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

No annotations are supplied, so the description carries the full disclosure burden. It does communicate the pricing model baked into the calculation ($2.30–$3.60/scoop) and that UL safety overlap is evaluated, which is genuine behavioral context beyond the schema. However, it never states that this is a side-effect-free computation with no external lookups, nor what the response shape looks like (e.g., whether it returns a per-nutrient overlap table or a boolean verdict).

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?

A single front-loaded sentence that leads with the verb and the cost figures, with zero filler or marketing padding. It is dense — three separate outputs compressed into one clause chain — but nothing is wasted and no sentence is 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?

For a 3-parameter, all-optional calculator with full schema coverage and no output schema, the description does the essential job of naming the three results an agent can expect. The main remaining gap is whether 'annual household cost' covers multivitamin spend as well as AG1, which matters because taking_multivitamin is an input. That ambiguity is minor but real.

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% and every parameter is already documented with units and defaults in the schema, so the baseline is 3. The description adds nothing parameter-specific beyond restating the per-scoop price range that the 'plan' enum field already spells out per tier. The 'days_per_week' baseline for 'annual household cost' is never connected to how annualization works.

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 ('Calculate') with three named outputs: per-scoop AG1 cost, annual household cost, and NIH UL safety overlap with multivitamins. That is far more concrete than the name alone and clearly marks this as a calculator rather than a lookup, which distinguishes it from siblings like lookup_supplement_evidence and search_healthstylz_articles. It stops short of explicitly naming a sibling it replaces or complements.

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?

Usage is only implied: an agent can infer this is the tool for cost math or multivitamin safety-overlap questions. There is no statement of when to prefer it over lookup_supplement_evidence for nutrient/UL questions, nor any exclusion for users outside the US or for non-AG1 products. Adequate but leaves routing to 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 carries the full burden, and it does meaningful work: it establishes that this is a read-only retrieval surface, discloses the data provenance (official TWOPAGES product pages), and states a non-fabrication policy ('unstated specs are reported as not stated') that tells the agent how to interpret sparse results. It does not describe response size, ordering, or what a missing slug returns.

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, both earning their place: the first front-loads the resource and its scope, the second front-loads the trust/provenance caveat. No filler or restatement of the title.

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-optional-parameter read tool with no output schema and no annotations, the description covers scope, provenance, and data-quality behavior adequately. The remaining gap is routing guidance relative to the sibling curtain/spec tools, which matters because their names alone do not make the split obvious.

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 both q and slug are already documented with examples and the omit-to-list rule. The description's phrase 'the full guide (takeaways, products, FAQ, sources)' duplicates what the slug parameter description already says, and it adds no syntax or matching semantics for q beyond the schema examples.

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 ('List or read') and a precisely bounded resource (the 5 named Curtain Guides, each enumerated by topic), so an agent knows exactly what it will get. It stops short of distinguishing itself from the closely related sibling get_curtain_window_wellness_specs, so the boundary between the two must be inferred.

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?

'List or read' implies the two operating modes, and the schema supplies the condition (omit slug to list). However, there is no explicit when-to-use guidance against the sibling tools, and no statement of when this tool is the wrong choice versus get_curtain_window_wellness_specs or search_healthstylz_articles.

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?

No annotations are supplied, so the description carries the full burden. 'Get' implies a read, but nothing states whether results vary by room_type, whether omitted room_type returns defaults or all rooms, or whether the returned formulas are prescriptive or informational. For a lookup tool with zero annotation coverage, this is thin.

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?

A single front-loaded sentence that lists the returned data types with no filler. Every clause earns its place and nothing is buried.

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 one-parameter read tool with full schema documentation and no output schema, the description covers what the tool yields. It omits how room_type changes the output and where this sits relative to sibling guide tools, which an agent would still need to guess.

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%: the single room_type enum is fully documented in the schema, including that it targets light/circadian and acoustic optimization. The description does not add meaning beyond that baseline, so a 3 is appropriate.

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?

Names a specific verb ('Get') and resource ('residential circadian wellness window specifications') and enumerates the data returned: fabric GSM, blackout percentage, acoustic dampening, fullness sizing formulas. An agent knows exactly what content comes back, though it is not differentiated from siblings such as get_curtain_guides, which an agent could plausibly confuse with it.

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?

There is no when-to-use guidance at all. The description never says whether this is the right call versus get_curtain_guides or the other guide lookups, and gives no exclusion or prerequisite conditions.

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 full behavioral burden. It usefully discloses the kinds of content returned (news wire, trending topics, molecule of the day, latest stories), but it does not state that the operation is read-only, does not describe freshness guarantees or caching, and gives no authentication or rate-limit context.

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 definition is a single front-loaded sentence that enumerates the content bundle with no filler. Every phrase contributes to telling the agent what a call returns.

