Use when verifying a clinician or organization NPI against CMS NPPES before contracting or credentialing. Returns enumeration status, taxonomy, license state, and identity fields from live NPPES lookup. Source: CMS NPPES Registry. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
| Name | Required | Description | Default |
|---|---|---|---|
| npi | No | ||
| state | No | ||
| provider_name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description does not need to cover safety. It adds valuable context: it performs a 'live NPPES lookup,' cites the source (CMS NPPES Registry), and discloses that results are cryptographically attested with a post-quantum signed settlement receipt. No contradiction with annotations.
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?
Three sentences, each earning its place: the first states purpose and timing, the second lists return fields and source, the third adds trusted attestation details. No redundancy or filler; the information is front-loaded and scannable.
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?
Without an output schema, the description should explain return values; it partially does by listing enumeration status, taxonomy, license state, and identity fields. However, it omits parameter semantics, response format, and whether results are single or plural. For a tool with three optional parameters and no output schema, this leaves noticeable 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 should compensate by explaining parameter roles. It implies 'npi' is the identifier but does not clarify the purpose or usage of 'state' or 'provider_name.' The description mentions return fields but not how parameters influence the lookup, leaving significant ambiguity for a schema with no required 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 opens with 'Use when verifying a clinician or organization NPI against CMS NPPES,' naming a specific verb (verify), resource (NPPES), and use context (contracting/credentialing). It also lists return fields (enumeration status, taxonomy, license state, identity fields), distinguishing it from sibling tools focused on benchmarks or recalls.
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 when to use the tool ('before contracting or credentialing') and identifies the data source (CMS NPPES Registry). It does not explicitly name alternatives or when-not-to-use conditions, but the use case is clear and distinct from sibling tools, which are all get_* tools for other data domains.
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.