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

With no annotations, the description carries full responsibility for disclosing side effects, auth needs, rate limits, or return behavior. It only says 'deep dive,' which implies a read-only analysis but provides no specifics about what is analyzed, how deep the inspection goes, or what the output contains. This falls short of useful behavioral disclosure.

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 with no filler, but it is under-specified rather than efficiently concise. It front-loads the purpose but leaves out critical details, making it a hybrid of brevity and vagueness rather than a model of concise specification.

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

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

Given the simplicity (only 2 params, no output schema) but the crowded sibling context, the description should explain what 'deep dive' yields, whether 'waitTime' affects depth, and how it differs from similar analytics tools. It does none of this, leaving the agent with insufficient contextual information to choose and 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 schema covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds no extra meaning beyond the schema—it does not mention 'url' or 'waitTime' or how they affect the analysis. Thus, no value beyond structured data is 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 identifies a specific resource (analytics platforms) with concrete examples (GA4, UA, Adobe Analytics) and uses 'deep dive' to imply a comprehensive analysis. It distinguishes from siblings like 'detect-ad-pixels' or 'identify-tag-managers' by focusing on analytics platforms rather than general marketing tech, though 'deep dive' lacks a precise operational verb.

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 the many sibling tools (e.g., 'parse-analytics-beacons', 'scan-marketing-stack'). The description only states what it does, leaving the agent to infer potential alternatives without explicit exclusions or conditions.

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. However, it only states the purpose and does not disclose whether the tool is read-only, whether it fetches the page, what it returns, or any side effects. The audit nature suggests a read operation but is not explicit, leaving significant ambiguity.

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 verb and key subject. It contains no unnecessary words and is appropriately sized for a tool with one parameter and a straightforward purpose.

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

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

The tool has no output schema and no annotations, so the description should explain what the audit produces or returns. However, it stops at the action itself without mentioning the output format (e.g., report, score, list of missing alts), prerequisites, or any behavioral details. This leaves a notable gap for the user to understand the tool's full usage.

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 a description for the single 'url' parameter (100% coverage), so the baseline is 3. The description does not add extra meaning about the parameter beyond implying that a webpage is needed, nor does it specify constraints like accessibility or format beyond what the schema states.

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 ('Audit') and a concrete resource ('image alt text on a webpage'). It also specifies the dual objectives of 'accessibility and SEO compliance', which distinguishes it from sibling tools like 'audit-seo' that likely cover broader SEO aspects.

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

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

The description implies the tool is used when auditing image alt text, but it provides no explicit guidance on when to use this tool versus alternatives or any exclusions. It does not mention related sibling tools or clarify scenarios where this tool is not 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 provided, the description carries the full burden of behavioral disclosure. It only says 'analyze', implying a read operation, but fails to mention whether it fetches the page, any prerequisites, rate limits, or what constitutes 'SEO metadata and structure'. This lack of detail reduces 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, a single sentence that front-loads the action and target. However, it is under-specified, lacking definitions for ambiguous terms like 'SEO metadata' and 'structure', which diminishes its overall effectiveness despite its brevity.

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 minimal and incomplete. There is no output schema or annotations to provide additional context, and the vague phrasing does not clarify what the analysis entails or what results the agent can expect. Given the existence of closely related sibling tools, this description is insufficient.

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 fully describes the only parameter (url) with a clear definition, and the description adds minimal extra meaning by reiterating that it's the webpage to analyze. Given high schema coverage (100%), the baseline is 3, and the description doesn't significantly enhance parameter understanding.

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 (analyze) and the resource (SEO metadata and structure on a webpage), making the core purpose understandable. However, it does not distinguish this tool from overlapping siblings like check-page-metadata or evaluate-page-structure, so it 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 the many similar marketing/SEO analysis tools. The description neither mentions alternatives nor specifies the context in which this tool is preferred, leaving the agent without clear selection criteria.

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

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

No annotations are provided, so the description must disclose behavioral traits. It fails to mention that the tool likely fetches a webpage (a network operation), whether it is read-only, any authentication needs, or what the output format looks like. The single sentence only states the action, not its implications.

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, focused sentence of nine words with no filler. It is appropriately sized for a simple tool with one parameter.

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 low complexity (one parameter, no output schema), but the description lacks any information about return values or operational details. While 'Analyze meta tags, titles, and descriptions' gives a basic idea, it does not fully specify what the agent should expect after invocation, leaving some 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?

Schema description coverage is 100% for the 'url' parameter, providing a clear description. The description does not add additional meaning beyond the schema, but since the schema already covers it, a 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 verb 'Analyze' and specifies the resource: meta tags, titles, and descriptions on a webpage. This distinguishes it from sibling tools like extract-schema-markup or audit-seo, which target different aspects.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or comparisons to sibling tools like audit-seo or evaluate-page-structure.

