Server Quality Checklist

Tool Scores

• fetch_parsedA
  3.9/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions the ScrapeUnblocker mechanism and that output is AI-parsed structured JSON, providing some transparency about the process. However, it does not disclose potential non-determinism of AI parsing, possible errors, or side effects of scraping (e.g., rate limits, target site load).

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

  Conciseness5/5

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

  The description is two sentences, front-loaded with the core function, and every sentence adds value. It avoids unnecessary elaboration and is highly scannable.

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

  Completeness3/5

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

  With no output schema and no annotations, the description is somewhat incomplete: it doesn't describe the expected JSON structure or mention that AI-parsed output may vary in shape. It covers the main purpose and use cases, but for a tool with unstructured AI output, a note about variability or potential failure would improve completeness.

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

  Parameters3/5

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

  Schema description coverage is 100%, so all parameters are already documented in the schema. The description adds context about typical extraction targets (product details, article content), which implicitly informs rules_hint, but does not add significant new meaning beyond the schema.

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

  Purpose5/5

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

  The description clearly states the tool's function: fetching a web page through ScrapeUnblocker and returning AI-parsed structured JSON instead of raw HTML. It specifies use cases (product details, article content), distinguishing it from the sibling fetch_html which presumably returns raw HTML.

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

  Usage Guidelines4/5

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

  The description provides clear usage context: 'Best for extracting fields from product, listing or article pages without writing your own HTML parsing.' It implicitly contrasts with raw HTML retrieval, suggesting when not to use it, but does not explicitly name the alternate tool or exclusions beyond that.

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

• google_searchA
  4/5.0
  Behavior3/5

  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 adds useful context by mentioning ScrapeUnblocker as the execution path and 'organic results' as an output filter. However, it omits potential behavioral details like rate limits, pagination behavior, error responses, or any caveats about scraping Google.

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

  Conciseness5/5

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

  The description is two sentences: the first states the action and output, the second states the use case. Every clause contributes value, and it is front-loaded with the core purpose. No redundancy or fluff.

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

  Completeness4/5

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

  The description covers what the tool does, how it does it, and when to use it. Since there is no output schema, it would benefit from specifying more about the returned JSON structure, but 'discover URLs' implies the key output. Given the simple parameter set and clear sibling relationship, this is largely complete but could be more detailed.

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

  Parameters3/5

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

  All three parameters (keyword, proxy_country, pages_to_check) have complete schema descriptions (100% coverage), so the baseline is 3. The tool description adds no parameter-specific nuance, but it doesn't need to because the schema already explains them well.

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

  Purpose5/5

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

  The description states a specific action and resource: 'Run a Google search through ScrapeUnblocker and return the organic results as structured JSON.' It clearly distinguishes this from sibling tools (fetch_html, fetch_parsed) by emphasizing search results rather than page content.

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

  Usage Guidelines4/5

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

  The line 'Use this to discover URLs before fetching them' gives clear usage context and positions this tool as a discovery step relative to fetch tools. It does not explicitly name alternatives or state when not to use, but the guidance is clear enough.

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

• fetch_htmlA
  4.2/5.0
  Behavior4/5

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

  With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool bypasses anti-bot protection, uses a real browser to render, and returns raw HTML as text. This reveals key behavioral traits, though it omits details like rate limits or potential latency.

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

  Conciseness5/5

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

  The description is three concise sentences: the core action, when to use it, and the return format. It is front-loaded and every sentence earns its place.

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

  Completeness4/5

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

  Given the tool's complexity (6 params, no output schema), the description covers the main purpose, use cases, and return value. It doesn't explain all parameters in the description, but the schema covers them fully. It could be more complete by contrasting with fetch_parsed, but overall it is sufficient.

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

  Parameters3/5

  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 specific parameter-level semantics beyond the schema, but the mention of 'fully rendered' hints at the purpose of wait parameters. It provides no extra value for the params, hence a 3.

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

  Purpose5/5

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

  The description states the tool fetches the fully rendered HTML of any web page through ScrapeUnblocker, bypassing anti-bot protections. This clearly distinguishes it from siblings like fetch_parsed (likely structured extraction) and google_search, and from a normal fetch.

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

  Usage Guidelines4/5

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

  The description explicitly states when to use this tool: 'when a normal fetch is blocked (403/429, captcha, access denied) or when the page needs a real browser to render.' This gives clear usage context, though it doesn't explicitly mention alternatives like fetch_parsed.

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