RxRadar

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 states it 'Get full details', which implies a read-only operation, but doesn't address potential behaviors like error handling (e.g., invalid NPI format), rate limits, authentication needs, or what 'full details' entails. This leaves significant gaps for a tool with no annotation coverage.

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 action ('Get full details') and key constraint ('by their 10-digit NPI number'), with zero wasted words. It's appropriately sized for a simple lookup 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?

Given the tool's low complexity (one parameter) and the presence of an output schema (which handles return values), the description is minimally adequate. However, with no annotations and incomplete behavioral transparency, it lacks details on error cases or operational constraints, making it just sufficient 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?

The description adds meaningful context beyond the input schema: it specifies that 'npi_number' must be a '10-digit NPI number', clarifying the expected format and length, which the schema (with 0% coverage) does not provide. Since there's only one parameter, this adequately compensates for the low 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 verb ('Get full details') and resource ('for a provider'), making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'search_providers', which might offer broader search functionality versus this specific lookup by NPI.

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 minimal guidance by specifying 'by their 10-digit NPI number', implying usage when you have an exact NPI. However, it lacks explicit when-to-use vs. alternatives (e.g., 'search_providers' for broader queries) or any prerequisites, leaving the agent to infer context.

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 what the tool searches for but doesn't mention important behavioral traits like whether this is a read-only operation, potential rate limits, authentication requirements, response format, or error handling. The examples help but don't fully compensate for the lack of structured behavioral information.

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 with two sentences: a clear purpose statement followed by helpful examples. Every sentence earns its place by providing essential information without redundancy. The examples are front-loaded with practical use cases that immediately clarify the tool's application.

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 should document return values) and moderate complexity with 3 parameters, the description provides adequate basic context about what the tool searches for. However, with no annotations and 0% schema description coverage, it leaves significant gaps in understanding parameter usage and behavioral characteristics that the description doesn't adequately address.

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 0%, so the description must compensate for all 3 parameters. It doesn't mention any parameters explicitly, though the examples imply the 'query' parameter usage. It doesn't explain 'limit' or 'product_type' at all. The description adds some value through query examples but doesn't fully compensate for the schema 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: searching FDA drug labels for specific medical information categories (indications, warnings, interactions, dosing). It uses the specific verb 'search' with the resource 'FDA drug labels' and lists the types of information retrievable. However, it doesn't explicitly differentiate from sibling tools like 'search_drug_safety' or 'search_literature' which might have overlapping domains.

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 that suggest when to use this tool (e.g., for contraindications, warnings, dosing queries). However, it lacks explicit guidance on when to choose this tool versus alternatives like 'search_drug_safety' or 'search_literature', and doesn't mention any exclusions or prerequisites for use.

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 whether this is a read-only operation, potential rate limits, authentication needs, or what the output contains (though an output schema exists). The examples hint at query formatting but don't explain result types or limitations, leaving significant gaps in transparency for a search tool.

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 function, and the examples are relevant and efficient. There's no wasted text, though it could be slightly more structured with bullet points or clearer separation of guidance.

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 annotations, but with an output schema), the description is partially complete. It covers the basic purpose and provides usage examples, but lacks details on behavioral traits, parameter meanings, and how it differs from siblings. The output schema reduces the need to explain return values, but overall completeness is adequate 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 0%, so the description must compensate but adds minimal parameter semantics. It mentions searching 'by drug name or reaction' which relates to the 'query' parameter, but doesn't explain 'limit' or 'serious_only'. The examples show query formats but don't clarify parameter roles or constraints, failing to adequately cover the 3 parameters beyond basic implication.

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 differentiate from '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 ('metformin lactic acidosis', etc.), suggesting it's for querying drug-reaction pairs, but doesn't explicitly state when to use this vs. alternatives like 'search_drug_labels' for official labeling or 'search_literature' for published studies. No explicit exclusions or prerequisites are mentioned, leaving usage context somewhat inferred rather than clearly defined.

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. It mentions natural language querying and provides examples but fails to disclose critical behavioral traits like rate limits, authentication needs, result format, or whether it's a read-only operation. The description is insufficient for a tool with no annotation coverage.

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 and front-loaded with the core purpose in the first sentence, followed by relevant examples. Every sentence earns its place by illustrating usage without 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's moderate complexity (3 parameters, no annotations, but with an output schema), the description is incomplete. It covers the basic purpose and query examples but lacks behavioral context and full parameter semantics. The output schema existence prevents a lower score, but gaps remain.

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 does not address 'limit' or 'pub_year_from'. The description adds some semantic value for 'query' but leaves other parameters undocumented, resulting in partial compensation.

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 specifying the biomedical literature domain rather than drug labels, safety, trials, or providers. The examples reinforce this specificity.

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 (biomedical queries) but does not explicitly state when to use this tool versus alternatives like search_drug_labels or search_trials. It provides clear natural language query guidance but lacks explicit sibling differentiation.

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 mentions 'natural language query' and gives examples, but doesn't disclose key behavioral traits: whether this is a read-only operation, any rate limits, authentication needs, pagination behavior, or what the output schema contains. For a search tool with zero annotation coverage, this is a significant gap in 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by concise examples that illustrate usage without redundancy. Every sentence earns its place by clarifying the tool's function, 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 (search with 3 parameters), no annotations, and an output schema exists (which reduces need to explain return values), the description is somewhat complete. It covers the basic purpose and query semantics but lacks details on behavioral traits and parameter usage beyond the query. This is adequate but with clear gaps 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?

Schema description coverage is 0%, so the schema provides no parameter details. The description adds some value by implying the 'query' parameter accepts natural language strings (via examples), but doesn't explain 'limit' (default 10) or 'state' (default null) parameters. It partially compensates for the coverage gap but leaves two of three parameters undocumented, resulting in minimal improvement.

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: 'Search NPI healthcare providers by natural language query.' It specifies the verb ('search'), resource ('NPI healthcare providers'), and method ('natural language query'). However, it doesn't explicitly differentiate from sibling tools like 'get_provider_details' (which likely retrieves details for a specific provider) or other search tools for different resources (drugs, literature, trials), missing full sibling distinction.

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 ('cardiologist in Houston', etc.), suggesting it's for broad provider searches. However, it lacks explicit guidance on when to use this tool versus alternatives like 'get_provider_details' (for specific provider details) or other search tools for non-provider data. No exclusions or prerequisites are mentioned, leaving usage context somewhat vague.

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 it searches by natural language query. It doesn't disclose behavioral traits like rate limits, authentication needs, pagination behavior, or what the search returns (though output schema exists). The examples help but don't cover operational constraints.

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, followed by relevant examples that earn their place by illustrating query format. No wasted words or redundant information, making it highly efficient for an AI agent.

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 (so return values are documented elsewhere) and moderate complexity, the description is reasonably complete for a search tool. It covers the primary use case and query format but lacks details on behavioral constraints and some parameter meanings, which could be improved.

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%, but the description adds meaningful context by explaining the 'query' parameter accepts natural language search terms, as shown in examples. However, it doesn't explain the semantics of 'limit', 'phase', or 'status' parameters, leaving them undocumented beyond the schema structure.

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 ('search') and resource ('ClinicalTrials.gov studies'), and distinguishes it from siblings by specifying the exact database it queries. The examples further clarify the scope of natural language queries it accepts.

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 (e.g., searching for specific treatments or conditions), but doesn't explicitly state when to use this tool versus alternatives like 'search_literature' or 'search_drug_labels'. No explicit guidance on when-not-to-use or prerequisites is provided.

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