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

No annotations are provided, so the description carries the behavioral disclosure burden. It clarifies that the tool returns extracted content and is meant for read-oriented workflows, but it omits material behavioral details such as rendering cost implications, failure behavior on non-public or dynamic pages, or any rate limits.

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

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

Two sentences with no wasted words. The core purpose is front-loaded, followed by a concise list of outputs and use cases, making it easy for an agent 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?

There is no output schema and no annotations, and the tool has six parameters including browser rendering modes with credit costs. The description provides useful output and use-case context, but it does not mention rendering/credit implications or operational limitations that would help an agent decide and invoke it confidently.

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, so the baseline is 3. The description's list of returned content types loosely maps to the include parameter options, but it adds no parameter semantics beyond what the schema already provides.

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

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

The description states a clear verb and resource ('Extract clean content from a webpage') and enumerates the response types. It does not explicitly differentiate from sibling tools like tooltrace_metadata, tooltrace_links, and tooltrace_schema, which share some of those output 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?

It explicitly says 'Use this for scraping, RAG ingestion, or content analysis,' providing clear context for when the tool is appropriate. However, it does not mention alternatives or when not to use it versus the specialized 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, the description carries the full burden of behavioral disclosure. It does reveal a meaningful transformation behavior (normalizing URLs and classifying internal/external links), but it does not disclose important execution behaviors like rendering modes, credit costs, or dynamic-content handling that the render parameter implies.

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, front-loading the core action and resource. Every phrase adds information: extraction, scope, anchor text, classification, and URL normalization.

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 annotations and no output schema, and the description only partially covers return concepts (anchor text, classification, normalized URLs). It omits any explanation of rendering behavior, credit implications, or when to choose this over similar sibling tools, leaving significant context to the schema and user inference.

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's mention of 'normalized URLs' loosely aligns with the mode parameter, but it does not add meaningful semantic detail beyond what the schema already documents for each parameter.

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

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

The description states a specific verb ('Extract'), a clear resource ('all links from a webpage'), and key output enrichments (anchor text, internal/external classification, normalized URLs). This clearly differentiates the tool from sibling tools like tooltrace_metadata or tooltrace_tech_stack, even without seeing their descriptions.

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 about when to use this tool versus alternatives such as tooltrace_extract or tooltrace_sitemap. There is no mention of use cases, limitations, or conditions that would make this tool preferable.

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 behavioral disclosure burden. It communicates that the operation is a read-only extraction and adds the 'lightweight' behavioral trait, but it does not disclose potential side effects, rate limits, or response format. The schema covers rendering modes and credits, partially compensating for the lack of annotation 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 sentence with no filler. The core action and output fields are front-loaded, and the distinguishing 'Lightweight alternative' note is placed at the end without bloating the description.

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 fairly simple metadata-extraction tool, the description covers the core purpose and output scope, and the schema covers parameters. However, with no output schema or annotations, the description could have added a bit more about expected return shape or the conditions under which browser rendering becomes necessary beyond what the parameter descriptions already state.

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 all four parameters, including the render mode's credit implications. The tool description adds no parameter-level meaning beyond what the schema provides, 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 ('Extract') with a clear resource ('page metadata') and enumerates the concrete fields returned (title, description, canonical URL, author, etc.). The phrase 'Lightweight alternative to full extraction' explicitly differentiates this from the sibling tooltrace_extract, so an agent can distinguish it without opening schemas.

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

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

The description clearly implies when to use this tool: when only page metadata is needed, as a lighter-weight option than full extraction. It does not name the alternative sibling explicitly or provide an exhaustive when-not-to-use list, but the context is unambiguous enough for tool 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 provided, the description carries the full burden. It discloses the output type (schema.org entities) and that it extracts from a webpage, but says nothing about limitations such as JSON-LD-only support, dynamic-rendering behavior, credit implications, or failure cases. This is acceptable for a simple read-style tool but leaves notable 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 compact two-sentence definition with no filler and the core action front-loaded in the first clause. It loses a point only because the 'etc.' tail adds little and could be replaced with a more precise statement about normalized/raw modes.

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 five parameters, three enums, and no output schema, the description gives only a high-level outcome. It does not explain how mode affects returned data, when rendering matters, or what the raw output looks like; some of this lives in the schema but the overall context is thin.

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 documents all five parameters (url, mode, render, wait_until, wait_for_selector). The description adds no parameter-level meaning beyond mentioning entity categories, which is not needed because the schema does the heavy lifting.

