Pet Data Station

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

With no annotations provided, the description carries the behavioral burden. It transparently discloses that the tool returns breed lists per jurisdiction plus a summary excerpt, and that full legal text is obtained via get_topic_context. It does not mention edge cases like empty results or default behavior when parameters are omitted, but the core behavior is clear.

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 the tool's purpose. It uses line breaks for readable parameter examples and includes a cross-reference to the sibling tool without verbose or redundant wording.

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 that an output schema is present, the description need not spell out return types. It covers the essential context: jurisdictions, example values, output nature, and where to get richer legal text. It lacks explicit note about behavior when optional parameters are omitted, but that is a minor gap.

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 description adds meaningful examples for 'jurisdiction' and 'breed', which is necessary given the 0% schema description coverage. However, 'limit' is left entirely to the schema (type/default) with no explanation of its effect, so one of the three parameters is not semantically enriched.

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 ('check') and resource ('banned / restricted dog-breed directories'), and clarifies the jurisdictional scope. This clearly distinguishes it from the unrelated sibling tools (nutrients, recalls, topics) and even signals the adjacent get_topic_context tool for full legal text.

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 concrete examples of valid jurisdiction and breed values, making it clear when the tool applies. It also points to get_topic_context for full legal text, implying when not to use this tool. However, it does not explicitly state 'use this when you need a quick directory check' or contrast with all alternatives.

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 must carry behavioral transparency. It only explains standards code syntax (bare codes = latest edition); it doesn't describe the output, constraints, or safe/read-only nature. This leaves significant behavioral ambiguity.

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 concise sentences plus an example; front-loaded with the primary purpose. Every element adds value.

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?

Despite an output schema, the description leaves several invocation details to a sibling tool and doesn't cover parameter values. The bare-code shorthand is helpful, but the overall description is only minimally sufficient.

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?

Input schema has 5 string parameters with 0% description coverage. The description explains the format and alias behavior for 'standards' and directs to get_nutrient_value for the other parameters, but does not define acceptable values for species, lifestage, nutrient, or basis.

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 clear action (compare) with object (one nutrient) and scope (across 2-3 standards). The example standards list and reference to get_nutrient_value disambiguate it from single-value sibling 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?

Explicitly frames the task as comparison across standards and points to get_nutrient_value for shared parameters, establishing usage context. It does not explicitly state when not to use it or edge cases, but the compare-vs-single distinction 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?

No annotations are provided, so the description carries the full burden. It states that all 6 params are required and provides input constraints, but does not explicitly note the operation is read-only or describe error behavior if the combination is not found. Still, it offers some behavioral context by defining the input space.

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 a one-sentence purpose, followed by a compact parameter list with inline explanations. Every line adds unique information, and there is no fluff or repetition of schema fields.

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's 6-parameter complexity, a minimal schema, and no annotations, the description is remarkably complete. It covers allowed values, units, aliases, and required status. The existing output schema covers return values, so the description does not need to do so, leaving no major information gaps.

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?

With 0% schema description coverage, the description fully compensates by documenting every parameter: allowed values (e.g., AAFCO|FEDIAF..., dog|cat, adult|GR), examples (year: 2024), units (DM, cal per 1000 kcal), and aliases (Ca, P). This goes far beyond the bare schema and is essential 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 opens with 'Single nutrient requirement from one standard', which clearly identifies the verb (retrieve), resource (nutrient requirement), and scope (single standard). This directly distinguishes it from the sibling compare_nutrient_standards tool, which presumably handles multiple standards.

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 when to use this tool by emphasizing 'one standard', contrasting with the sibling comparison tool. It gives clear context for single-nutrient lookups, though it doesn't explicitly state 'use this for single standards, use compare_nutrient_standards for comparisons'.

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 present, the description carries the full burden. It discloses key behavioral traits: token range (~200-500), source-verified claim types (verbatim vs paraphrased), citation structure, and the trust block. It does not mention error cases or explicit read-only status, but the fetch nature and detailed output coverage provide substantial transparency.

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 but every phrase adds value: token budget, claim handling, citation components, trust block, and sequencing. It is not wasteful, though slightly longer than strictly necessary due to parenthetical details.

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's moderate complexity, the description covers the input, the output structure (including trust axes and display_grade), and the required preceding step. An output schema exists, so return values are further documented, making the description complete for agent 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 0%, so the description must compensate for the sole parameter (topic_id). It does so by explaining that the tool fetches 'one topic' and directing the agent to search_pet_topics to obtain valid topic_ids. This adds meaningful context beyond the bare schema, though it stops short of specifying format or constraints.

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 ('Fetch') and resource ('one topic's RAG context'), and clearly distinguishes from siblings by naming the discovery tool ('Discover topic_ids with search_pet_topics first'). It also outlines the return contents, making the tool's scope 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?

The description explicitly states to use search_pet_topics first to discover topic_ids, providing clear sequencing guidance and implicitly excluding direct use without a known topic_id. This directly addresses when to use this tool versus alternatives.

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. It discloses the data scope (FDA, EU RASFF, CN enforcement) and return behavior (summary excerpts traceable via topic_id), adding meaningful context. It could mention read-only semantics, but the search verb implies a safe read operation.

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 two focused sentences plus parameter guidance, front-loaded with the primary purpose. No redundant text or 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?

The output schema exists and the description explains output traceability, making it complete for a search tool. Its only minor gap is the undocumented 'limit' parameter and a lack of explicit exclusion for search_pet_topics, but the overall context is sufficient.

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 description adds semantic examples for brand, reason, and year, but omits the 'limit' parameter. Given schema description coverage is 0%, the description must compensate; it partially does but leaves a gap for one parameter, which is a meaningful omission.

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 ('Search') and identifies the resource ('pet-food recall / enforcement data') with explicit data sources (FDA, EU RASFF, CN enforcement). It clearly distinguishes from sibling tools like get_topic_context, which provides full detail, and search_pet_topics, which searches topics.

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 clearly indicates that full detail is available via get_topic_context, providing a definitive follow-up path. While it does not explicitly state when not to use this tool, the examples and differentiation from siblings imply the appropriate use case.

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 burden and does well by disclosing the metadata-only anti-dump design, the trust block components (authority_tier/fidelity/license + display_grade), and referencing server instructions. It omits details like rate limits or error behavior, but for a read-only search tool the disclosed behavior is substantial.

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 the primary purpose. It uses three sentences to convey purpose, search behavior, return structure, and a follow-up pointer. Dense but every clause adds value; the only minor issue is one long sentence that could be split for readability.

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?

The tool has an output schema, so return values are externally defined. The description adds context about the anti-dump design, trust block, and follow-up workflow, making it well-rounded. It lacks explicit sorting/pagination details, but those are not critical for a search tool of this scope.

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?

Despite 0% schema coverage, the description adds meaning to q (tokenized AND matching) and category (optional exact filter with concrete examples). The limit parameter is not mentioned, but the schema already provides a default, and its semantics are simple. The critical parameters are well explained.

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 starts with 'Search the source-verified pet regulatory & health topic whitelist', providing a specific verb and resource. It clearly distinguishes itself from siblings by noting it returns metadata only and directs users to get_topic_context for full text, making its unique role 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 clear context: this is a free-text search over topics, and it explicitly recommends a follow-up with get_topic_context(topic_id) for full details. However, it does not explicitly state when not to use this tool versus other siblings like search_pet_recalls, so it stops short of full exclusion guidance.

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