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 for behavioral transparency. It only mentions focusing on advertising platforms, which hints at scope but does not disclose what the tool returns, whether it performs any analysis, or how it behaves in edge cases (e.g., no pixels found). No details about output, side effects, or requirements are provided.

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 one sentence with no wasted words, but it is under-specified rather than efficiently concise. It lacks important details that would make the sentence informative, so while brevity is good, the content is too thin to be considered well-structured.

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

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

Given the tool's purpose (detecting ad pixels), the description is incomplete. It does not explain what the output looks like, what constitutes an ad pixel, or how the tool handles the URL. Even with a simple schema, the lack of behavioral context makes it insufficient for an agent to correctly invoke and interpret 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 schema provides 100% coverage for both 'url' and 'waitTime' parameters with clear descriptions. The tool description adds no additional parameter meaning. Since schema coverage is high, the baseline of 3 is appropriate; the description does not need to compensate, but it also does not enrich the parameters.

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 says to 'Focus on advertising platforms (Facebook, TikTok, etc.) on a webpage', but it lacks a specific verb like 'detect' or 'find'. The tool name suggests detection, but the description is vague about the actual action, making it ambiguous whether it analyzes, lists, or reports. It does identify the resource (advertising platforms) and distinguishes from siblings by scope, but the verb is weak.

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 sibling tools like 'find-marketing-technologies' or 'analyze-analytics-platforms'. The description gives no context about prerequisites, situations, or exclusions, leaving the agent without clear decision criteria.

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 available, the description carries the full burden of disclosing behavior. It only lists the analysis scope but does not mention read-only status, network activity, return format, or error conditions, leaving key behavioral traits undisclosed.

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 front-loads the verb and clearly enumerates the tool's focus. There is no wasted wording 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?

For a simple one-parameter tool, the description gives a high-level overview of what is analyzed, but without annotations or an output schema, it does not explain the return value or behavior beyond the scope. It is minimally complete but leaves gaps about expected output.

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

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

The input schema provides 100% coverage for the single parameter 'url', including a clear description. The tool description does not add additional meaning beyond the schema's parameter description, 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 uses a specific verb 'Analyze' and details the resource: URL structure, headings hierarchy, and page organization. This clearly distinguishes the tool from siblings focused on metadata, SEO, or marketing technology, though it does not explicitly contrast with alternatives.

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 description lacks use cases, prerequisites, or exclusions, leaving the agent without decision criteria 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 full burden. It only states 'Analyze' but does not disclose whether the tool performs network requests, what side effects exist (e.g., hitting external URLs), or any limitations such as rate limits. The read-only nature is implied but not explicitly stated.

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, focused sentence that communicates the core purpose without any 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?

While the tool is simple with one parameter, there is no output schema, so the description should explain what the analysis returns (e.g., extracted markup, structured data). It only says 'Analyze' without specifying output shape, leaving the return value ambiguous.

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 covers the single 'url' parameter with a clear description. The tool description adds no extra semantic value beyond what the schema already provides, so a 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 explicitly states the verb 'Analyze' and the resource: 'JSON-LD, microdata, and schema.org markup on a webpage.' This clearly distinguishes it from sibling tools like audit-seo or find-marketing-technologies, which focus on other aspects.

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

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

No explicit when-to-use or alternative guidance is provided. However, the description and tool name imply it is the go-to tool for extracting schema markup, differentiating from overlapping siblings like check-page-metadata or audit-seo.

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

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

Annotations are absent, so the description carries the full burden. It only states an outcome ('high-level overview') with no detail on detection method, limitations, network requests, or output behavior. This leaves significant behavioral ambiguity.

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?

One sentence, front-loaded with the action, no redundancy. It is concise but perhaps too terse to convey the tool's full context.

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 must explain scope, return value, and limitations, but it only gives a high-level phrase. With numerous sibling tools, more context is needed to select 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?

Input schema covers 100% of parameters with descriptions, so baseline is 3. The description adds no additional parameter semantics beyond the schema's definitions of url and waitTime.

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 clear verb ('Get') and specifies the resource ('all marketing technologies on a webpage') and scope ('high-level overview'). However, it does not differentiate from sibling tools like scan-marketing-stack or identify-tag-managers, so it is not 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 usage guidance is provided beyond the inherent implication that the tool is for obtaining an overview. There are no exclusions, alternatives, or context about when to prefer this over 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?

With no annotations provided, the description carries the full burden of behavioral disclosure, but it only offers a high-level statement. It does not explain how highlighting is applied, whether the page is modified, if authentication or rate limits apply, or what the return value looks like. Essential behavioral details 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 a single, concise sentence that front-loads the core action. It has no redundant fluff and every word contributes to understanding the tool's purpose, exemplifying good conciseness.

