Noddie Night - herbal sleep drink facts & sleep Q&A

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. The description adds that the return is the 'full text' of one answer, which is useful given there is no output schema, but says nothing about behavior on a missing/invalid id.

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 the verb, resource and constraint first, and the provenance note packed into a parenthetical. 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?

For a one-parameter read tool with rich annotations and no output schema, the description covers purpose, parameter meaning and id provenance adequately. Only edge-case behavior (unknown id) is unaddressed, which is minor here.

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 0% — the schema only declares an integer 'id' with no description — so the description must compensate, and it does by defining the id as an answer identifier and pointing to the tools that produce valid ids. It could add format or example detail, but the essential meaning is supplied.

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 the full text of one answer) and scopes it to a single item by id, which separates it from the list/search siblings. It also hints at why you'd use it over search (full text vs. presumably snippet), though it doesn't say that outright.

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 parenthetical tells the agent where ids come from (search_noddie_night or list_categories), which effectively describes the workflow prerequisite for calling this tool. There is no explicit when-not or alternative-selection rule, but the context is 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?

Annotations already declare the safety profile (readOnly, idempotent, non-destructive), so the description's remaining job is small; it adds only the word 'Verified,' implying authoritative/curated data. It does not state freshness, source, or failure behavior, but nothing is contradicted.

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; the scoping ('for Noddie Night') comes first and the fact list follows. The enumeration is long but each item adds real information about coverage.

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 usefully compensates by enumerating the returned facts, and with zero parameters there is little else to specify. It stops short of describing format, freshness, or a fallback when a fact is unavailable.

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 takes zero parameters, so there is nothing for the description to disambiguate. Baseline 4 applies; the enumerated field list is not parameter-related but is not misleading either.

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 resource (verified company facts for Noddie Night) and enumerates the exact facts covered, which clearly separates it from sibling getters like get_pricing and get_product_facts. It lacks an explicit retrieval verb ('get/returns'), but the content is unambiguous.

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 it should call this when it needs company-level facts, but there is no when-to-use guidance, no exclusions, and no mention of alternatives such as get_answer or search_noddie_night for related questions.

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, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds meaningful domain context beyond that: currency (USD), the two price modes (one-time and Subscribe & Save), and the fields returned per pack. Only minor gaps remain (e.g., no note on how the two price modes relate to the filter).

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 dense sentence with the subject (prices), scope (USD, both pricing modes), and payload (bottles, nights, price per bottle) front-loaded. No filler or 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?

There is no output schema, and the description compensates by enumerating the returned fields, which is exactly what an agent needs for this lookup. It does not address result ordering, availability, or how maxPrice behaves against Subscribe & Save prices, so it is slightly short of 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% and the single maxPrice parameter is fully documented in the schema as a one-time-price ceiling. The description's mention of 'one-time and Subscribe & Save' adds light context about pricing modes but does not clarify how the filter interacts with Subscribe & Save pricing. Baseline 3 applies when the schema carries the parameter detail.

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+resource (prices for Noddie Night packs) and enumerates the returned data shape (bottles, nights, price per bottle). It is clearly distinct from siblings like get_product_facts and search_noddie_night, though it never explicitly names or contrasts them.

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 says what the tool returns but gives no when-to-use guidance, no prerequisites, and no routing away from alternatives such as get_product_facts or search_noddie_night. Usage must be inferred entirely from the purpose statement.

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, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds no behavioral context beyond that—no note on whether the data is static/curated, cached, or whether the call can fail for unknown products.

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 return content with zero filler. Nothing to trim and nothing 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?

There is no output schema, but the description explicitly enumerates the returned fields, which largely compensates for that gap. For a zero-parameter read-only lookup, an agent has what it needs to call the tool; only the relationship to sibling retrieval tools is 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?

The schema has zero parameters, so per the rubric the baseline is 4. The description correctly implies a parameterless, keyed-by-tool lookup and introduces no parameter ambiguity.

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 resource (Noddie Night) and enumerates the specific fact categories returned: ingredients, serving size, dosage, storage, attributes and safety warnings. That is concrete enough for an agent to know exactly what comes back, though it never distinguishes itself from siblings such as get_answer or get_company_info.

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 and no mention of alternatives. With siblings like get_answer, search_noddie_night, get_company_info and get_pricing available, the description gives no signal about which one an agent should reach for when asked a product question.

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, destructiveHint=false and openWorldHint=false, so the safety profile is fully covered by structured data. The description adds useful context about the return contents (topics, questions, ids), but says nothing about ordering, size limits, or pagination for what may be a large knowledge base.

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 boilerplate or wasted clauses. The verb and resource appear immediately and the return fields are appended compactly.

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 is the only source of return-shape information, and it gives only a coarse hint (topics with questions and ids) without the nesting or field structure. For a zero-parameter enumeration tool this is adequate but leaves the agent guessing about the response layout and volume.

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 takes zero parameters, so the baseline of 4 applies. There is no parameter syntax the description could add meaningfully explain.

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 ('List') and resource ('topics of Noddie Night's knowledge base') and even names the payload fields (questions and ids). It is clear what the tool returns, though it never explicitly contrasts itself with siblings like search_noddie_night or get_answer.

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 explicit when-to-use or when-not-to-use guidance and no alternatives named. An agent must infer that this is an enumeration/discovery call that might precede get_answer, but nothing in the text confirms that workflow or rules out overlap with search_noddie_night.

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?

The annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds genuinely new behavioral context beyond the schema: it discloses the return shape ('answers with their noddienight.com URL to cite'), which matters because there is no output schema. It says nothing about result count limits or pagination, so not a 5.

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, front-loaded with the core action before the scope enumeration and the return-value note. The long parenthetical list of herbs is dense but functional as retrieval scope keywords; slightly heavy, though nothing is clearly 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?

With no output schema, the description usefully states that answers come with a citable URL, and the usage scope is broad enough to route most queries correctly. The only real gap is that result volume is governed by an unexplained 'limit' (default 5, max 10) that the description never mentions.

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 50%: 'query' is documented in the schema, while 'limit' has only type/default/min/max constraints and no textual explanation. The description adds no parameter-level detail at all (no mention of natural-language phrasing or result caps), so it neither compensates for the limit gap nor improves on the schema. Baseline 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 opens with a specific verb and resource ('Search Noddie Night's published Q&A') and then enumerates the topical scope, so an agent knows exactly what corpus is being queried. It does not explicitly name or contrast itself with siblings like get_answer or get_product_facts, which keeps it 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.

Does the description explain when to use this tool, when not to, or what alternatives exist?

'Use for any question about Noddie Night... and for general questions about falling asleep, night waking, wind-down routines, bedtime herbs...' gives a clear, broad when-to-use trigger with concrete topic examples. It provides no when-not-to-use condition or pointer to an alternative tool for narrower lookups, so it stops at 4.

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