tool correctly without ambiguity.

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 has 100% coverage with detailed descriptions for both parameters. The description adds some context about when the 'url' parameter is applicable and that the preview may be truncated, but this is largely redundant with the schema descriptions. It doesn't add significant new parameter-level meaning 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 clearly states the tool's purpose: to preview a paid WebLens endpoint before paying, showing the live price, a one-line summary, and a recorded sample of the response shape. It distinguishes itself from direct execution tools by emphasizing the 'before paying' aspect, making it clear this is a preflight/preview tool.

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 guidance on when to use the tool: to see what a paid endpoint costs and returns. It also specifies conditional usage based on endpoint type — live preview for fetch-backed endpoints when a URL is passed, recorded sample only for SerpAPI/Anthropic-backed endpoints — with the reason (avoid burning upstream credits). It doesn't explicitly name alternatives but the contrast with actually calling the endpoint 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 the full burden of behavioral disclosure. It does disclose the main workflow (search, fetch, summarize) and price, which is useful. However, it does not mention output format, whether sources/citations are returned, potential limitations, or failure behavior, leaving significant gaps.

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

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

The description is a single, well-structured sentence that delivers the core functionality and pricing with no filler. It is appropriately front-loaded and every word earns its place.

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 multi-step tool with no output schema and no annotations, the description provides a high-level overview but lacks details about the return value structure, citation behavior, or any caveats. It is adequate for basic selection but not fully complete for agents expecting precise output expectations.

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 documents both parameters (query and resultCount) clearly with 100% coverage, so the description does not need to add param-level details. The description's mention of 'top results' loosely aligns with resultCount but does not explicitly explain or augment 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 what the tool does: searches the web, fetches top results, and summarizes findings. It identifies a specific composite action, but it does not explicitly differentiate itself from the sibling tool 'deep_research' or other search/fetch tools, so it falls short of a 5.

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 is provided about when to use this tool versus alternatives like deep_research, search_web, or fetch_webpage. The phrase 'one-stop research' implies a broad use case, but there are no explicit conditions, exclusions, or alternative recommendations.

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 return format (base64 PNG) and cost ($0.008), which is useful. However, it does not mention any limitations, prerequisites, or behavioral characteristics beyond the basic action. For a non-destructive screenshot tool, this is sufficient but not rich.

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 three short sentences covering purpose, output format, and price. Every sentence earns its place, with no fluff or redundancy. It is optimally 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?

For a simple tool with only one required parameter and no output schema, the description adequately covers the core purpose, return format, and cost. It could benefit from a note about read-only behavior or usage alternatives, but given the low complexity and solid schema, it is nearly complete.

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

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

The schema provides 100% coverage with descriptions for all four parameters, so the baseline is 3. The description does not add any additional meaning about parameters, only stating the overall purpose. It neither improves nor detracts from the schema's 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 function with a specific verb and resource: 'Capture a screenshot of a webpage.' It also specifies the return format (base64 PNG), distinguishing it from sibling tools like fetch_webpage or crawl_site which retrieve textual content. This is unambiguous and unique.

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?

Usage is implied by the description and name—use when a visual screenshot is needed—but there is no explicit guidance on when to use it versus alternatives like fetch_webpage or crawl_site. No exclusions or alternative tools are mentioned, so it leaves the agent to infer context.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the price ($0.015), which is a useful behavioral detail, but it does not disclose whether the tool is read-only, the return format (array of strings), or any potential side effects like network limitations. The description is too sparse to be fully 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?

The description is extremely concise: two short sentences that front-load the core purpose and follow with pricing. Every word earns its place, with no fluff or repetition. This is an ideal model of efficiency.

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 only 2 parameters and no output schema, the description adequately covers returns ('suggestions') and the use case. However, it lacks explicit usage guidelines and more detailed behavioral transparency, leaving some gaps. Yet the low complexity means it is mostly complete.

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

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

Schema description coverage is 100%, so the baseline is 3. The description adds the phrase 'partial query' which clarifies the 'query' parameter's purpose, but it does not add any additional context for 'limit' or other parameters. The value added beyond the schema is minimal.

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: 'Get Google Autocomplete suggestions for a partial query.' This is a specific verb+resource combination that distinguishes it from sibling search tools (e.g., search_web, search_news) by emphasizing autocomplete functionality.

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 'keyword research and intent discovery' provides implied usage context, but the description does not explicitly state when to use this tool over alternatives, nor does it offer exclusions. The agent must infer that autocomplete is for partial queries vs. full searches, which is a reasonable inference but not directly stated.

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

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

With no annotations provided, the description carries the burden of disclosing behavior. It details the output contents (stories with points, comments, links, aggregates) and price, but does not explicitly state that it is a read-only operation, nor does it mention rate limits or other constraints. It gives useful but incomplete 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 concise at two sentences and front-loads the core purpose. The first sentence packs relevant detail about outputs and aggregates, and the second adds pricing. No fluff, but slightly dense phrasing prevents a perfect score.

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

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

For a simple search tool with no output schema or annotations, the description covers the main purpose, output structure, and pricing. It lacks explicit notes on read-only status or edge-case behavior, but overall it provides enough context for an agent to invoke the tool appropriately.

