RxRadar
Server Details
Semantic search across 5 US government healthcare databases.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.4/5 across 6 of 6 tools scored.
Each tool has a clearly distinct purpose targeting different healthcare data sources: provider details, drug labels, drug safety reports, biomedical literature, provider search, and clinical trials. The descriptions specify unique resources (NPI, FDA labels, FAERS, PubMed, ClinicalTrials.gov) with no functional overlap, making tool selection unambiguous.
All tools follow a consistent verb_noun pattern with 'search_' or 'get_' prefixes (e.g., search_drug_labels_tool, get_provider_details_tool). The naming is uniform across all six tools, using snake_case throughout and clear action-object naming that accurately reflects each tool's function.
Six tools is well-scoped for a healthcare data server, covering key domains like providers, drugs, literature, and trials without being overwhelming. Each tool serves a distinct data source, making the count appropriate for comprehensive healthcare information retrieval without redundancy or excessive complexity.
The toolset provides strong coverage for healthcare data retrieval across major sources, but lacks update/delete operations (e.g., no tools for modifying provider records or drug data). Given the server's apparent search/retrieval focus, this is a minor gap, as agents can perform core lookup workflows effectively with the available tools.
Available Tools
6 toolsget_provider_details_toolAInspect
Get full details for a provider by their 10-digit NPI number.
| Name | Required | Description | Default |
|---|---|---|---|
| npi_number | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 the tool retrieves details but does not disclose behavioral traits such as error handling for invalid NPI numbers, rate limits, authentication needs, or what 'full details' entails. This leaves significant gaps in understanding the tool's 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 a single, efficient sentence that front-loads the purpose with no wasted words. It directly communicates the tool's function and key parameter detail, making it highly concise 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 has an output schema (which likely defines the return structure), the description does not need to explain return values. However, with no annotations and a simple input schema, the description adequately covers the basic purpose and parameter semantics, though it could improve by adding more behavioral context.
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 meaning beyond the input schema by specifying that the 'npi_number' parameter must be a 10-digit NPI number, which is not indicated in the schema (schema description coverage is 0%). This compensates for the low schema coverage, providing crucial context for parameter usage.
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 identifier ('by their 10-digit NPI number'). It distinguishes from sibling tools like 'search_providers_tool' by focusing on retrieval of a single provider's details rather than searching 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 to look up details, but it does not explicitly state when to use this versus alternatives like 'search_providers_tool' or provide exclusions. The context is clear but lacks explicit 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.
search_drug_labels_toolBInspect
Search FDA drug labels for indications, warnings, interactions, and dosing.
Examples: 'statin contraindications liver disease', 'ACE inhibitor pregnancy warnings', 'metformin dosing renal impairment'
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| product_type | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 mentions what the tool searches for but doesn't describe behavioral traits like rate limits, authentication requirements, pagination behavior, error conditions, or what the output looks like. The examples help illustrate usage 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 extremely concise and well-structured. The first sentence clearly states the purpose, and the second sentence provides helpful examples that illustrate usage without unnecessary elaboration. Every sentence earns its place, and the information is front-loaded appropriately.
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 there's an output schema (which handles return values) and no annotations, the description is moderately complete for a search tool. It explains what the tool searches and provides usage examples, but it lacks important context about behavioral traits (rate limits, auth needs) and doesn't address parameter semantics adequately. For a tool with 3 parameters and 0% schema coverage, more parameter guidance would be beneficial.
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. It doesn't mention any of the three parameters (query, limit, product_type) explicitly. However, the examples illustrate what types of queries are appropriate, providing some semantic context for the 'query' parameter. This adds marginal value but doesn't fully compensate for the complete lack of parameter documentation in the 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 tool's purpose: searching FDA drug labels for specific medical information categories (indications, warnings, interactions, dosing). It uses a specific verb ('Search') and identifies the resource ('FDA drug labels'). However, it doesn't explicitly differentiate from sibling tools like 'search_drug_safety_tool' or 'search_literature_tool', which likely search different data 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 implied usage guidance through the examples, suggesting this tool is for querying specific clinical topics in drug labels. However, it doesn't explicitly state when to use this tool versus alternatives like 'search_drug_safety_tool' or 'search_literature_tool', nor does it 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.
search_drug_safety_toolBInspect
Search FDA adverse event reports (FAERS) by drug name or reaction.
Examples: 'metformin lactic acidosis', 'warfarin bleeding elderly', 'statin myopathy rhabdomyolysis'
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| serious_only | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 mentions the data source (FAERS) and search scope (drug name or reaction), but doesn't describe response format, pagination, rate limits, authentication needs, or error handling. For a search tool with zero annotation coverage, 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 well-structured: one clear purpose sentence followed by three relevant examples. Every sentence earns its place by either defining the tool or illustrating usage. No wasted words 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 has an output schema (which handles return values), no annotations, and moderate complexity with 3 parameters, the description provides basic purpose and examples but lacks behavioral context and parameter explanations. It's minimally adequate but has clear gaps in transparency and parameter semantics that reduce 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 0%, so the schema provides no parameter documentation. The description mentions 'drug name or reaction' which relates to the 'query' parameter, but doesn't explain the 'limit' or 'serious_only' parameters at all. It provides examples that hint at query format, but doesn't fully compensate for the coverage gap across all three parameters.
