RxRadar

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 the basic operation. It doesn't disclose behavioral traits like authentication requirements, rate limits, error handling, or whether this is a read-only operation (though implied by 'Get'). No contradictions exist since annotations are absent.

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, efficient sentence that front-loads the core purpose with zero wasted words. Every element ('Get full details', 'provider', '10-digit NPI number') earns its place by providing essential 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?

Given the simple input schema (1 required parameter) and presence of an output schema, the description adequately covers the tool's purpose and parameter semantics. However, for a tool with no annotations, it could better address behavioral aspects like permissions or response format, though the output schema mitigates some of this 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 schema has 0% description coverage, so the description fully compensates by explaining the single parameter's purpose ('NPI number'), format ('10-digit'), and that it's required for retrieving provider details. This adds crucial meaning beyond the bare 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 clearly states the specific action ('Get full details') and resource ('for a provider'), specifying the exact identifier ('by their 10-digit NPI number'). It distinguishes from sibling tools like 'search_providers' by focusing on retrieving detailed information for a single provider rather than searching across multiple providers.

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 when you have a specific NPI number and need detailed provider information, contrasting with 'search_providers' for broader searches. However, it doesn't explicitly state when NOT to use this tool or mention all alternatives like 'search_drug_labels' for different resource types.

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 for behavioral disclosure. While it mentions what content is searched (FDA drug labels), it doesn't describe important behavioral traits like whether this is a read-only operation, what authentication might be required, rate limits, response format, or what happens with null/default parameters. The examples help but don't constitute comprehensive 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 perfectly front-loaded with the core purpose in the first sentence, followed by concrete examples that demonstrate usage patterns. Every sentence earns its place by providing either declarative information or illustrative value. No wasted words or redundant 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?

Given the tool's moderate complexity (search with 3 parameters), no annotations, and the presence of an output schema, the description is minimally adequate. The output schema existence means return values don't need explanation, but the description should do more to compensate for the complete lack of parameter documentation and behavioral context beyond the basic purpose statement.

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 and 3 parameters (query, limit, product_type), the description provides no explicit information about parameters. The examples show query-like inputs but don't explain the query parameter's format, what limit controls, or what product_type accepts. The description doesn't compensate for the complete lack of schema documentation.

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 searches FDA drug labels for specific medical information categories (indications, warnings, interactions, dosing), providing a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like 'search_drug_safety' or 'search_literature' to explain why this tool is specifically for FDA labels versus other drug information sources.

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 three concrete examples that imply when to use this tool (for contraindications, warnings, dosing queries related to FDA labels), giving clear context. However, it doesn't explicitly state when NOT to use it or name alternatives among the sibling tools, leaving some ambiguity about tool selection boundaries.

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 searching FAERS but doesn't disclose traits like rate limits, authentication needs, data freshness, or pagination behavior. The examples hint at query format but don't explain what happens if no results are found or how results are structured. For a search tool with zero annotation coverage, this is a significant gap.

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 two sentences: one stating the purpose and one providing examples. It's front-loaded with the core functionality, and the examples are relevant and concise. There's no wasted text, though it could be slightly more structured by explicitly listing parameters.

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 an output schema (which reduces the need to describe return values), no annotations, and 0% schema coverage, the description is moderately complete. It covers the basic purpose and query usage but lacks details on behavioral traits and parameter semantics. For a search tool with three parameters and no annotations, it should do more to explain limits and filtering options.

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 0%, so the description must compensate for undocumented parameters. It mentions searching by 'drug name or reaction' and provides query examples, which adds meaning for the 'query' parameter. However, it doesn't explain 'limit' (default 10) or 'serious_only' (default false), leaving two of three parameters without semantic context. The description partially helps but doesn't fully address the coverage gap.

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 searches FDA adverse event reports (FAERS) by drug name or reaction, providing a specific verb ('Search') and resource ('FDA adverse event reports'). It distinguishes from siblings like search_drug_labels and search_literature by focusing on safety reports rather than labels or literature. However, it doesn't explicitly contrast with search_trials, which might also involve drug safety.