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

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

The description names a specific action ('Extract'), a specific resource ('JSON-LD structured data from a webpage'), and enumerates concrete schema.org entity types. This clearly distinguishes it from sibling tools focused on sitemaps, metadata, links, tech stack, or generic extraction.

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

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

The intended use is implicitly clear: select this tool when JSON-LD/schema.org entities are needed from a URL. However, it never explicitly states when not to use it or names an alternative such as tooltrace_extract for non-JSON-LD extraction, leaving the routing decision largely 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 provided, the description carries the full burden of behavioral disclosure. It discloses the output shape (0-100 score, weighted checks, evidence, recommendations), and the word 'audit' conveys a non-destructive read operation. It stops short of describing failure behavior for unreachable URLs or cost escalation when render='auto' falls back to a browser, but those details partially exist in the 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?

Two sentences with zero filler. The action is front-loaded in the first sentence, and the second compresses the output format and the full check list without redundancy or vague hedging. Every clause 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?

There is no output schema, but the description compensates by summarizing the return value (score, evidence, recommendations). Combined with the well-documented parameter schema, an agent has enough to select and invoke the tool correctly. The remaining gaps — precise return structure and failure-mode notes — are minor for an audit 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% — url, render, wait_until, and wait_for_selector all have substantive schema descriptions, including credit costs, defaults, and conditional usage ('Only used with browser rendering'). The description adds nothing about parameters, which meets the baseline of 3 for high-coverage schemas.

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+resource — 'Run an SEO audit on a webpage' — and precisely enumerates the audited dimensions (metadata, headings, images, canonical signals, robots directives, social tags, schema, content length) plus the scoring output. The comprehensive audit scope implicitly distinguishes it from the narrower single-purpose siblings like tooltrace_metadata, tooltrace_links, and tooltrace_schema.

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 use case is implied: call this when you need a holistic, scored SEO audit. However, the description never states when to prefer this over the specialized siblings or when a lighter tool would suffice, and the siblings overlap heavily with the audit's own checks (metadata, links, schema). The render parameter in the schema adds cost/behavior context, but the description itself offers no explicit when-to-use or when-not-to-use guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It clearly describes the operation pipeline: discovering sitemap URLs, parsing sitemap XML, and returning page metadata. It does not cover failure modes or discovery specifics such as robots.txt handling, but the core behavioral contract is explicit and useful.

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 with no filler. The primary purpose is front-loaded, and the second sentence efficiently lists the behavioral steps and return values. Every phrase 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 low-complexity tool with two parameters and no output schema, the description covers what the tool does, what it returns, and the main input variations (website URL or direct sitemap URL). It omits edge cases like sitemap index files or missing-sitemap behavior, but an agent has enough information to call 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?

Schema description coverage is 100%, with both 'url' and 'render' fully documented including render's credit costs and default. The tool description adds no additional parameter-specific 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 states a specific verb-and-resource combination: inspect a website's sitemap, discover sitemap URLs, and parse sitemap XML. It clearly differentiates this from sibling tools by focusing on sitemap-specific outputs (page URLs, last-modified dates, change frequencies) rather than generic extraction or metadata.

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 purpose statement implies that this tool should be used when sitemap information is needed, but it does not explicitly say when to prefer it over alternatives like tooltrace_links or tooltrace_metadata. There are no exclusion criteria or named alternative tools, so the agent must infer usage from sibling names.

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 only mentions that it 'returns categorized detections with confidence levels'; it does not state that the tool performs network requests, is read-only, has credit costs, or the implications of rendering modes. These gaps are significant even though the schema describes render credits.

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

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

Two sentences, with the action verb and resource front-loaded, followed by an efficient list of categories. There is no filler or redundant phrasing; every clause 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?

With no output schema, the description provides a useful summary of what is returned (categorized detections with confidence levels) and names the common detection areas. However, it is minimal for a 4-parameter tool and lacks behavioral/contextual nuance like rendering behavior and credit implications, though those are partially covered by the input schema.

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

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

Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The tool description adds no additional parameter-level meaning 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 names a specific verb and resource ('Detect the technology stack of a website') and enumerates concrete detection categories (CMS, frameworks, JavaScript libraries, etc.). This scope clearly sets it apart from sibling tools like tooltrace_sitemap or tooltrace_metadata without needing to open their schemas.

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

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

The description states what the tool does but gives no guidance on when to choose it over alternatives or any exclusions. Sibling tools are available in context signals, but the description never references them or suggests conditions for selecting this tool.

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