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 has 100% coverage for all three parameters, including descriptions for query, sort, and limit. The description adds general context about the tool's output but does not enhance understanding of the parameters themselves, so it aligns with the baseline for full schema coverage.

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

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

The description clearly states the tool's function: searching Hacker News discussions for a topic. It specifies the resource (Hacker News) and the action (search), and distinguishes it from sibling search tools like search_news and search_web by focusing on discussions and aggregates.

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 when to use the tool (when you want HN discussions), but it does not explicitly mention alternatives or state when not to use it. The context is clear from the wording, but lacks direct 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 are present, so the description must carry the burden of behavioral disclosure. It does list the expected return values (direct URLs, dimensions, thumbnails, source pages) and pricing, but it does not disclose potential rate limits, pagination behavior, or whether the operation is read-only. This is adequate but not 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?

The description is a single sentence that immediately states the tool's function and includes relevant output details, with the price appended as useful context. Every part earns its place; there is no redundancy or unnecessary elaboration.

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, the schema and description together are sufficient for an agent to correctly invoke it. The description covers return types, and the schema covers parameter constraints (limit default and max). It is complete for typical use, though it could be enhanced by noting any API-specific caveats like rate limits.

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 'query' and 'limit' having clear descriptions. The tool description does not add any parameter-level details beyond what the schema already provides, so it meets the baseline but does not exceed it.

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 specific action ('Search Google Images') and the resource being searched, distinguishing it from sibling search tools like search_web or search_news. It also lists the output types (direct image URLs, dimensions, thumbnails, source pages), making the tool's purpose unmistakable.

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 image searches but does not explicitly state when to use this tool over other search siblings, such as 'use search_web for general web results.' The context is clear from the name and description, but no explicit alternatives or exclusions 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 mentions price ($0.015) and 'real-time' nature, but does not explicitly disclose read-only behavior, rate limits, or error handling. The verb 'Search' implicitly suggests a safe read operation, but this is not explicit.

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 directly states the core functionality and cost. There is no redundant wording or unnecessary detail.

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 2-parameter search tool without an output schema, the description provides enough context: it names the data source (Google News), the output fields (source, date, thumbnail), and the price. It does not explain response structure, but that is not essential for such a straightforward 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?

The input schema already provides 100% coverage for both parameters with descriptions ('Number of results' and 'News search query'). The description adds no additional parameter-related meaning, so the 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 action ('Search'), the resource ('Google News'), and the result content ('articles with source, date, and thumbnail'). This differentiates it from sibling tools like search_web, which covers general web pages, or search_images for images.

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 'real-time articles' implies a news context, but the description does not explicitly state when to use this tool instead of alternatives like search_web. There is no mention of when-not-to-use or references 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 must bear the full burden. It does disclose the return fields (names, addresses, ratings, etc.) and the price ($0.045), which is useful. However, it does not mention rate limits, authentication requirements, pagination behavior, or any other operational details, leaving behavioral transparency incomplete.

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, well-structured sentence that front-loads the core purpose and then lists the returned data. The price is appended as a separate clause, adding practical value without wordiness. Every element earns its place.

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 low complexity (3 params, no output schema), the description covers the essential elements: what it does, what data it returns, and its cost. It lacks explicit guidance on default pagination or error handling, but these are not critical for a search tool. The description is reasonably complete for its scope.

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%, so the baseline is 3. The description does not add parameter-specific semantics beyond what the schema already provides (query, location, limit). It lists return fields but not parameter nuances, so it remains at 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 clearly states the tool's function with a specific verb ('Search') and resource ('local businesses via Google Local'), and enumerates the returned fields (names, addresses, ratings, reviews, phone, website, coordinates). This distinguishes it from sibling search tools like search_web and search_news, which target broader or different content types.

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

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

The description provides clear context by specifying 'local businesses via Google Local', which implies when to use it (when local business info is needed). However, it does not explicitly mention alternatives or exclusion conditions, 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?

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context like the price and return fields, but it does not disclose potential rate limits, authentication needs, or other operational caveats.

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 short sentences, with the main action front-loaded and the price included as a concise, practical addition. Every word earns its place with 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?

The description covers the tool's purpose and the type of information returned, which partially compensates for the absence of an output schema. The schema fully covers parameters, so for a simple tool this is nearly complete, though it could mention rate limits or other caveats.

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 'query' and 'limit' having clear descriptions. The tool description adds no additional parameter meaning beyond the schema, so the baseline score 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 tool searches Google Scholar for academic papers and specifies the content of results (snippets, publication info, citation counts). This distinguishes it from sibling search tools like search_web or search_news, which serve different purposes.

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 use for academic paper discovery, but it does not explicitly say when to choose this over sibling tools or provide exclusions. No alternative tools are mentioned, leaving usage guidance implicit rather than explicit.

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

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

With no annotations, the description carries the burden. It discloses the output content (prices, sellers, ratings, links) and the cost ($0.015), but does not mention rate limits, authentication, or side effects. As a read-only search tool, the absence of explicit safety info is a gap, but the output description adds some value.

