Server Quality Checklist

Tool Scores

• sports_scoresA
  4.6/5.0
  Behavior5/5

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

  With no annotations provided, the description carries the full burden and excels: it discloses unofficial status, ESPN terms-of-use restrictions, possible feed changes/blocking, failure means no charge, billing row rates, max_items hard spend cap, UTC timezone behavior, odds limitations, and trademark/copyright caveats. This is far beyond what structured annotations would normally provide.

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

  Conciseness4/5

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

  The description is dense and front-loaded with the main purpose before billing and legal details, and every clause adds operational value. It is however a single long paragraph; bullet points or short labeled sections would make it easier for an agent to parse, so it is not maximally structured.

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

  Completeness5/5

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

  For a multi-mode tool with 12 parameters, no annotations, and 0% schema coverage, the description covers modes, filters, billing, spending limits, timezone behavior, data source risks, and legal constraints. An output schema exists, so return-value documentation is not the description's job. Nothing essential an agent needs to select and invoke this tool correctly is missing.

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

  Parameters4/5

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

  Schema description coverage is 0%, so the description must compensate, and it largely does: it explains each mode value, teams/status filtering before billing, include_odds default, date_from/date_to scope, timezone affecting only dateLocal, max_items as a hard cap, and event_ids for summary mode. However, some parameters such as season_type and exact date string formats are left undescribed, preventing a 5.

  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 specific outputs (scores, schedules, standings, team directories, game summaries) and enumerates 33 leagues with concrete examples, making the tool's scope unmistakable. It also distinguishes itself from the sibling tennis_scores by covering multiple sports and explicitly noting a change in include_odds default versus the Actor.

  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?

  It provides clear mode-based usage: scoreboard for date-range games, schedule for a team's season, summary for event_ids from an earlier scoreboard run, and filtering by teams/status before billing. It does not explicitly state when to prefer the sibling tennis_scores, but the league list makes the boundary reasonably inferable.

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

• youtube_thumbnailsA
  4.6/5.0
  Behavior5/5

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

  With no annotations provided, the description carries the full burden, and it excels: it discloses that URLs are derived from a HEAD probe ('never guessed'), that save_images writes to the run's key-value store on the user's account, exact billing per video, the max_videos hard spend cap sent to Apify, and free row types. This is unusually transparent about side effects, costs, and edge cases.

  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 dense but not bloated; every sentence earns its place. The core operation is front-loaded, followed by parameter guidance, pricing/spend cap, free-row behavior, and a legal disclaimer. It is long only because it packs genuinely useful operational and financial details with no filler.

  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?

  For a 6-paramter tool with no annotations, the description is remarkably complete: it covers input format, output fields, storage side effect, billing, spend cap, free rows, unsupported playlists, and legal status. The only real gap is the lack of explicit guidance on image_format and include_metadata, though the output schema and self-explanatory names partly fill that gap.

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

  Parameters4/5

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

  Schema description coverage is 0%, so the description must compensate. It explains the main parameters well: videos (URLs or IDs), sizes (with allowed values), save_images (storage behavior), and max_videos (cost cap). However, image_format and include_metadata are not described, leaving two parameters to rely on schema enum/name inference. Still, the description adds substantial meaning for the most behaviorally significant parameters.

  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 starts with a specific verb and resource: 'Resolve YouTube video URLs or bare 11-character video IDs to their thumbnail image URLs.' It clearly distinguishes this tool from the unrelated siblings (extract_pdf, sports_scores, tennis_scores, list_supported_utilities) and specifies the exact output artifacts. The scope and function are immediately identifiable.

  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 gives clear usage context: when you need thumbnail URLs/metadata, how to choose sizes, and when to toggle save_images off for URL/metadata-only results. It does not explicitly name an alternative tool or state 'when not to use this tool,' but the context is strong enough for an agent to select it appropriately among the listed siblings.

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

• extract_pdfA
  4.7/5.0
  Behavior5/5

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

  With no annotations, the description carries the full disclosure burden and meets it: it reveals the two-tier billing model ($0.0003/page, $0.003/OCR page), the hard spend-cap semantics of max_pages with a concrete example (1000 pages = $0.30 or $3.00 with OCR), that the ceiling is forwarded to Apify as the run's maximum charge, and that summary/error/out-of-range rows are free. This is exactly the behavioral context an agent needs before spending money.

  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?

  Purpose is front-loaded in the first sentence, then modes, cost-protection, and billing follow in logical order. The description is long, but every sentence carries non-obvious information — the pricing paragraph is essential for a paid tool and would be risky to trim. There is zero filler.

