Use when credentialing a healthcare provider or verifying vendor eligibility against federal exclusions. Screens HHS OIG LEIE by provider name or NPI with list version and as-of date in the attested payload. Source: HHS OIG LEIE synced exclusions. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
| Name | Required | Description | Default |
|---|---|---|---|
| npi | 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, and the description adds valuable context: the source is a 'synced' HHS OIG LEIE list, and results come with a 'post-quantum signed settlement receipt' plus list version/as-of date in the attested payload. It does not describe edge-case behavior for missing inputs, but the attestation details go beyond 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?
The description is three sentences and front-loaded with the primary use case. The source and cryptographic attestation details are relevant, though the 'Source: HHS OIG LEIE synced exclusions' sentence is slightly redundant with the first sentence but adds the 'synced' nuance.
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?
The description covers purpose, source, and attestation, but there is no output schema and no mention of response structure, error conditions, or whether parameters are mutually exclusive. Since neither parameter is required in the schema, the tool's behavior when no query is provided is unclear, making the description incomplete for an exclusion screening 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 description must compensate. It names both parameters ('provider name or NPI') and implies they are query options, which is useful. However, it does not clarify whether at least one is required, how both are handled together, or the matching behavior, leaving ambiguity that the schema (with no required fields) does not resolve.
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: 'Screens HHS OIG LEIE by provider name or NPI' and opens with a specific use case ('Use when credentialing a healthcare provider or verifying vendor eligibility'). This distinguishes it from sibling tools like get_ofac_sanctions_screening and get_sam_exclusion_screening by naming the exact federal exclusion list.
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?
It provides explicit guidance on when to use the tool: 'Use when credentialing a healthcare provider or verifying vendor eligibility against federal exclusions.' However, it does not explicitly mention when not to use it or mention alternatives, though the HHS OIG focus clearly differentiates it from similar exclusion screening tools.
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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Every tool targets a distinct regulatory domain or data source—from OFAC sanctions to CRA ratings to NIST AI RMF—with clear boundaries. Even the three screening tools (OFAC, OIG, SAM) differ by governing agency and list, and their descriptions explicitly disambiguate them.
All tools follow the consistent lower_snake_case pattern 'get_<domain>_<focus>', such as get_ofac_sanctions_screening and get_us_state_ai_legislation. There are no mixed conventions, vague verbs, or unexpected abbreviations.
18 tools is slightly above the ideal 3-15 range but appropriate for a broad governance data server covering federal, state, and international regulatory sources. Each tool corresponds to a meaningful dataset, so the count feels justified rather than padded.
The surface covers a wide array of governance and compliance domains, including AI regulation, financial enforcement, sanctions, and legal screening, with no critical dead ends for typical lookups. However, it lacks some common regulatory areas (e.g., SEC, HIPAA, GDPR) and offers only read-only access, which is acceptable but not exhaustive.