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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions that the tool 'provides safety information about reported side effects and reactions,' which implies a read-only operation, but it doesn't disclose critical behavioral traits such as whether this is a query of a public database, potential rate limits, authentication needs, or what the output format looks like (e.g., list of events with details). This leaves significant gaps for an agent to understand how to handle the tool effectively.

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 appropriately sized with three concise sentences that front-load the core purpose. Each sentence adds value: the first states the action, the second clarifies the type of information, and the third provides input guidance. There's no wasted text, making it efficient for quick understanding.

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 (3 parameters, no output schema, no annotations), the description is somewhat complete but has gaps. It covers the purpose and basic input guidance but lacks details on behavioral aspects (e.g., output format, data source limitations) and doesn't leverage sibling context to clarify usage. Without annotations or output schema, more context on what to expect from the tool would improve completeness.

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 schema already documents all parameters (drugName, limit, seriousness) with descriptions and defaults. The description adds minimal value by reiterating that drugName can be 'brand name or generic name,' which is already covered in the schema's description. It doesn't provide additional context like examples or usage tips beyond the schema, so it meets the baseline for high 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 clearly states the tool's purpose with a specific verb ('Get') and resource ('adverse event reports for a drug'), and it distinguishes the type of data (safety information about side effects and reactions). However, it doesn't explicitly differentiate from sibling tools like 'get-drug-safety-info', which might cover similar ground, so it falls short of a perfect score.

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 provides implied usage by specifying 'Use brand name or generic name' for the drugName parameter, which helps guide input. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get-drug-safety-info' or other siblings, and it doesn't mention exclusions or prerequisites, leaving some ambiguity.

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 full burden. It discloses that the tool returns 'all brand versions of the generic drug,' which adds useful behavioral context beyond the input schema. However, it lacks details on error handling, rate limits, authentication needs, or what specific information is included in the response. For a read-only tool with no annotations, this is adequate but not comprehensive.

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 sentences, front-loaded with the core purpose, followed by usage context and return behavior. Every sentence adds value without redundancy, making it efficient and well-structured.

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 (2 parameters, no output schema, no annotations), the description is reasonably complete. It covers purpose, usage context, and return behavior. However, without an output schema, it could benefit from more detail on the response format (e.g., structure of returned drug information).

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 schema already fully documents both parameters (genericName and limit). The description adds no additional parameter semantics beyond what the schema provides, such as format examples or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.

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 tool's purpose: 'Get drug information by generic (active ingredient) name.' It specifies the verb ('Get'), resource ('drug information'), and key constraint ('by generic name'). It also distinguishes from siblings by emphasizing generic name lookup versus brand name or NDC-based alternatives.

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 provides clear context for when to use this tool: 'Useful when you know the generic name but not the brand name.' This gives practical guidance. However, it does not explicitly state when not to use it or name specific alternatives among the sibling tools (e.g., get-drug-by-name for brand names).

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 full burden of behavioral disclosure. It describes the return values (e.g., brand name, generic name, manufacturer name) and specifies that the input should be a brand name, which adds useful context. However, it doesn't cover potential errors, rate limits, or authentication needs, leaving gaps in behavioral 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 front-loaded with the core purpose and usage, followed by a detailed list of return values. While the list of return values is lengthy, each item is relevant to the tool's function. The structure is efficient, with no redundant sentences, though it could be slightly more concise by grouping related return categories.

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 (single parameter, no output schema, no annotations), the description is adequate but has gaps. It explains the purpose, input semantics, and return values, but lacks error handling, performance expectations, or integration with sibling tools. Without annotations or output schema, more behavioral context would improve completeness.

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 input schema has 100% description coverage, with the parameter 'drugName' documented as 'Drug name.' The description adds semantic value by clarifying that 'The drug name should be the brand name,' which provides crucial context beyond the schema. Since there's only one parameter, this compensation is effective, warranting a score above the baseline.

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 tool's purpose as 'Get drug by name' and specifies it retrieves drug information, which is a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'get-drug-by-generic-name' or 'get-drug-by-ndc' beyond mentioning 'brand name' in the description.

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 provides some guidance by stating 'The drug name should be the brand name,' which implies when to use this tool versus alternatives like 'get-drug-by-generic-name.' However, it lacks explicit when-not-to-use instructions or clear alternatives, leaving usage context somewhat implied rather than fully articulated.

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 of behavioral disclosure. It describes the input formats but doesn't cover critical aspects like whether this is a read-only operation, potential rate limits, error handling, or what the output includes (e.g., drug details, availability). For a tool with no annotations, this leaves significant gaps in understanding its 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?

The description is extremely concise and front-loaded, with three sentences that efficiently cover the tool's purpose and input handling. Every sentence adds value: the first states the purpose, the second specifies formats, and the third clarifies dash handling. There is no wasted text 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?

Given the tool's complexity (a lookup with one parameter) and the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'drug information' entails in the output, potential limitations, or how it integrates with sibling tools. For a tool with no structured output or behavioral hints, more context is needed to ensure effective use.