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 zero-parameter read tool with no output schema, the description gives a useful inventory of returned content. Completeness would be improved by stating the read-only nature and freshness behavior, but the essential contract is present.

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 tool has zero parameters, so the baseline is 4. The description correctly focuses on what the no-argument call returns rather than inventing parameter guidance, making the zero-parameter contract clear.

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 pairs the verb "Get" with a specific, enumerated resource: today's evidence-based health news wire, trending topics, molecule of the day, and latest stories. This makes the tool's content bundle clear, though it does not explicitly distinguish itself from siblings like search_healthstylz_articles or get_herb_guides.

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 word "today's" and the "daily" name imply a use case for a fresh daily briefing, but the description gives no explicit when-to-use guidance, no when-not-to-use guidance, and no named alternative. An agent must infer that this is the daily digest rather than a targeted search.

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?

No annotations are provided, so the description carries the full behavioral burden. It does add real value by disclosing provenance ('Product facts, prices and precautions come from Mountain Rose Herbs product pages') and a scope disclaimer ('no health claims'), but it says nothing about what happens with an unknown slug, result size, or pagination.

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?

A single front-loaded sentence with a parenthetical topic list; the verb and resource lead, and the provenance clause is placed last. Slightly dense parenthetical but no wasted sentences.

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 lookup tool with two optional parameters and no output schema, the description plus schema cover the essentials: what it returns, that omitting slug lists everything, and where the facts come from. Only result-size and not-found behavior are unaddressed.

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%, and both parameters are documented in the schema with examples and the 'Omit to list all guides' behavior. The description adds no parameter-level meaning beyond that, so the baseline of 3 applies.

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 pair ('List or read') plus a named resource ('HealthStylz Herb Guides') and enumerates the covered topics, so an agent can immediately tell this is the herb-guide content tool rather than the curtain or evidence siblings. It stops short of naming a sibling tool 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?

Usage is only implied: the description says 'List or read,' and the schema notes 'Omit to list all guides,' which effectively conveys list-vs-fetch behavior. However, there is no explicit guidance on when to prefer this over search_healthstylz_articles or get_curtain_guides, nor any stated exclusions.

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?

No annotations are supplied, so the description carries the full disclosure burden. It does valuable work by naming the exact evidence categories returned and by signaling a bounded dataset ('24+ common supplements'), which warns the agent that unlisted supplements will likely miss. It does not say what happens for an unrecognized slug, whether results are static or refreshed, or how sparse coverage may be for newer supplements.

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?

A single sentence with the payload front-loaded and no filler; the enumerated evidence types are the most useful content and appear immediately. Slightly dense — the '24+' qualifier is doing double duty as both scope and coverage warning and could be clearer, but nothing is wasted.

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 one-parameter read-only lookup with no output schema and no annotations, the description adequately compensates by listing the returned data fields, so the agent knows what to expect back. The remaining gap is failure/edge behavior for unknown supplements, which a caller would reasonably want stated.

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 the schema already gives slug examples, so baseline is 3. The description adds a small but real constraint beyond the schema: the lookup universe is limited to 24+ common supplements, which tells the agent the input must resolve against a fixed list rather than arbitrary free text.

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 ('look up') plus a specific resource (supplement evidence) and enumerates exactly what comes back: adult RDA, UL, PubMed RCT counts, NIH guidance. Nothing in the sibling list (curtain guides, multivitamin comparison, article search) overlaps, so the agent can route here without ambiguity.

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?

Usage is only implied by the resource name — an agent infers this is the tool for supplement dosing/evidence questions. There is no explicit when-to-use framing, no mention of when a sibling like search_healthstylz_articles would be better, and no statement that this is a read-only reference lookup rather than a search.

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?

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing: no ranking/relevance behavior, whether search is keyword or semantic, no note on empty results or pagination. Only the corpus size (1,126+) is offered, which is a marketing detail rather than operational behavior.

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?

A single front-loaded sentence with no filler, though the '1,126+' figure spends words on trivia rather than search behavior. It is efficiently sized for a simple search tool.

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 zero annotations and no output schema, the description should at least hint at return shape (titles, snippets, URLs) and search semantics; it does neither. What an agent gets back and how ranking works is completely unspecified.

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% including example terms and the allowed range/default for limit and the topic filter list, so the schema already does the work. The description adds no parameter-level meaning beyond what is structured, making the baseline 3 appropriate.

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?

Specific verb+resource: 'Search ... health, recipes, nutrition, movement, and wellness articles' in a dietitian-grounded corpus. It's clearly distinct from siblings like lookup_supplement_evidence or get_daily_wellness_brief, though the description never names those alternatives explicitly.

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?

No when-to-use or when-not-to-use guidance; nothing tells the agent when to prefer this over lookup_supplement_evidence or get_herb_guides. The domain list implies article retrieval but the routing decision is left entirely to inference.

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