foodose GLP-1 nutrition

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

Annotations already mark the tool readOnly, idempotent, and non-destructive; the description adds that it is educational only, not medical advice, and not a substitute for a clinician. It also discloses the output style ('full derivation with a PubMed citation per step'), which is important behavioral information. No contradiction with annotations exists.

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 block but efficiently front-loads the output and population, then covers output detail and disclaimers. Every sentence contributes (targets, usage, derivation, safety, contraindications). It is appropriately sized for a medical tool where disclaimers are necessary.

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 calculation tool with 11 parameters and no output schema, the description gives the essential context: what it computes, for whom, what it returns, and key exclusions. It does not describe the return structure in detail, but 'full derivation with a PubMed citation per step' indicates a detailed output. Given readOnly annotations and the absence of side effects, this is reasonably 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?

The schema describes 6 of 11 parameters (weightKg, heightCm, doseMg, goalWeightKg, startWeightKg, sex) with meaningful descriptions, but the main description adds no parameter-level meaning. Parameters like age, goal, activity, medication, and weeklyLossKg rely on enum/default values or bare names, and since schema coverage is only 55%, the description should have compensated. It doesn't, leaving the less obvious parameters under-specified.

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 the exact outputs (calorie, protein, carbohydrate, fat, fibre targets plus appetite ceiling) and the population (GLP-1 receptor agonist users). It contrasts itself with standard calorie calculations, which helps distinguish it from generic calculator tools. No ambiguity remains about what the tool produces or for whom.

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 says 'Use it for anyone taking or considering one of these medications' and lists exclusions (pregnancy, breastfeeding, insulin/sulfonylurea use, disordered eating history, under 18). It does not name sibling tools as alternatives, but the inclusion criteria and the contrast with standard calorie calculations imply the right context. It could be stronger by explicitly stating when to use get_appetite_ceiling instead, but the guidance is clear overall.

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?

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful behavioral context: it returns the factor, the supporting evidence, licensed doses, and the whole-table behavior when medication is omitted. It does not describe the output shape, but this is a simple read-only lookup.

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 with core semantics, then ends with a practical usage tip. Minor awkwardness around 'foodose models' and the phrase 'This is the site's original data' adds context without being bloated.

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 two-parameter, read-only tool with no output schema, the description explains what is returned, the semantic distinction from weight loss, and how to retrieve the full dataset. It could be more explicit about the response format, but nothing critical is missing for correct tool selection.

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 0%, so the description carries the burden of explaining the parameters. It explains that medication and dose select the factor, and that omitting medication returns the full table. It doesn't spell out doseMg units explicitly, though the property name and title make the meaning clear enough.

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 clearly states the resource and purpose: returning a foodose appetite suppression factor for a medication and dose, along with evidence and licensed doses. It also distinguishes this from weight-loss estimates ('rather than how much weight they lose'), which separates it from sibling calculate_glp1_target.

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 clear usage context: call with a specific medication and dose, or call with no medication to get the whole table. It does not explicitly name alternatives like calculate_glp1_target or search_foods, but the context is specific enough to avoid confusion.

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?

Annotations already declare readOnlyHint and idempotentHint, so the core safety profile is covered. The description adds meaningful behavioral context beyond annotations by specifying the per-100g normalization, realistic serving size, and the meaning of the GLP-1 tolerance rating, while its 'foods foodose uses' phrasing aligns with openWorldHint=false.

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-loads the most important datum ('Per 100 g nutrition') before describing serving size and tolerance. The licensing sentence 'Free to use with attribution' is extra but brief; overall every part is useful and there is no redundant 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?

With no output schema, the description does enough by explaining what fields to expect: nutrition per 100g, serving size, and GLP-1 tolerance rating. Annotations cover the read-only/idempotent behavior, and the schema covers query/tolerance plus limit boundaries, so an agent can invoke the tool without critical missing context.

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 descriptions cover query and tolerance, but limit and minProteinPer100g lack descriptions. The description adds value by clarifying tolerance semantics and explaining that nutrition is per 100g, which helps interpret minProteinPer100g, but it does not fully compensate for the undocumented parameters, so it is minimally adequate.

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 title supplies the explicit verb+resource ('Search the foodose food database'), and the description clarifies that the tool returns per-100g nutrition, serving sizes, and GLP-1 tolerance ratings for foods. This distinguishes it from search_recipes, though the description itself is a noun phrase rather than a direct action statement.

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 implies this is the tool for looking up food-level nutrition and GLP-1 tolerance data, especially versus search_recipes. However, it does not explicitly state when to use this tool instead of get_appetite_ceiling or calculate_glp1_target, nor does it offer any exclusion criteria.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context: results are published recipes, nutrition is per serving, and ingredients are USDA-traceable. It doesn't mention pagination, ordering, or that only published recipes are returned, but the annotations carry the main behavioral burden.

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?

Two sentences, no filler. The first sentence front-loads the core purpose and key data attributes; the second sentence gives usage context. Every clause earns its place.

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-only search tool with no output schema, the description covers the main things an agent needs: what is searched, what data is included, and when to use it. It doesn't describe result ordering, pagination, or how filters combine, but those are minor for a simple search tool with annotations already covering safety and idempotency.

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 only 17% (only 'query' has a description), so the description must compensate. It does add meaning by explaining that results include per-serving calories and protein, which maps to minProteinPerServing, and that ingredients are USDA-traceable, which relates to query matching. However, it doesn't explain diet/meal enum semantics, maxTotalMinutes, or limit behavior beyond what the schema already shows. Baseline 3 is appropriate because the description adds some but not full compensation for the low coverage.

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 ('Search'), a specific resource ('Published GLP-1 recipes'), and adds distinguishing detail: per-serving calories/protein and USDA FoodData Central traceability. It also contrasts with target-setting tools, so an agent can tell it apart from calculate_glp1_target and get_appetite_ceiling without opening the schema.

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: use it when someone asks what to actually eat, rather than what their targets are. It doesn't explicitly name sibling alternatives or say when not to use it, but the 'rather than' phrasing implies the boundary against target-calculation tools. It could be stronger with an explicit pointer to search_foods for ingredient-level lookups.

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