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

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

Given the lack of annotations and output schema, the description is too sparse to provide complete context. It does not clarify the output format, how highlights are rendered, or how this tool differs from similar siblings like visualize-marketing-tech. The tool's nominal simplicity is not enough to compensate for these gaps.

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

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

The schema describes both parameters (url, highlightPixels) with 100% coverage, so the description need not elaborate on them. The description adds no extra parameter meaning, but the high schema coverage supports a 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's function: taking a screenshot and visually highlighting marketing technologies. The verb 'take a screenshot' and the qualifier 'with marketing technologies visually highlighted' distinguish it from sibling tools like puppeteer_screenshot or find-marketing-technologies, making the 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 provides no explicit guidance on when to use this tool versus alternatives such as find-marketing-technologies or visualize-marketing-tech. It does not mention use cases, exclusions, or context, forcing the agent to rely on the tool name and schema for decision-making.

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 must disclose behavioral traits, but it only says 'Analyze' without explaining how the analysis works, whether it loads the page, or what side effects (e.g., network requests) occur. This is a significant black box for an agent, as it does not know what happens behind the scenes.

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 front-loads the purpose. Every word adds value, and it avoids 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?

There is no output schema and no annotations, so the description needs to provide adequate context for what the tool returns or what constitutes a tag management system. It fails to do so, offering only a one-line summary that does not cover expected behavior or results, making it incomplete for a tool with these gaps.

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

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

The schema provides 100% description coverage for both parameters (url and waitTime), so the schema already explains their meaning. The description itself adds no extra parameter details, but per rubric, the baseline is 3 when schema coverage is high.

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 ('Analyze') and the resource ('tag management systems (GTM, Tealium, etc.) on a webpage'), which is specific and distinctive. It distinguishes itself from sibling tools like find-marketing-technologies or analyze-analytics-platforms by explicitly focusing on tag managers.

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 when tag manager detection is needed, but it does not explicitly state when to use this tool versus alternatives like find-marketing-technologies or scan-marketing-stack. There is no mention of when not to use it, so guidance 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 full responsibility for behavioral disclosure. It only states 'capture and parse' without revealing that the tool may navigate to a page, wait for beacons, or simulate clicks (as implied by the waitTime and simulateClick parameters). Potential side effects, prerequisites, or limitations are omitted, leaving the agent without critical operational context.

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

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

The description is a single, concise sentence with no wasted words. It gets straight to the point, which is ideal for quick scanning. Despite its brevity, it effectively conveys the core purpose 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?

The tool has no output schema and no annotations, so the description must explain return values and behavioral context, but it remains minimal. It does not mention what the parsed output looks like, how the tool handles multiple beacons, or the role of the optional parameters (waitTime, simulateClick). Given the tool's moderate complexity, the description is insufficient for an agent to fully anticipate its 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?

The input schema covers 100% of parameters with descriptions, so the baseline is 3 even without additional context from the tool description. The description does not add extra meaning beyond the schema; it merely mentions 'beacons' without tying it to the parameters. Thus, the schema does the heavy lifting, and the description adds no incremental value.

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: capturing and parsing Adobe Analytics and Experience Platform beacons on a webpage. It uses specific verbs ('capture', 'parse') and names the exact platforms, effectively distinguishing it from siblings like 'track-marketing-beacons' or 'analyze-analytics-platforms'.

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 by specifying the platforms (Adobe Analytics and Experience Platform), but it does not explicitly state when to use this tool versus alternatives, nor does it mention when not to use it. There is no direct comparison or exclusion of sibling tools, so the guidance remains implicit rather than actionable.

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 disclose behavior. It only states 'Navigate to a URL' without revealing whether navigation waits for page load, handles errors, returns a result, or how the dangerous LaunchOptions affect security. This is minimal behavioral disclosure.

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 with zero filler. It is appropriate for a simple navigation action.

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 this is a 3-parameter tool with no output schema and no annotations, the description is too sparse. It does not explain return behavior, the security implications of allowDangerous, or how navigation fits with other tools. The schema describes parameters but the tool's overall execution context is missing.

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 each parameter already described (url, launchOptions, allowDangerous). The description adds no additional parameter guidance, but the schema carries the full burden, so a baseline score of 3 applies.

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

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

The description uses the specific verb 'Navigate' and identifies the resource 'URL', clearly distinguishing this from sibling tools like puppeteer_screenshot and the marketing analysis tools. However, it lacks any additional context about the tool's role (e.g., browser automation prerequisite), so it is clear but not rich.

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?

There is no mention of when to use this tool versus alternatives, nor any context about it being a prerequisite for other Puppeteer tools. No 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?

With no annotations, the description must carry the full burden of behavioral disclosure. It fails to mention what happens after the screenshot is taken—whether it saves to a file, returns a data URL, or requires further handling. Side effects, output format, and error behavior are entirely absent, leaving the agent uncertain about the tool's effects.

