nutrition

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

With no annotations provided, the description carries the full burden of disclosing behavior. It clearly states the tool returns complete recipe details, that nutrition is computed from ingredient food rows rather than hand-entered, and that each figure traces back to national datasets cited by lookup_food. This adds meaningful transparency about data provenance and the no-cook constraint, going beyond just describing inputs and outputs. Minor gap: it doesn't explicitly state whether the tool creates, modifies, or has side effects, but the read-only nature is strongly implied.

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 compact and front-loaded: the first sentence delivers the core purpose and components, the second adds nutritional data provenance, and the third clarifies input format and the search alternative. Every sentence earns its place without redundant repetition of the tool title or input schema. It is informative yet efficient.

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 only one parameter, full schema coverage, and no output schema, the description provides all necessary context for correct invocation: what the tool returns, how to specify the recipe, and how to find one if only a name fragment is known. The context about data provenance and raw/vegan constraints is sufficient for an agent to decide when and how to use this tool. No missing critical information is evident.

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?

Schema description coverage is 100%, so the schema already documents the single parameter ('recipe') with an example value. The description adds extra meaning by explaining that the parameter accepts a slug OR a recipe name, and gives a concrete example format. This goes beyond the schema's basic 'Recipe slug or name' explanation, reinforcing the valid input format for correct invocation.

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 states a specific verb ('Get'), a specific resource ('one Heathy recipe in full'), and enumerates exactly what is included: ingredients with food slugs and gram weights, method, per-serving nutrition, allergens, and carbon footprint. It also distinguishes itself from siblings by noting that search_recipes is used to find recipes. This is clearly differentiated from other tools like search_recipes, lookup_food, and food/nutrient lookup tools.

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 explicitly states when to use this tool: when you need a full recipe by slug or name, and it explicitly mentions using search_recipes to find one. It clarifies that all recipes are raw/no-cook and vegan, which helps an agent decide if this tool is appropriate given user constraints. It also implies the alternative path (search_recipes) without needing inference.

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, the description takes the full burden and delivers: it discloses that condition names resolve despite physiological titles, that diagnosis-related topics contain evidence-rating notes, and that ADHD/autism notes explicitly state diet is not a treatment per NICE. It also instructs to reproduce notes verbatim rather than summarize, a critical behavioral trait.

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 a single dense paragraph that front-loads the core function and then adds necessary nuances about condition resolution and evidence notes. Every sentence contributes value, though the paragraph could be slightly restructured for scannability. It remains appropriately sized for the complexity.

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?

For a tool with no output schema, the description explains what results will contain (nutrients, reasons, raw recipes, evidence notes) and how to handle them. It covers goal and condition inputs, omission behavior, and the critical note-reproduction rule. A minor gap is that it doesn't explicitly describe the shape of the output list, but the essential calling context is 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?

Schema coverage is 100%, so the baseline is 3. The description adds real semantics beyond the schema: it clarifies that the topic parameter accepts goals or named conditions, gives examples, states how condition resolution works, and explains the effect of omitting the argument. It does not add detail on include_recipes beyond the schema, but the extra semantic richness justifies a 4.

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 states exactly what the tool does: 'What to eat for a health goal — the nutrients involved, why each one, and which raw recipes carry them.' It names a specific resource (health topics), a clear verb ('eat for'), and explains how condition names resolve, distinguishing it from generic recipe or food lookup tools.

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 gives an explicit trigger: 'Call this before answering "what should I eat for X".' It also specifies the omission behavior for listing all topics. It does not explicitly contrast with sibling tools like search_recipes or lookup_food, but the use-case framing makes the intended context clear.

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 burden of behavioral disclosure, and it does so well. It reveals the mandatory safety block, the evidence-tier convention, and the requirement to reproduce safety information. It could add explicit read-only confirmation or mention behavior for unknown herbs, but the disclosed traits are substantive and useful.

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 dense but every clause earns its place: purpose, traditional/modern separation, evidence tier, mandatory safety block, reproduction obligation, and accepted name forms. It is front-loaded with the core purpose and does not include filler or redundant schema repetition.

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 there is no output schema and no annotations, the description provides a remarkably complete picture. It explains the structure of the response, the evidence grading, the mandatory safety block, and the required downstream behavior. An agent has enough guidance to invoke the tool and use its output responsibly.

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?

Schema coverage is 100% for the single 'herb' parameter, so the baseline is 3, but the description adds real value beyond the schema. It explains accepted name types (English, Sanskrit, Hindi, botanical) and gives concrete examples, making the parameter much easier to populate correctly.

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 opens with a specific verb and resource: 'Get a sourced monograph for an Ayurvedic herb.' It clearly distinguishes this tool from food/recipe/health-topic siblings by focusing on herb-specific monographs, and it clarifies the core behavior of keeping traditional use and modern research strictly separate.

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 gives clear context for when to use the tool: whenever a sourced Ayurvedic herb monograph is needed, with traditional and modern evidence separated. It does not explicitly name alternative tools or state when not to use it, but the unique purpose and accepted name types make the appropriate use case unambiguous.

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 of behavioral disclosure. It explains that values are aggregated across up to 11 datasets, reports per-source contribution and spread, and covers IFCT 2017. This goes well beyond a generic lookup description, though it does not describe error handling or exact return formatting.

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 compact and front-loaded. The first sentence states the core action and scope, the second explains the key differentiator, and the third covers input flexibility and unique coverage. Every sentence contributes necessary information without redundancy.