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 description coverage is 100%, with the parameter 'ndcCode' fully documented in the schema. The description adds value by reiterating the acceptable formats (e.g., 'XXXXX-XXXX, XXXXX-XXXX-XX, or without dashes'), but this doesn't provide new semantic meaning beyond what's already in the schema. This meets the baseline of 3 for high schema 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 clearly states the tool's purpose: 'Get drug information by National Drug Code (NDC).' It specifies the verb ('Get') and resource ('drug information'), and while it doesn't explicitly differentiate from siblings like 'get-drug-by-product-ndc', the focus on NDC codes provides some implicit distinction. However, it doesn't fully explain how it differs from 'get-drug-by-product-ndc', which might handle similar inputs.

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 usage by detailing the acceptable NDC formats, suggesting when to use this tool (for NDC-based lookups). However, it provides no explicit guidance on when to choose this over alternatives like 'get-drug-by-product-ndc' or 'get-drug-by-name', nor does it mention any exclusions or prerequisites. The context is clear but lacks comparative direction.

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 full burden but only states what the tool does operationally. It doesn't disclose behavioral traits like whether this is a read-only operation, what format the drug information returns, error handling, rate limits, or authentication requirements for a tool that accesses drug data.

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 extremely concise with just two sentences that are front-loaded with the core purpose. Every word earns its place - the first sentence states what the tool does, and the second clarifies its scope and behavior.

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 simple lookup tool with 100% schema coverage but no annotations or output schema, the description adequately covers the basic operation. However, it doesn't address what 'drug information' includes, the response format, or potential limitations - gaps that become more significant without structured output documentation.

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 schema already documents the single parameter's format. The description reinforces the format requirement but doesn't add meaningful semantic context beyond what the schema provides, such as examples of valid NDCs or what constitutes a 'product' versus 'package' NDC.

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 specific verb ('Get drug information') and resource ('by product NDC'), and explicitly distinguishes from sibling tools by specifying it uses product NDC format and ignores package variations (unlike 'get-drug-by-ndc' which likely handles package-level NDCs).

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 provides clear context about when to use this tool ('by product NDC only') and what it does ('ignores package variations and finds all packages for a product'), but doesn't explicitly mention when NOT to use it or name specific alternatives among the sibling tools.

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 full burden for behavioral disclosure. While it mentions the tool 'gets' information (implying read-only), it doesn't explicitly state whether this requires authentication, has rate limits, what format the information returns in, or potential limitations. For a tool with no annotation coverage, this leaves significant behavioral gaps.

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 appropriately concise with two sentences that efficiently convey the tool's purpose and key usage requirement. The first sentence clearly states what the tool does, and the second provides necessary input guidance. No wasted words or unnecessary elaboration.

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 has no annotations, no output schema, and a simple single parameter, the description provides adequate basic information about purpose and input format. However, for a tool that returns 'comprehensive safety information', the description should ideally mention what format the information comes in (structured data, text summary, etc.) or any limitations, making it minimally complete but with clear 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?

Schema description coverage is 100%, with the single parameter 'drugName' clearly documented in the schema as 'Drug brand name'. The description adds minimal value beyond this by repeating 'Use brand name', which essentially restates what the schema already provides. Baseline 3 is appropriate when schema does the heavy lifting.

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 tool's purpose: 'Get comprehensive safety information for a drug' with specific details about what information is included (warnings, contraindications, drug interactions, precautions). It distinguishes from some siblings by focusing on safety information rather than general drug lookup or adverse events, though it doesn't explicitly contrast with all siblings like 'get-drug-by-generic-name'.

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 provides some usage guidance: 'Use brand name' specifies the required input format. However, it doesn't explicitly state when to use this tool versus alternatives like 'get-drug-adverse-events' (which might focus on different safety aspects) or other drug lookup tools. The guidance is implied rather than explicit about tool selection.

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 of behavioral disclosure. It mentions the tool 'Get all drugs' but does not specify behavioral traits like whether it returns a list, pagination details, error handling, or performance characteristics (e.g., rate limits). The description adds minimal context beyond the basic operation, leaving gaps in transparency for an agent to use it effectively.

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 the core purpose in the first sentence and adds useful context in the second, with no wasted words. Both sentences earn their place by clarifying the tool's function and typical use cases, making it efficient and well-structured for quick understanding.

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 (a filtered list operation with 2 parameters), no annotations, and no output schema, the description is adequate but incomplete. It covers the purpose and usage context but lacks details on return values, error conditions, or behavioral nuances. This leaves the agent with gaps, especially since there's no output schema to clarify what the tool returns.

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%, with both parameters ('manufacturerName' and 'limit') well-documented in the schema. The description does not add any parameter-specific details beyond what the schema provides, such as format examples or constraints. Given the high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.

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 tool's purpose with a specific verb ('Get') and resource ('all drugs manufactured by a specific company'), distinguishing it from siblings like 'get-drug-by-name' or 'get-drug-by-ndc' which focus on individual drug lookup rather than manufacturer-based filtering. It explicitly mentions the manufacturer scope, making the purpose distinct and 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 provides clear usage context by stating it's 'Useful for finding alternatives or checking manufacturer portfolios,' which implicitly guides when to use this tool (e.g., for manufacturer-related queries). However, it does not explicitly mention when not to use it or name specific alternatives among sibling tools, such as preferring 'get-drug-by-name' for individual drug lookups, which prevents a score of 5.

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