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

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

The description is a single, front-loaded sentence that directly states the tool's purpose without redundancy. Every word earns its place, making it exceptionally concise and well-structured.

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

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

The tool has no output schema and no annotations, so the description must compensate. It does not clarify what the tool returns (e.g., file path, base64 image) or how the captured screenshot is delivered. With four parameters, including optional sizing and element targeting, the tool is moderately complex, yet the description omits essential context for an agent to use 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 provides 100% coverage with descriptions for all four parameters (name, width, height, selector), so the baseline is 3. The description's phrase 'or a specific element' loosely maps to the selector parameter but adds no new meaning beyond what the schema already states.

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: 'Take a screenshot of the current page or a specific element'. It uses a specific verb ('take') and resource ('current page or specific element'), and distinguishes itself from sibling tools like puppeteer_navigate by focusing on capture rather than navigation.

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. Sibling tools like audit-seo or evaluate-page-structure are not mentioned, and there is no context about scenarios where a screenshot is preferred over other analysis tools. The description only states what it does, not when to apply 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, the description carries the full burden of behavioral disclosure. It does not mention whether the webpage is loaded or rendered, side effects, timeouts, or limitations. This is insufficient for a scanning tool that likely interacts with a live page.

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, front-loaded with 'Comprehensive analysis,' and contains no redundant or filler words. It is appropriately sized for the minimal information it conveys.

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 too minimal for a tool with two parameters and no output schema. It does not explain the output format, the scope of 'marketing and analytics technologies,' or how this scan differs from the many sibling tools, leaving significant gaps for an agent to decide and invoke the tool 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 provides 100% description coverage for both parameters (url and waitTime), so the description does not need to add parameter details. The description adds no additional semantic meaning beyond what the schema already provides, which 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 a comprehensive analysis of marketing and analytics technologies on a webpage. The verb 'analysis' and resource are specific, but it doesn't explicitly distinguish from very similar sibling tools like find-marketing-technologies, so purpose lacks clear 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 such as detect-ad-pixels or identify-tag-managers. The description only states what it does, not when it is the preferred choice or any 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 present, so the description carries the full burden of explaining behavior. It only states 'analyze network requests' without disclosing that the tool likely navigates to the URL, waits for requests, or returns a list. The agent is left uncertain about side effects, prerequisites, and output structure.

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 is front-loaded with the core action and resource. Every word contributes without filler, making it appropriately concise. Despite being brief, it avoids redundancy and is easy to scan.

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 (3 parameters), lack of output schema, and absence of annotations, the description is incomplete. It does not explain return values, parameter interplay, or behavioral details like page loading, which are essential for an agent to invoke the tool 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?

Input schema covers all 3 parameters (url, waitTime, maxRequests) with descriptions, providing 100% coverage. The description adds no extra meaning beyond the schema, such as how waitTime affects capture or how maxRequests truncates results, so it meets the baseline but does not enhance parameter understanding.

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 the specific verb 'analyze' and identifies the resource as 'network requests' with a focus on tracking and marketing activities. While this clearly states the tool's function, it does not explicitly distinguish it from overlapping sibling tools like parse-analytics-beacons or detect-ad-pixels, so it falls short of full 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?

The description provides no guidance on when to use this tool versus the numerous siblings (e.g., find-marketing-technologies, analyze-analytics-platforms). There is no mention of preferred scenarios, exclusions, or alternatives, leaving the agent without selection criteria.

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 behavior. It does not state whether the tool performs its own technology detection, how highlights are rendered, whether the page is modified, or what the screenshot output contains. It also does not mention network/permission implications, so the agent is left without critical operational context.

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

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

The description is a single, front-loaded sentence with no wasted words. It conveys the essential action and outcome efficiently.

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 2-parameter tool with no output schema, the description is adequate but incomplete. It doesn't clarify whether the tool internally detects technologies or relies on external data, nor does it describe the screenshot format or any side effects. Given the sibling tools, more context would help disambiguate its exact role.

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 adds minimal additional meaning—the word 'detailed' loosely maps to the 'highlightMode' enum but does not explain the difference between 'basic' and 'detailed' beyond the schema's own description.

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

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

The description clearly states 'Take a screenshot with detailed marketing technologies visually highlighted', which identifies the verb, resource, and output. It distinguishes itself from plain screenshot tools and detection tools enough to suggest a visual deliverable, though it doesn't explicitly differentiate from 'highlight-marketing-tools'.

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

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

The description gives no guidance on when to use this tool versus alternatives like 'find-marketing-technologies' or 'puppeteer_screenshot'. There are no stated exclusions or preferred contexts, leaving the agent to infer usage from the purpose alone.

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