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?

For a tool with no output schema and no annotations, the description does well by explaining what data will be returned (nutrient profile, source counts, spread) and how inputs are accepted. It does not specify the exact response structure or behavior for unknown foods, but what is present is sufficient for correct selection and invocation in most cases.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful value by clarifying accepted input formats for 'food' with examples ('bitter-gourd', 'karela', 'chickoo') and explaining that omitting 'nutrients' returns the full ~168-nutrient profile. These details help an agent construct valid calls more confidently.

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 opens with a specific verb and resource: 'Get the full per-100 g nutrient profile of a whole plant food.' It also differentiates the tool from siblings by emphasizing multi-dataset reconciliation and Indian-food coverage, so an agent can distinguish it from search_foods, nutrient_ranking, and get_recipe.

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 this tool: when a detailed, source-reconciled nutrient profile is needed, including regional Indian foods not available elsewhere. It does not explicitly name alternatives or state when not to use it, but the specialization and input format guidance make the intended use clear.

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, the description carries the behavioral burden and does well: it specifies per-100 g normalization, whole-plant-food scope, reconciled multi-source data, and the fact that each result reports source count. It also mentions min_sources as a way to spot or exclude thinly-sourced outliers, which is useful behavioral detail.

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?

Three sentences, each earning its place: the first defines the core ranking behavior and data provenance, the second covers filtering, the third explains the source-count output and min_sources usage. No filler or repetition.

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?

Although there is no output schema, the description covers the primary return characteristic (source backing count) and the ranking basis. It does not state the sort order explicitly or enumerate all output fields, but for a four-parameter tool the description is sufficiently complete for correct invocation.

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?

Schema coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema by explaining the multi-source ranking basis, the per-100 g meaning, and how min_sources relates to spotting or excluding poorly-backed values.

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 uses a specific verb+resource pair: 'Rank whole plant foods by how much of a nutrient they provide per 100 g.' It clearly differentiates itself from siblings like search_foods or lookup_food by framing the 'highest in vitamin C / iron / calcium' use case rather than generic search or lookup.

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 communicates when to use the tool: for nutrient-ranking questions with optional category filtering and source-threshold exclusion. It does not explicitly name alternatives or state when not to use it, but the intended context is clear enough for an agent to select it appropriately.

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?

Since no annotations are provided, the description carries full behavioral disclosure. It states a key behavioral trait: 'Returns ranked candidates rather than guessing a single answer,' which informs the agent that the result is a list of possibilities, not a definitive resolution. It also hints at alias coverage, but does not detail edge cases like no-match behavior or result fields.

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 three sentences, each earning its place: the first defines the tool's purpose, the second gives workflow guidance, and the third discloses ranking behavior. It is front-loaded with the most important information and contains no filler.

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 no output schema, the description adequately explains the return nature ('ranked candidates') and the intended downstream use, which is enough for an agent to call the tool correctly. It does not fully specify result fields or error behavior, but the search tool is simple and the schema already covers parameters.

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?

Schema description coverage is 100%, so the baseline is 3. The description adds contextual value by clarifying what the query should contain (plain-English or local names, regional/non-English aliases) and the purpose of the result (producing a slug), but it does not provide syntax-level detail beyond the 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 names a specific verb ('Find'), a concrete output (the Heathy slug), and a clear scope (foods and Ayurvedic herbs by name, including aliases). It also distinguishes itself from sibling tools by explicitly mentioning that lookup_food and herb_monograph are the downstream consumers of the slug, which prevents confusion with search_recipes or nutrient_ranking.

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 gives an explicit 'when to use' directive: 'Call this first when you have a plain-English or local name and need the slug for lookup_food or herb_monograph.' This is strong context, though it does not mention exclusions or alternatives beyond implicitly positioning this as the lookup tool for food/herb slugs.

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?

There are no annotations, so the description carries the behavioral burden. It discloses that recipes are original, that nutrition figures derive from the same food rows lookup_food returns and are therefore checkable, and that results are ranked summaries rather than full recipes. It doesn't specify ranking order or pagination, but the key consumer expectations are stated.

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 front-loaded with the core purpose and every sentence earns its place: search scope, data provenance, result granularity/next step, and alternative routing. It is dense but not bloated, and the most important operational information comes 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?

For a read tool with 7 optional parameters, no annotations, and no output schema, the description is largely complete: it defines the object type, explains result granularity, points to get_recipe for details, and warns when health_topic is more appropriate. A small gap is that 'ranked summaries' doesn't convey exactly what fields appear or how ranking is determined, but this is not fatal given the follow-up pointer.

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?

Schema description coverage is 100%, so the baseline is 3. The description enumerates the facets ('free text, health goal, category, ingredient, time or allergen') that map to query, goal, category, ingredient, max_time, and exclude_allergens, but it adds no constraints, formats, or meaning beyond what the schema already provides. The schema's examples are richer than the description on parameter usage.

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 uses a specific verb ('Find') and resource ('raw / no-cook vegan recipes'), and clearly identifies the search facets: free text, health goal, category, ingredient, time, and allergen. It also contrasts itself with get_recipe (summaries vs full details) and health_topic, making sibling differentiation explicit.

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?

It gives explicit routing guidance: for 'what should I eat for X' questions, prefer health_topic first; for full recipe details, call get_recipe with a slug. This is clear when-to-use and alternative guidance, not just implied context.

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