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 FDA adverse event reports (FAERS) by drug name or reaction.' This specifies the verb (search), resource (FAERS reports), and search criteria (drug name or reaction). However, it doesn't explicitly differentiate from sibling tools like 'search_drug_labels_tool' or 'search_literature_tool' beyond the FAERS focus.
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 ('metformin lactic acidosis', etc.), suggesting it's for querying adverse events with drug-reaction pairs. However, it lacks explicit guidance on when to use this tool versus alternatives like 'search_drug_labels_tool' or 'search_trials_tool', and doesn't mention prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_literature_toolAInspect
Search PubMed biomedical literature by natural language query.
Examples: 'SGLT2 inhibitors heart failure outcomes 2022', 'mRNA vaccine immunogenicity elderly', 'GLP-1 agonist weight loss meta-analysis'
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| pub_year_from | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 'search' but does not disclose behavioral traits such as rate limits, authentication needs, result format, or whether it's read-only. The examples hint at query format but lack 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by relevant examples. Every sentence earns its place by clarifying usage without 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 3 parameters with 0% schema coverage and no annotations, the description is incomplete—it only covers one parameter. However, an output schema exists, so return values need not be explained. The complexity is moderate, but gaps in parameter and behavioral details 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 with examples of natural language biomedical queries, adding meaning beyond the schema. However, it does not address 'limit' or 'pub_year_from', leaving two parameters undocumented.
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 ('PubMed biomedical literature'), and distinguishes it from siblings by specifying the domain (biomedical literature vs. drug labels, safety, trials, or providers). The examples reinforce the biomedical focus.
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 the examples (natural language queries for biomedical topics), but does not explicitly state when to use this tool versus alternatives like 'search_trials_tool' or 'search_drug_labels_tool'. No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_providers_toolBInspect
Search NPI healthcare providers by natural language query.
Examples: 'cardiologist in Houston', 'pediatric oncologist Boston', 'dermatologist accepting Medicare Florida'
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| state | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 searches by natural language query but doesn't describe key behaviors: whether it's read-only (implied by 'search'), how results are returned (e.g., pagination, sorting), rate limits, authentication needs, or error handling. The examples hint at query format but lack operational details, leaving 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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by examples that illustrate usage without redundancy. Every sentence earns its place by clarifying query format. There's no wasted text, making it efficient and easy to parse.
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 primary action and query semantics but lacks behavioral details (e.g., performance, constraints) and doesn't explain the 'limit' or 'state' parameters. The output schema exists, so return values needn't be described, but other gaps remain for a search tool with no annotations.
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 explains the 'query' parameter through examples (e.g., 'cardiologist in Houston'), adding meaning beyond the schema's type definition. However, it doesn't address 'limit' (default 10) or 'state' (default null), leaving two of three parameters with minimal context. The description adds value for 'query' but doesn't fully compensate for the low 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 searches NPI healthcare providers using natural language queries, providing a specific verb ('search') and resource ('NPI healthcare providers'). It distinguishes from sibling tools like search_drug_labels_tool or search_trials_tool by focusing on providers rather than drugs or trials. However, it doesn't explicitly differentiate from get_provider_details_tool, which appears to retrieve details for specific providers rather than searching.
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'), suggesting it's for broad provider searches based on specialties, locations, or insurance. However, it doesn't explicitly state when to use this tool versus alternatives like get_provider_details_tool for detailed info on a known provider, or when not to use it (e.g., for non-provider searches). The examples provide context but lack explicit 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.
search_trials_toolCInspect
Search ClinicalTrials.gov studies by natural language query.
Examples: 'pembrolizumab non-small cell lung cancer', 'CAR-T cell therapy B-cell lymphoma', 'GLP-1 receptor agonist obesity phase 3'
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| phase | No | ||
| query | Yes | ||
| status | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Tool Definition Quality
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 states the tool searches by natural language query but doesn't describe behavioral traits such as rate limits, authentication needs, response format, pagination, or error handling. 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, with the core purpose in the first sentence and relevant examples following. Every sentence earns its place by illustrating usage 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 (4 parameters, 1 required), no annotations, and an output schema present, the description is partially complete. It covers the basic purpose and provides examples but lacks details on parameters, behavioral context, and usage guidelines. The output schema reduces the need to explain return values, but gaps remain in other areas.
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 only mentions the 'query' parameter implicitly through examples but doesn't explain 'limit', 'phase', or 'status' parameters. The examples add minimal value beyond the schema, failing to adequately compensate for 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 ClinicalTrials.gov studies by natural language query, providing a specific verb ('search') and resource ('ClinicalTrials.gov studies'). It distinguishes from sibling tools like search_drug_labels_tool and search_literature_tool by specifying the database, though it doesn't explicitly contrast them. The purpose is clear but lacks explicit sibling differentiation.
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 no guidance on when to use this tool versus alternatives. It mentions examples of queries but doesn't specify contexts, prerequisites, or exclusions relative to sibling tools like search_providers_tool or search_drug_safety_tool. Usage is implied through examples only, with no explicit when/when-not statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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