Server Quality Checklist

Tool Scores

• suggest_affiliate_productsA
  4.6/5.0
  Behavior4/5

  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 notes that revenue estimates are algorithmic benchmarks (not live pricing), equipment ranks highest in commission, and the output is sorted by affiliate score with price ranges. It does not cover potential rate limits or caching behavior, but the provided details are substantial.

  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 concise—four sentences—with no filler. It front-loads the main purpose, then provides ranking priority, usage guidelines, input constraints, and an example. Every sentence adds value.

  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 has 4 parameters, no output schema, and no annotations, the description explains the output format (sorted items with price range and commission) and notes on data accuracy. Missing details like pagination or error handling, but provided information is sufficient for typical use cases.

  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 100% with adequate parameter descriptions. The description adds context by stating the tool accepts ingredient list or recipe_id from extract_recipe_ingredients and provides an example ('recipe_name='Beef Bourguignon''). This adds meaningful usage guidance 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 begins with a specific verb-object pair: 'Generate a revenue-ranked affiliate shopping list for a recipe.' It clearly states the resource (shopping list) and the action (generate, sorted by commission). It also distinguishes from sibling tool match_ingredients_to_products by explaining that tool is for ingredient-level SKUs.

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

  Usage Guidelines5/5

  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 ('ranked shopping lists and blog monetization strategy') and when to use the sibling tool ('ingredient-level product SKUs and substitutions'). It also specifies that it accepts an ingredient list or recipe_id from extract_recipe_ingredients, providing clear input guidance.

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

• extract_recipe_ingredientsA
  4.7/5.0
  Behavior4/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It explains the success/failure condition for YouTube URLs (requires yt-dlp), caching behavior via recipe_id, and the nature of the return value. It does not elaborate on edge cases like malformed transcripts or error handling, but covers the main dependencies and output.

  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 concise at 7 sentences. It front-loads the core purpose, then covers critical caveats, return value, ordering, use cases, and an example. Every sentence adds necessary information without redundancy.

  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 tool has 3 parameters with full schema descriptions, no output schema, and moderate complexity. The description compensates by listing returned fields (ingredients with name, quantity, unit, category; plus recipe name, equipment, technique tags). It also explains caching and downstream dependencies, making the tool's role and output clear for an agent.

  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 coverage is 100%, so baseline is 3. The description adds value beyond schema descriptions by explaining that transcript can be either raw text or a YouTube URL, that recipe_id is for caching and auto-derived if omitted, and by providing an example that illustrates parameter usage and return structure.

  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 extracts structured recipe data (recipe name, ingredients, equipment, technique tags) from transcript text or YouTube URL. It distinguishes from sibling tools (match_ingredients_to_products and suggest_affiliate_products) by positioning itself as the first step that populates cache for downstream use.

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

  Usage Guidelines5/5

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

  The description explicitly says 'Call this first' and explains the dependency between this tool and its siblings. It provides clear guidance on when to use raw transcript text versus YouTube URL, including the caveat about yt-dlp installation. Use cases (recipe monetization, shoppable recipe creation) are listed.

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

• match_ingredients_to_productsA
  4.7/5.0
  Behavior4/5

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

  No annotations provided, so description carries full burden. It discloses that commission rates are benchmark estimates (not live data), which is a key behavioral trait. However, it does not mention if any data is persisted or require authentication, missing some context.

  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?

  Description is concise (6 sentences), front-loaded with the core purpose, covers key aspects without redundancy, and includes a illustrative example.

  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?

  Moderately complex tool with no output schema; the description adequately explains the return values (affiliate details, price range, commission rate, substitutions) and provides an example, making it complete for an agent.

  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 coverage is 100% and description adds value by clarifying the mutual exclusivity of ingredients and recipe_id, and providing an example output snippet, going beyond the schema's descriptions.

  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?

  Description clearly states the tool matches recipe ingredients to purchasable products with affiliate details, and distinguishes itself from the sibling tool 'suggest_affiliate_products' by specifying its focus on ingredient-level details.

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

  Usage Guidelines5/5

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

  Explicitly says when to use (for ingredient-level product details) and when not (use suggest_affiliate_products for revenue-ranked shopping list), and notes that input can be an ingredient list or a recipe_id from a prior extraction.

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