MALAI GLP-1 Nutrition (no account)

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

Beyond the read-only and idempotent annotations, it discloses meaningful calculation behavior: a calorie floor, a cap on the rate of loss, adult-only applicability, and parity with the MALAI app's calculator. It does not provide the numeric safety thresholds or formula, but the schema also explains that loss is capped and the applied rate is returned.

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 opening sentence front-loads the purpose, outputs, principal inputs, and safety behavior. The remaining sentences efficiently add app parity, concrete use cases, an eligibility restriction, and sibling routing without digression.

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 nine-parameter calculator, the definition combines clear scope, example requests, safety constraints, and an explicit alternative with a fully described input schema and an output schema. Together with the safety annotations, this gives an agent enough context to select and invoke it correctly without requiring formula or return-value details in the description.

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% and already documents units, defaults, enum meanings, height conversion, and rate capping in detail. The description usefully summarizes the major inputs and illustrates a weekly-loss request, but adds little parameter-level information beyond that comprehensive 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 uses a specific verb and resource: estimating daily calories, protein, carbs, fat, and fiber from stated physiological and goal inputs. It also distinguishes calculated targets from a signed-in user's saved targets in malai_get_nutrition_targets.

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?

Concrete example requests show when to use it, while the adults-only restriction establishes eligibility. The explicit exclusion for saved targets, including the correct sibling tool, provides clear when-not and alternative guidance.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), so the description's job is added context, and it delivers: the formula's source ('2025 joint advisory'), the range derivation from body weight, and that results are split into per-meal amounts matching MALAI's calculator. It stops short of noting the GLP-1 framing's limits or rounding behavior, but the methodology disclosure is well beyond the annotations.

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?

It is front-loaded with the verb, formula, and output shape in a single dense sentence, followed by example triggers and one exclusion clause. Every element earns its place; nothing is repeated from the schema or annotations.

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 an output schema present, the description needn't explain return values, and the annotations fully carry the safety profile. The remaining gaps for a two-required-param calculator – method, scope, unit handling implied by the 210 lb example, and sibling routing – are all covered.

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 schema already documents weight, weight_unit, and meals_per_day with enum and bounds. The description reinforces that weight drives the g/kg computation and that meals_per_day spreads intake per meal, but adds no syntax, default value, or edge-case meaning 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 gives a specific verb+resource ('Calculate a daily protein range') and states the exact method (1.2–1.6 g/kg, 2025 joint advisory, split per meal). It explicitly names the sibling it is not (malai_get_nutrition_targets), so an agent can separate it from the other nutrition tools without opening a 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?

It supplies concrete trigger utterances ('how much protein should I eat on Zepbound', 'I weigh 210 lb, what's my protein target') and an explicit exclusion routing signed-in saved targets to malai_get_nutrition_targets. Both the when and the when-not are stated outright.

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, non-destructive and closed-world, so the safety profile is covered. The description adds real behavioral context beyond that: it cites prescribing information as the source and enforces a clinical boundary against dose-change advice. It does not restate return shape, which is acceptable given an output schema 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?

Purpose is front-loaded in the first clause, followed by content coverage and then the safety exclusion in the position of highest emphasis. The content list is dense but each element maps to a real section in the schema, so nothing is filler; only minor trimming would be possible.

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?

An output schema exists, so the description need not explain return values, and both parameters are fully documented in the schema. For a read-only guide lookup, nothing an agent needs in order to select and call this tool correctly is missing.

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, but the description goes further by spelling out what the section content actually contains (dose steps, side effects with trial frequencies, food tips), which gives meaning to the sections enum beyond bare labels. It also names the accepted brand values in prose, reinforcing the enum.

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?

States a specific verb and resource ('Get MALAI's guide to a GLP-1 medication') and names the four supported brands, then enumerates the concrete content delivered: dose steps, side effects with trial frequencies, food tips, phases, first week, FAQs. This is clearly distinguishable from the sibling tools, which are all nutrition logging/calculation utilities.

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?

Provides explicit positive triggers as quoted user phrasings ('what are Mounjaro side effects', 'what should I eat in my first week on Wegovy') and an explicit exclusion: 'Never use it to recommend a dose change, skipping or stopping', with the redirect to the prescriber. Both when-to-use and when-not-to-use are stated.

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 readOnly, idempotent, non-destructive, and closed-world, so the safety profile is fully covered without the text. The description adds the useful behavioral fact that this is an explanatory/instructional response scoped to when account-linked tools are absent, but says nothing about response format or what the explanation includes beyond the bullet list of benefits. Adequate but not rich.

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, with the scope and the capability list front-loaded ahead of the usage condition, and no filler. The parenthetical enumeration is dense but each item is informative rather than redundant.

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?

This is a low-complexity, zero-required-parameter instructional tool with full schema coverage and an output schema, so the description needn't explain return values. What an agent needs in order to call it correctly — purpose, trigger, precondition, and per-assistant tailoring hint — is essentially present, with only the explicit alternative routing left implicit.

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?

There is a single optional parameter ("assistant") with 100% schema description coverage and a closed enum, so the schema already explains it fully. The description does not mention that steps are tailored per assistant, adding no meaning beyond the schema. Baseline 3 applies when the schema does the work.

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 ("Explain what connecting a MALAI account adds ... and how to connect it") and parenthetically enumerates the concrete capabilities unlocked, so an agent knows this is an onboarding/explanation tool rather than a data query. It is implicitly distinguished from the sibling calculators and search tools by the phrase "while only the open MALAI tools are available," though it never names a sibling directly.

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?

"Use when the user wants to log food, track progress, or ask about their own numbers while only the open MALAI tools are available" gives a clear triggering condition plus a gating precondition (limited toolset). It stops short of explicitly naming the alternative path (e.g., use malai_search_foods once connected), so it is strong context rather than full routing.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed-world), and the description goes beyond them with two genuinely useful traits: the dataset is US-foods-only USDA data, and a null means unknown rather than zero. It does not mention result caps or what happens when a food is not found, leaving the remaining behavioral surface to the output schema.

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?

Front-loaded with the tool's identity and scope, then the nutrient list, then triggers, then the exclusion — a sensible order with no filler. It is dense, packing four clauses into the first sentence, but every clause carries information.

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 tool with an output schema and full annotations, the description covers what to query, what comes back, the data source and its US scope, and null semantics. An agent can call it correctly without inspecting anything else.

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 both params (query, limit) are fully documented there and the baseline is 3. The description notes the per-serving vs per-100 g basis and the protein-calorie share, but those are output semantics rather than added meaning for the inputs.

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?

States a specific verb and resource ("look up nutrition for a food") plus the backing source (USDA FoodData Central, US foods) and the exact nutrient set returned. It is unmistakably distinct from the logging and calculation siblings.

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?

Gives three concrete trigger phrasings ("how much protein is in cottage cheese", "compare tofu and chicken", "is a turkey sandwich high in protein") and one explicit exclusion ("Don't use to log food"). The exclusion, however, routes to malai_how_to_connect rather than naming malai_log_food, which is the actual sibling an agent would otherwise reach for.

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