mcp

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 that the tool uses ScrapingAnt for web scraping, mentions anti-bot avoidance capabilities, and describes proxy behavior. However, it doesn't cover rate limits, authentication needs, error conditions, or what happens when scraping fails. It provides some behavioral context but leaves important operational aspects unspecified.

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 followed by parameter explanations. Each sentence adds value, though the proxy_country explanation could be slightly more concise. The information is front-loaded with the core purpose first.

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

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

Given the tool's complexity (web scraping with anti-bot measures), no annotations, and an output schema (which handles return values), the description does well. It covers the scraping approach, parameter semantics, and usage context. However, it lacks information about error handling, rate limits, and authentication requirements that would be helpful for a scraping tool.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all 4 parameters. It explains what each parameter does, provides default values, gives usage guidance for proxy_type and proxy_country, and lists available country codes. This adds significant value beyond the bare schema.

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

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

The description clearly states the tool's purpose with specific verbs ('fetch', 'scrape') and resource ('web page content as HTML'), and distinguishes it from siblings by specifying the output format (HTML vs markdown/text). The first sentence directly answers what the tool does.

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 certain parameters (e.g., 'Use residential if you encounter anti-bot detection', 'Use when facing geo-restrictions'), but doesn't explicitly state when to choose this tool over its siblings (get_web_page_markdown, get_web_page_text) beyond the output format difference. No explicit exclusions or alternatives are mentioned.

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 that this is a scraping operation using a third-party service (ScrapingAnt) and mentions anti-bot avoidance capabilities. However, it doesn't cover important behavioral aspects like rate limits, authentication requirements, error handling, or what happens when scraping fails. The description adds some context but leaves 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 well-structured with a clear purpose statement followed by detailed parameter explanations. Every sentence adds value, though the parameter explanations could be slightly more concise. The information is front-loaded with the core purpose stated first.

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 4 parameters with 0% schema coverage and an output schema exists (so return values don't need explanation), the description does an excellent job covering parameter semantics. However, as a scraping tool with no annotations, it should ideally mention more about behavioral aspects like rate limits, costs, or common failure modes to be fully 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?

With 0% schema description coverage, the description fully compensates by providing detailed semantic information for all 4 parameters. It explains what each parameter does, provides default values, gives usage guidance (when to use residential proxy or specific countries), and lists available country codes. This adds substantial value beyond the bare schema.

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

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

The description clearly states the specific action ('fetch/scrape a URL using ScrapingAnt'), the resource ('web page content'), and the output format ('as Markdown'). It distinguishes from sibling tools get_web_page_html and get_web_page_text by specifying the Markdown output format.

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 about when to use certain parameters (proxy_type for anti-bot detection, proxy_country for geo-restrictions) and mentions the service provider (ScrapingAnt). However, it doesn't explicitly state when to use this tool versus the sibling HTML and text extraction tools, which would be needed for a perfect score.

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. It discloses the scraping service (ScrapingAnt), mentions anti-bot avoidance capabilities, and describes proxy behavior, but doesn't cover rate limits, authentication needs, error handling, or what happens with invalid URLs. It provides moderate behavioral 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 well-structured with a clear purpose statement followed by detailed parameter explanations. While somewhat lengthy due to parameter details, every sentence adds value and there's no wasted text. It could be slightly more 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?

Given 4 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining parameters and basic behavior. The existence of an output schema means return values don't need explanation. It could better address error cases and sibling tool differentiation.

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

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

With 0% schema description coverage, the description fully compensates by providing detailed semantic explanations for all 4 parameters. It explains what each parameter controls, default values, practical use cases (anti-bot detection, geo-restrictions), and even lists available country codes for proxy_country.

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 ('fetch/scrape a URL using ScrapingAnt') and resource ('web page content as plain text'), distinguishing it from sibling tools that return HTML or markdown formats. It provides a complete picture of what the tool does.

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 context through parameter explanations (e.g., 'Use residential if you encounter anti-bot detection'), but doesn't explicitly state when to choose this tool over siblings like get_web_page_html or get_web_page_markdown. It provides good practical guidance without explicit alternatives.

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