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

With no annotations provided, the description carries the full burden and does so thoroughly. It explains default behavior (render JavaScript, depth 1, limit 10), optional behaviors (include_subdomains, include_external_links, patterns), and warns about large responses exceeding token limits. It also states the output format (Markdown) and the trade-off of disabling render.

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 well-structured and front-loaded: it states the core function first, then best-use cases, exclusions, a practical tip, and a clear Args list. Every sentence serves a purpose; length is justified by the tool's complexity and the absence of schema-level descriptions.

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 complex 8-parameter tool, no annotations, and an output schema, the description still manages to cover all needed context: purpose, use cases, exclusions, parameter semantics, default behaviors, and a warning about potential token limits. It is complete enough for an agent to select 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 has 0% description coverage, so the description must compensate—and it does. The 'Args' section explains every parameter's meaning, defaults, and constraints (e.g., 'limit: Maximum number of pages to crawl (default: 10, max: 100000)'). This adds significant semantic value beyond the bare schema types.

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: 'Crawl multiple pages starting from a URL and return all content as Markdown.' It uses a specific verb ('crawl'), names the resource ('a URL'), and explicitly differentiates from siblings by noting 'Single pages (use scrape_url — it's faster)'.

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 when-to-use guidance: 'Best for' fetching entire documentation sections, blog archives, or multiple related pages. It also gives clear when-not-to-use guidance and names the alternative tool (scrape_url) for single pages. The tip about using include_patterns to scope crawls adds practical usage 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, the description discloses the key behavior of not fetching full page content, that it returns a list of URLs, and that it crawls with configurable depth/limit and include/exclude patterns. It clearly states it 'discovers' URLs, implying a read-only operation.

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 well-structured with a clear intro, best-for list, typical workflow, and concise args explanations. Each sentence adds value; no wordiness.

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 an output schema present, the description doesn't need to explain return values. It covers purpose, usage context, parameter semantics, and behavior comprehensively, making it complete for an agent to select and invoke 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 description provides a detailed Args section that explains every parameter, including the meaning of limit, depth, include_subdomains, include_external_links, include_patterns, and exclude_patterns, with an example wildcard for include_patterns. This fully compensates for the 0% 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 states 'Discover URLs on a website without fetching full page content' and 'Returns a list of URLs found by crawling from the starting URL.' This clearly identifies the resource (website URLs) and specific verb (discover), and distinguishes it from sibling tools like scrape_url, which fetches content.

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 to find the right page before scraping it' and includes a 'Typical workflow: map_url to find URLs -> scrape_url on the right page.' It also lists best-for use cases, providing clear guidance on when to use and an alternative.

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 JavaScript rendering behavior via the render flag and notes the performance tradeoff, adding behavioral detail beyond 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?

The description is well-structured with a clear purpose statement, best-for/not-recommended sections, and an Args list. Every sentence adds useful context 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's behavior, use cases, and parameter options are fully described. The output is described as Markdown content, and the presence of an output schema (not shown here) would further define the return value, so 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?

The schema has zero description coverage, but the 'Args' section in the description explains both parameters. It defines 'url' as the URL to fetch and explains 'render' including its default value and effect on performance.

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: 'Fetch a single web page and return its content as Markdown.' It identifies the specific resource (web page) and distinguishes itself from siblings by noting when to use it versus map_url and crawl_url.

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 usage guidance: 'Use this when you know which URL contains the information you need.' It also gives clear exclusions and alternatives, saying to use map_url when the URL is unknown and crawl_url for multiple pages.

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