483 Risk Radar by Health AI
Server Details
FDA device & vehicle recall risk for AI agents: recall history, MAUDE trend, risk score.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 3.9/5 across 6 of 6 tools scored.
The five health-related tools each have distinct, well-described purposes. The vehicle safety tool is clearly different, but its inclusion does not cause confusion with the others; however, the overall domain mismatch slightly reduces clarity.
Most tools follow a domain_action pattern, but some use different verbs ('lookup', 'search', 'stats', 'chain'), and 'evidence_cohort_stats' is a noun phrase. This inconsistency makes the naming pattern less predictable.
Six tools is a reasonable number, but the inclusion of a vehicle safety tool alongside health-focused ones suggests two separate domains. The count is slightly too small to cover both adequately, and one tool feels out of place.
For the health AI regulatory domain, the tools cover key operations: lookup by ID, lookup by product code, aggregate stats, search, and predicate tracing. Minor gaps exist (e.g., direct comparison), but core needs are met.
Available Tools
6 toolsdevice_evidence_lookupAInspect
Look up the structured premarket evidence FDA accepted for a specific AI/ML-enabled device by 510(k) number (e.g. K252148). Returns parsed summary fields — validation study design, sample sizes, endpoints, reported performance, predicate chain, PCCP — each with a verbatim source quote and page. Null means the summary did not state it.
| Name | Required | Description | Default |
|---|---|---|---|
| k_number | Yes | 510(k) number, e.g. K252148 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses return format (parsed summary fields with verbatim source quotes and pages) and explains that null means the summary did not state it. Since no annotations are provided, the description carries full burden and handles it well, though it omits safety aspects like read-only nature.
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 three sentences with zero waste. It front-loads the purpose, then details the return structure, and finally explains null handling. Every sentence 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?
Given the simple one-parameter input and no output schema, the description covers the main aspects: input, output format, and null interpretation. It lacks error handling details, but is sufficiently complete for a straightforward lookup 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 100%, so baseline is 3. The description adds an example (K252148) and clarifies that the tool is for AI/ML-enabled devices, but does not enhance parameter semantics beyond what the schema already provides.
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 'look up' and specifies the resource: structured premarket evidence for an AI/ML-enabled device by 510(k) number. It distinguishes from siblings like evidence_search by focusing on a specific device's FDA-accepted evidence with parsed fields.
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 510(k) number and want structured evidence, but does not explicitly state when to use this tool vs alternatives like evidence_search or predicate_chain. No when-not-to-use guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
device_risk_lookupAInspect
Look up FDA compliance risk for a medical device category by three-letter product code (e.g. FRN = infusion pump). Returns recall history, MAUDE adverse-event trend, warning-letter matches, and a composite risk score.
| Name | Required | Description | Default |
|---|---|---|---|
| product_code | Yes | FDA product code, e.g. FRN |
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. It clearly discloses the return types (recall history, MAUDE adverse-event trend, warning-letter matches, composite risk score). However, it does not explicitly state that the operation is read-only or note any authentication requirements, which would enhance transparency. The description does not contradict any annotations since none exist.
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 consists of two concise sentences. The first sentence states the purpose and input, and the second lists the outputs. Every word adds value; there is no redundancy or fluff. It is front-loaded with the core action and resource.
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 one parameter and no output schema, the description is nearly complete. It specifies the input format and explains what the output includes. However, it could improve by mentioning typical use cases or any limitations (e.g., only for FDA-registered devices). The sibling tools suggest a broader domain, but this tool's description stands alone well.
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 for the single parameter, providing its type and constraints. The description adds value by giving an example ('FRN = infusion pump') and context that the code is three letters, reinforcing the schema. This extra detail helps the agent understand the parameter beyond 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 uses a specific verb 'Look up' and clearly identifies the resource as 'FDA compliance risk for a medical device category'. It specifies the input as 'three-letter product code' with an example, and lists distinct outputs (recall history, MAUDE trend, warning-letter matches, composite risk score). This distinguishes it from sibling tools like device_evidence_lookup, which likely focus on different aspects.
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 for risk lookup but does not explicitly state when to use this tool versus alternatives like device_evidence_lookup or evidence_search. No exclusions or prerequisites are mentioned. The context signals show sibling tools, but no guidance on tool selection is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
evidence_cohort_statsAInspect
Aggregate evidence-reporting stats across the AI/ML device corpus (optionally by panel): share reporting sensitivity+specificity, clinical data, and PCCP, plus median predicate age. The live version of published AI/ML-clearance analyses.
| Name | Required | Description | Default |
|---|---|---|---|
| panel | No | Advisory panel, e.g. Radiology; omit for all |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It mentions aggregation and live status but does not disclose whether the tool is read-only, mutation risk, or any other behavioral traits.
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 sentence but packs multiple details; it is reasonably concise but could be restructured for better 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?
Given the single optional parameter and no output schema, the description adequately covers purpose, metrics returned, and source (live version of published analyses). It lacks output format details but is sufficient for an aggregate stats 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 coverage is 100% and the description adds context (e.g., 'e.g. Radiology; omit for all') beyond the schema's parameter description. The tool description also reinforces the optional 'by panel' 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 tool aggregates evidence-reporting stats for AI/ML devices, lists specific metrics (sensitivity, specificity, clinical data, PCCP, median predicate age), and notes optional panel filtering. It distinguishes from siblings like device_evidence_lookup which are individual lookups.