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

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

The description is a single, front-loaded sentence that states the action and expected results, followed by the cost. No wasted words; every element is informative.

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 search tool with 2 parameters and no output schema, the description is nearly complete—it gives purpose and return contents. It lacks details like regional restrictions or result ordering, but these are minor 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?

The input schema covers both parameters with descriptions (100% coverage). The description adds no additional meaning about parameter formatting or usage beyond what the schema provides, so the 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 action ('Search'), the resource ('Google Shopping'), and the return contents ('prices, sellers, ratings, and links'). This distinguishes it from sibling search tools like search_web or search_images.

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 shopping-related product queries but does not explicitly state when to use this tool over alternatives like search_web. No exclusions or alternative names are mentioned, so guidance is implied rather than explicit.

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 of behavioral disclosure. It adds the price ($0.015) but does not reveal whether the operation is read-only, what the response contains beyond 'timeline', or any limitations (e.g., time range, granularity). This is insufficient for an API-like 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 a single sentence that front-loads the core function and includes a cost signal. There is no redundant information, making it highly 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 tool is simple, but the absence of an output schema means the description should explain what the returned timeline actually looks like (e.g., time series format, units, or default period). It only says 'timeline', which is vague and leaves room for ambiguity.

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 already describes the query parameter fully ('Topic to get trend data for'), and the description does not add substantial meaning beyond that. The phrase 'interest-over-time timeline' gives context but not specific requirements or formatting details.

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 action (Get) and the resource (Google Trends interest-over-time timeline) for a specific query. This distinguishes it from sibling tools like search_web and search_news, making its purpose unambiguous.

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

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

The description implies the tool is for retrieving Google Trends data, but it does not explicitly state when to prefer it over other search tools or provide exclusion criteria. It lacks explicit alternatives like 'for web search, use search_web instead'.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It adds useful context like real-time results, snippets, and the extra cost of fetching content, but it does not mention potential rate limits, timeouts, or failure modes. The standalone 'Price: $0.015' is vague, and the description omits details about content-fetching reliability, leaving gaps in 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 two sentences long, front-loaded with the core purpose, and includes cost information without unnecessary fluff. Each sentence earns its place: the first defines functionality, the second explains an optional feature and its cost.

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 moderate complexity (optional content-fetching feature and costs), the description explains the primary function, the optional behavior, and pricing in a concise way. It lacks an output schema and does not detail result format, but mentioning snippets gives adequate context. The ambiguous standalone price is a minor gap, yet overall 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?

Schema description coverage is 100%, meaning the schema already documents all parameters. The description only repeats the includeContent behavior (also in the schema) and adds general context instead of parameter-level details. Since the schema handles parameters, 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 function: 'Search the web' and mentions 'real-time results with snippets', which is specific and distinguishes it from targeted siblings like search_news or search_images. The verb+resource combination is unambiguous and directly conveys the tool's purpose.

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 that this tool handles general web searches with real-time snippets, and describes an optional content-fetching mode that expands its utility. However, it does not explicitly mention when to prefer this over sibling tools (e.g., search_news), nor does it state exclusions, so it falls short of full explicit 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?

With no annotations, the description carries the full burden of disclosing behaviors. It mentions a price ($0.035) but provides no details about side effects, required permissions, rate limits, or return format. The claim 'AI understands what you want' is vague and not actionable.

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 'Extract data using natural language', but the second sentence 'AI understands what you want' is marketing fluff that does not earn its place. It is concise but not tightly 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?

Given the absence of annotations and output schema, the description is incomplete for an agent to confidently select and invoke the tool. It lacks comparison to similar tools and does not explain what the output looks like or any limitations.

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 covers 100% of the parameters (url and query), and the query description already mentions natural language. The description adds little beyond the schema, meeting the baseline of 3.

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 'Extract data using natural language' which identifies the action (extract) and resource (data) clearly. However, it does not distinguish this tool from the sibling 'extract_data', so it lacks sibling differentiation.

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 is provided on when to use this tool versus alternatives like 'extract_data' or 'fetch_webpage'. The description simply states what it does without any contextual hints or 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 the burden. It adds some transparency by noting 'full transcript with timestamps' and 'Price: $0.03,' but lacks disclosure on error cases (e.g., missing transcripts), rate limits, or the exact structure of the returned data.

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 long, front-loading the purpose, then providing input format and pricing. Every word adds value, with 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?

The tool is simple and the description covers its core purpose, but with no output schema, it does not explain the format of the returned transcript beyond 'timestamps.' It also omits edge cases or prerequisites, leaving some gaps for a complete understanding.

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 already provides 100% coverage with descriptions for both parameters. The description reiterates the videoId input format but adds no new meaning for the lang parameter, so 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 'Get the full transcript of a YouTube video with timestamps,' which specifies the verb, resource, and output nature. It does not explicitly differentiate from sibling tools, but the specialized purpose is evident.

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 'Accepts a video ID or any YouTube URL,' which provides input guidelines, but it does not state when to use this tool over alternatives or any exclusions. Usage context is implied rather than explicit.

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