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 through examples like 'metformin lactic acidosis', suggesting it's for querying drug-reaction pairs. It doesn't explicitly state when to use this tool versus alternatives like search_drug_labels or search_trials, nor does it provide exclusions or prerequisites. The examples give some context but lack clear guidance on 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?

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool searches PubMed but doesn't mention rate limits, authentication needs, result format, or whether it's a read-only operation. The examples help but lack critical operational details.

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 perfectly concise with two sentences: a clear purpose statement followed by specific, relevant examples. Every element earns its place, and the structure is front-loaded with the core functionality.

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 an output schema (which handles return values) but no annotations and 0% schema coverage, the description is incomplete. It covers the main query parameter well but misses behavioral context and documentation for other parameters. The examples help but don't fully compensate for the 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 0%, so the description must compensate. It explains the 'query' parameter through examples but doesn't mention 'limit' or 'pub_year_from'. The natural language guidance for queries adds value, but two of three parameters remain undocumented in both schema and description.

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 specific verb ('Search') and resource ('PubMed biomedical literature'), and distinguishes it from siblings by focusing on literature rather than providers, drug labels, safety, or trials. The examples reinforce the specific domain of biomedical literature search.

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 context through the examples showing natural language queries for biomedical topics, but does not explicitly state when to use this tool versus alternatives like search_drug_labels or search_trials. It provides clear guidance on query format but lacks explicit exclusions or 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 provided, the description carries full burden but lacks behavioral details. It doesn't disclose rate limits, authentication needs, pagination behavior, or what happens with invalid queries. The examples hint at natural language processing but don't explicitly describe the tool's behavior beyond basic functionality.

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 perfectly front-loaded with the core purpose in the first sentence, followed by illustrative examples. Every sentence earns its place, and there's 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 moderate complexity (search with 3 parameters), no annotations, but presence of an output schema, the description provides adequate context for basic usage. The output schema reduces the need to describe return values, but more behavioral transparency would improve completeness for a search tool.

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 for 3 parameters, the description only partially compensates. It explains the 'query' parameter through examples but doesn't mention 'limit' or 'state' parameters at all. The natural language query guidance is helpful but incomplete given the parameter coverage gap.

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 specific verb ('search') and resource ('NPI healthcare providers'), and distinguishes it from siblings by specifying the domain (healthcare providers vs. drugs, literature, trials). The examples reinforce the specific use case.

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 (searching healthcare providers by natural language query) and the examples illustrate typical use cases. However, it doesn't explicitly state when NOT to use it or name alternatives among siblings (e.g., get_provider_details for detailed info).

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. While it mentions the natural language query capability and provides examples, it doesn't describe important behavioral traits like whether this is a read-only operation, what authentication might be required, rate limits, pagination behavior, or what the output format looks like. The description adds some context but leaves significant 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 extremely concise and well-structured. The first sentence clearly states the core functionality, followed by relevant examples that demonstrate usage patterns. Every sentence earns its place, and there's 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 that an output schema exists, the description doesn't need to explain return values. However, for a search tool with 4 parameters (none documented in schema) and no annotations, the description is incomplete. It covers the basic purpose and provides helpful examples, but doesn't address parameter meanings or important behavioral aspects that would help an agent use the tool effectively.

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 0%, meaning none of the 4 parameters have descriptions in the schema. The tool description doesn't mention any parameters beyond the natural language query concept. It doesn't explain what 'limit', 'phase', or 'status' parameters do, their formats, or how they interact with the query. The description fails to compensate for the complete lack of schema documentation.

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: searching ClinicalTrials.gov studies using natural language queries. It specifies the resource (ClinicalTrials.gov studies) and action (search by natural language query). However, it doesn't explicitly differentiate from sibling tools like search_literature or search_drug_labels, which likely search different databases or resources.

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 through examples of natural language queries, suggesting when to use this tool for clinical trial searches. However, it lacks explicit guidance on when to choose this tool over alternatives like search_literature or search_drug_labels, and doesn't mention any prerequisites or exclusions.

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