FDA 510(k) device clearance history from OpenFDA by device name. Returns K numbers, applicants, decisions, and receipt dates. Use for medtech competitive intelligence, regulatory pathway research, and supplier qualification. Source: FDA 510(k) database. $0.10 standard. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
| Name | Required | Description | Default |
|---|---|---|---|
| device_name | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the tool as read-only and non-destructive. The description adds valuable context about the data source (FDA 510(k) database), the settlement receipt attestation, and a verification URL, giving extra trust and provenance details beyond the annotations. It does not cover pagination or error behavior, but the added context is meaningful.
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, front-loaded with the core function, then returns fields, use cases, and trust/verification details. Every sentence earns its place, and there is no redundant or filler content.
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 one-parameter tool with no output schema, the description is complete: it explains what the tool returns, provides use cases, and includes trust/verification info. The annotations cover safety, so no further behavioral disclosure is necessary.
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, but the description states 'by device name,' which clearly identifies the sole parameter's semantic role. Since there is only one parameter and no enums, this compensation is sufficient, though it does not add format or syntax details beyond the schema's min/max length.
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 retrieves FDA 510(k) device clearance history by device name, listing specific return fields (K numbers, applicants, decisions, receipt dates). This distinguishes it from sibling tools like get_fda_recall_history, which cover recalls rather than clearances.
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 explicit use cases: medtech competitive intelligence, regulatory pathway research, and supplier qualification. It does not spell out when-not-to-use or name alternatives, but the context is clear enough for an agent to select it appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Several tools have overlapping or nearly identical purposes, such as get_drug_adverse_events and get_openfda_adverse_events both pulling FAERS data, get_drug_recall_status and get_fda_recall_history both handling recalls, and get_cms_star_rating overlapping with get_hospital_care_compare_quality. The distinctions rely on subtle source differences or output formatting, making it easy for an agent to select the wrong tool.
All 29 tools follow a strict get_<domain>_<descriptor> pattern, with snake_case throughout. The naming is highly predictable and consistent, which helps agents infer functionality even if they haven't seen a specific tool before.
29 tools is on the heavy side for a healthcare data server, but the breadth of healthcare domains (pharma, providers, payers, supply chain, quality) partially justifies the count. However, the presence of overlapping tools suggests the count could be reduced by consolidation without losing coverage.
The tool surface covers a wide range of healthcare operations: financial benchmarks, drug safety, compliance, quality ratings, provider verification, supply chain, and value-based care. Minor gaps exist (e.g., no specific patient outcome benchmark tool), but overall the core workflows for healthcare intelligence and benchmarking are well represented.