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

  Completeness5/5

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

  For an 11-parameter paid tool with no annotations, the description covers the high-risk dimensions thoroughly: output shape per mode, page-range formats, the hard spend cap, and what is free vs billed. An output schema exists so return values need not be detailed, and the unmentioned parameters are low-risk booleans or bounded integers with obvious names.

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

  Parameters4/5

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

  Schema description coverage is 0%, so the description compensates. It adds real meaning to the semantically complex parameters: output_mode (return shape per mode), page_range (explicit format examples '1-5', '8', '12-'), max_pages (spend cap), and max_pages_per_pdf (charge avoidance). A few parameters (chunk_overlap, extract_tables, include_markdown, max_ocr_pages_per_pdf) are left to their names and defaults, which are largely self-explanatory.

  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 first sentence states a specific verb and resource: 'Extract text, markdown and ruled tables from PDFs at a URL', plus an optional capability (Tesseract OCR for scanned pages). The scope is unambiguous and naturally differentiates it from the unrelated siblings (youtube_thumbnails, sports_scores, tennis_scores, list_supported_utilities).

  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?

  Provides strong contextual guidance: when to enable OCR (scanned pages), how output_mode shapes the result ('page' one row per page, 'chunk' RAG chunks), and when page_range/max_pages_per_pdf apply (avoiding charges for unwanted pages). It stops short of explicit when-not-to-use statements, but no sibling tool competes with PDF extraction, so exclusions are not needed.

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

• list_supported_utilitiesA
  4.7/5.0
  Behavior5/5

  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 explicitly states 'No network, no cost,' which tells the agent this tool has no side effects and will not trigger charges, an important safety property for an agent choosing which tool to invoke. It also clarifies that the tool reports reachability and pricing rather than performing those operations.

  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 with no filler. The first sentence front-loads the purpose and the full list of returned details; the second adds the league-keys note and the crucial no-cost/no-network guarantee. Every clause earns its place.

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

  Completeness5/5

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

  This is a zero-parameter introspection tool with an output schema, so the description only needs to explain what the tool lists and any behavioral caveats. It covers the utilities, actors, pricing, spend cap, reachability, league keys, and the fact that it is free and offline. Nothing needed for correct invocation is missing.

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

  Parameters4/5

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

  The tool has zero parameters, so there are no parameter semantics for the description to clarify. The baseline of 4 applies because the description successfully explains what the tool does and what it returns, leaving no ambiguity about invocation.

  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 names a specific verb ('List') and resource ('utilities this server exposes'), then enumerates exactly what will be returned: the Apify Actor, per-event price, hard spend cap argument, reachability, and league keys. This clearly distinguishes it from the sibling tools, which are the actual utilities being listed.

  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 makes the tool's role as a discovery/introspection endpoint clear: it lists server utilities and key details such as cost and reachability. It also notes that league keys for sports_scores are returned here, giving a concrete reason to use this tool. It does not explicitly state when not to use it or compare against alternatives, but the context is strong enough for an agent to infer when to call it.

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

• tennis_scoresA
  4.7/5.0
  Behavior5/5

  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, and it does so extensively. It covers data source reality, billing and max_items as a hard spend cap, free rows, failure behavior if ESPN blocks access, date/time UTC handling, and legal/copyright restrictions. This is far beyond typical descriptions.

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

  Conciseness4/5

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

  The description is long, but the length is largely justified by billing, legal, and data-source caveats that are essential for correct use. Core functionality is front-loaded. It loses one point because the dense single-block structure and legal boilerplate could be organized more readably, but no sentence feels wasted.

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

  Completeness5/5

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

  The description covers what data is returned, defaults, filtering semantics, billing implications, failure modes, timezone behavior, and legal constraints. An output schema exists, so return values do not need enumeration. For a tool with billing and legal sensitivity, this is exceptionally complete.

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

  Parameters5/5

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

  Schema description coverage is 0%, so the description must compensate, and it does. It explains that player is a name substring, that dates are UTC unless a timezone is set which only affects dateLocal, that max_items is a billing cap, and that tours default to both. These details add significant meaning beyond the raw 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 identifies the tool as an ATP and WTA tennis match data retriever covering tour events and Grand Slams, with explicit detail about the fields returned such as per-set scores, round, seeds, and court. It distinguishes itself from the sibling sports_scores by positioning itself as the tennis-default version of the same Actor family.

  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 gives clear context for when to use the tool: for tennis scores from ESPN feeds, with both tours defaulting to today. It also warns that standings and schedule modes return a free notice for tennis, implying those are not supported. It stops short of explicitly stating 'use sports_scores for non-tennis sports,' but the 'same Actor family' reference provides enough implicit guidance.

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