Server Quality Checklist

Tool Scores

• browse_recipesA
  4.4/5.0
  Behavior5/5

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

  Annotations already mark this as read-only, idempotent, and non-destructive, but the description adds substantial behavioral detail: hand-built slugs return a page the taxonomy does not hold, 'page_served' must be trusted over the requested page, 'has_more' drives pagination, and 'total_available' is always null. This goes well beyond the annotations and significantly improves call correctness.

  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 compact and front-loaded with the core purpose, then each subsequent sentence adds a distinct, useful caveat or behavior. There is no filler or redundant restatement of the tool name or title.

  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?

  Given the tool's complexity and the presence of an output schema, the description covers all necessary call-related quirks: how to source valid slugs, what to trust in the response, why a count is missing, and the impossibility of combining taxonomies. The agent has enough context to invoke it correctly.

  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 the baseline is 3. The description reinforces that 'value' must be a slug from list_facets and adds the caveat that made-up slugs receive an unexpected page, which adds meaning beyond the schema's simple examples. It does not add new information about the 'page' parameter beyond what the schema already states.

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

  Purpose4/5

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

  The description opens with 'Browse the recipes a taxonomy holds, one page at a time,' which is a specific verb and resource. It clearly conveys the paginated browsing scope, though it does not explicitly contrast with sibling tools like search_recipes or get_recipe, so it stops short of a 5.

  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 operational context: 'value' must come from list_facets, the site writes its own slugs, and taxonomies cannot be combined. It does not explicitly state when to prefer this tool over its siblings, but the browsing-oriented language and pagination details imply the intended use case.

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

• search_recipesA
  4.5/5.0
  Behavior4/5

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

  Annotations already declare readOnly, openWorld, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond annotations: the one-page limit, the null total_available, the ordering not being relevance-based, and the possibility of collection rows. However, it does not fully elaborate on the output schema, but the output schema exists, reducing the burden.

  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 but well-organized: it front-loads the core search behavior, then explains edge cases (ordering, collections) and alternatives. Every sentence adds information, though a few could be tighter. The length is justified by the complexity of the site's behavior.

  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 site's unusual behavior (no pagination, null total, non-relevance ordering, collection items), the description covers these critical quirks thoroughly. It also redirects to sibling tools for filtering. The output schema exists, so return value details are covered elsewhere. Minor gap: no explicit mention of error cases, but overall complete for confident invocation.

  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 both parameters are already described in the schema. The description reinforces query semantics (Spanish, dish/ingredient/technique) and explains the effect of limit in the context of the site's ordering. This adds meaningful context beyond the schema fields, justifying a score above baseline.

  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 searches Pequerecetas for a dish or ingredient in Spanish, and explicitly distinguishes it from sibling tools like get_recipe, list_facets, and browse_recipes. The verb 'search' plus the target resource and language specificity make the purpose unambiguous.

  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 explains when to use this tool vs alternatives: it notes that search takes no filters and recommends list_facets and browse_recipes for narrowing by diet, ingredient, technique, or age. It also clarifies the lack of pagination and result counting, which guides proper invocation.

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

• get_recipeA
  4.6/5.0
  Behavior5/5

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

  Beyond the annotations (read-only, idempotent), the description discloses significant behaviors: the response 'kind' can be 'recipe' or 'collection', 'source_shape' indicates whether ingredients came from structured data or article text, rescaling is skipped without a stated serving size, and tool-lines are never multiplied. These details greatly exceed the annotation metadata.

  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 a single dense paragraph but logically proceeds through key facts: what it reads, the 'kind' distinction, 'source_shape' meanings, servings behavior, and the tool-line rule. Sentences are purposeful though some are long and could be broken up; overall it earns its length.

  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?

  Given the output schema exists, the description does not need to explain return types. It thoroughly covers the non-obvious behaviors needed to interpret the result correctly (kind, source_shape, servings fallback, tool-line handling). An agent has enough context to invoke the tool and understand the response without surprises.

  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 the baseline is 3. The description adds value by giving a concrete slug example and explaining important edge-case behavior for 'servings' (e.g., unscaled response when the page states no servings, and marking tool lines rather than multiplying). This is meaningful beyond the schema's own parameter 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 uses a specific verb 'Read' and identifies the resource as 'one page of the recipe section', clearly distinguishing this from siblings like search_recipes or browse_recipes by requiring a slug. The inclusion of an example slug ('paella-de-marisco') makes the target concrete.

  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 clearly implies when to use this tool: when you have a slug for a specific recipe page and need its details, as opposed to searching or browsing. However, it does not explicitly name alternative tools or state when not to use it, so it stops short of full routing guidance.

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

• list_facetsA
  4.7/5.0
  Behavior4/5

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

  Annotations already establish that this is read-only, idempotent, open-world, and non-destructive. The description adds useful behavioral context beyond that, especially that omitting facet triggers eight underlying requests, one per taxonomy. No contradiction with annotations.

  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 front-loaded with the tool's purpose in the first sentence, followed by a compact list of taxonomies and then actionable usage guidance. Every sentence adds useful information with no 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?

  The tool has a single optional parameter, a comprehensive schema, an output schema, and annotations covering safety and world behavior. The description fills the remaining practical gaps: how to call it, why to call it before browse_recipes, and the request-cost implication of omitting the facet parameter.

  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 schema already documents the facet parameter and its allowed values, so the baseline is 3. The description adds human-readable meanings for each taxonomy and the important distinction that passing facet returns one taxonomy while omitting it fetches all eight, with a note about request cost.

  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 a specific operation: listing the taxonomic values the site uses to browse recipes. It also names all eight taxonomies and ties itself to browse_recipes, so an agent can distinguish it from sibling tools like get_recipe or search_recipes.

  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 directs when to call this tool: 'Call this before browse_recipes rather than spelling a value yourself.' This gives a clear use case and a reason to prefer it over hard-codng values, and the optional facet parameter behavior is fully explained.

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

• scale_ingredientsA
  4.7/5.0
  Behavior5/5

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

  The annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior. The description adds rich behavioral detail beyond that: it explains the three possible per-line outcomes ('scaled', 'rounded', 'unscaled'), states that no unit conversion occurs, preserves approximate measures like 'pizca', and marks equipment lines as 'is_equipment'. This gives the agent a clear mental model of what the tool actually does at the line level.

  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 compact and information-dense. The first sentence states the core action and scope, the second explains the parameter grouping, and subsequent sentences explain output behavior and edge cases. Every sentence earns its place with no repetition or 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?

  Given the tool's moderate complexity, the presence of a complete input schema, rich annotations, and an output schema (per context signals), the description covers all essential non-schema semantics: parameter combos, per-line statuses, equipment handling, unit behavior, and offline execution. Nothing critical 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 input schema already documents each parameter with a description, so baseline is 3. The description adds a critical constraint not fully explicit in the schema: the mutual exclusivity/grouping of 'factor' versus 'from_servings' and 'to_servings' together. This is exactly the kind of semantic that helps an agent build a valid request.

  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 a specific operation: 'Rescale a list of Spanish ingredient lines, offline.' It names the resource (ingredient lines) and the verb (rescale), and the sibling tools are all about retrieving or browsing recipes, so there is no ambiguity about which tool to pick.

  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 direct usage instructions: 'Give either factor, or from_servings and to_servings together.' This tells the agent how to invoke the tool correctly. It doesn't explicitly say when not to use it versus siblings, but sibling names make it obvious that this is the only rescaling option, so the guidance is sufficient.

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