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?
Usage is implied for aggregate stats vs individual lookups, but there is no explicit guidance on when to use this tool versus siblings, nor any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
evidence_searchAInspect
Find AI/ML device clearances by filter — product code, panel, applicant, and whether the submission reported clinical data, both sensitivity+specificity, or a PCCP. Answers 'what evidence did FDA accept for devices like mine'. Returns matching records with their parsed evidence.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| panel | No | Advisory panel, e.g. Radiology | |
| has_pccp | No | Included a Predetermined Change Control Plan | |
| applicant | No | Substring match on applicant/company name | |
| product_code | No | FDA product code, e.g. QIH | |
| has_clinical_data | No | ||
| reports_sens_spec | No | Reported both sensitivity and specificity |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It states that the tool returns matching records with parsed evidence, which is the main behavior. However, it does not disclose whether it is read-only, idempotent, or has any limitations like pagination or rate limits. The description is adequate but not rich.
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 long, front-loading the purpose and key filters. Every sentence adds value with no repetition or fluff. It is 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 7 optional parameters and no output schema or annotations, the description adequately covers the main functionality: search filters and return type. It does not explain the 'limit' parameter or that all filters are optional, but the schema covers that. The description is complete enough for a straightforward 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?
The description adds value by grouping parameters (e.g., 'clinical data, both sensitivity+specificity, or a PCCP') and framing them as filters. However, schema coverage is already 71% (5 of 7 parameters have descriptions), so the baseline is 3. The description does not provide additional parameter semantics beyond restating the schema descriptions.
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: finding AI/ML device clearances by specific filters (product code, panel, applicant, clinical data, sensitivity+specificity, PCCP). It also answers a user question ('what evidence did FDA accept for devices like mine') and mentions returning matching records. However, it does not explicitly differentiate from sibling tools like device_evidence_lookup or predicate_chain.
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 for discovering evidence accepted by FDA for similar devices, giving some context. However, it lacks explicit when-to-use or when-not-to-use guidance, and does not mention alternatives or prerequisites. The guidance is implicit rather than direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
predicate_chainAInspect
Trace the predicate ancestry of a 510(k) device, with each cited predicate's age (how many years old the predicate was when the child cleared). Reveals how AI/ML devices chain to older predicates.
| Name | Required | Description | Default |
|---|---|---|---|
| depth | No | Max ancestry depth (default 4) | |
| k_number | Yes | 510(k) number, e.g. K252148 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description adds context about tracing ancestry and revealing predicate ages and AI/ML chaining. Missing details on safe operation (read-only), auth needs, or return structure.
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, front-loaded with purpose, no 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?
Adequate for a simple tool with documented parameters, but lacks description of output format, which is important since no output schema exists.
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 covers 100% of parameters with descriptions; the tool description adds context about output (predicate age, AI/ML chaining) but does not enhance parameter meaning beyond 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 traces predicate ancestry of a 510(k) device and reveals ages and AI/ML chaining, distinguishing it from sibling tools like device_evidence_lookup or device_risk_lookup.
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 use for predicate chaining, especially for AI/ML devices, but does not explicitly state when to use vs alternatives or when not to use.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
vehicle_risk_lookupAInspect
Look up NHTSA safety history for a vehicle by make, model, and model year. Returns recall campaigns and complaint statistics (crashes, fires, injuries, top components).
| Name | Required | Description | Default |
|---|---|---|---|
| make | Yes | e.g. honda | |
| year | Yes | model year, e.g. 2020 | |
| model | Yes | e.g. civic |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It indicates the tool is a lookup (likely read-only) and lists return values. However, it does not explicitly state that it is non-destructive, idempotent, or require authentication. The description adds some value by specifying output fields (crashes, fires, injuries) but lacks depth on side effects or limitations.
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 sentence that efficiently conveys the tool's action (look up), target (NHTSA safety history), input criteria (make, model, model year), and output (recall campaigns and complaint statistics). No unnecessary words; information is front-loaded.
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 lack of an output schema, the description reasonably covers the return content (recalls, complaints, specific statistics like crashes and injuries). It explicitly lists input requirements. Could be improved with details on output structure or pagination, but for a simple lookup, it is sufficiently complete.
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 each parameter having a brief example (e.g., 'e.g. honda'). The description reinforces the purpose of the three parameters but adds no additional meaning, validation rules, or formatting instructions beyond the schema. Baseline score of 3 is appropriate.
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 function: 'Look up NHTSA safety history for a vehicle by make, model, and model year.' It specifies the resource (NHTSA safety history) and the return content (recall campaigns and complaint statistics). The name 'vehicle_risk_lookup' and sibling tools (e.g., device_risk_lookup) indicate domain specificity, distinguishing it from other 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?
The description explicitly states the required input parameters (make, model, model year) and the output type. It does not provide explicit when-to-use or when-not-to-use guidance, but the context of sibling tools (e.g., device_risk_lookup) implies this is for vehicles. The description implies usage contexts for vehicle safety queries.
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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