Use when verifying federal contractor or grantee eligibility against SAM.gov exclusions. Queries SAM Exclusions API by entity name, UEI, or CAGE with conservative name matching and live attestation metadata. Source: SAM.gov Exclusions API. Cryptographically attested with a post-quantum signed settlement receipt. Verify at trust.stratalize.com/verify.
| Name | Required | Description | Default |
|---|---|---|---|
| uei | No | ||
| cage_code | No | ||
| entity_name | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral details: conservative name matching, live attestation metadata, and cryptographic attestation with a verification link, which go beyond the basic read-only safety profile.
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, front-loaded with the usage statement, then functionality, then source/attestation. Every sentence earns its place with no wasted words.
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?
With no output schema, the description hints at response features (live attestation metadata, cryptographic receipt) but doesn't explicitly describe the main result (e.g., exclusion status or list). It also omits a note that at least one parameter should be provided, though the OR phrasing implies it. Overall fairly complete for a read-only 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 0%, so the description must compensate. It mentions all three parameters (entity name, UEI, CAGE) and implies OR semantics ('by entity name, UEI, or CAGE'), plus 'conservative name matching' gives additional meaning to entity_name. However, it doesn't clarify parameter combinations or constraints beyond the schema's maxLength.
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: verifying federal contractor or grantee eligibility against SAM.gov exclusions. It specifies the resource (SAM.gov Exclusions API) and the query keys (entity name, UEI, CAGE), distinguishing it from sibling screening tools like OFAC and OIG.
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?
Starts with 'Use when verifying...' providing explicit context for when to use the tool. However, it does not mention when not to use it or explicitly name alternatives, though sibling tools imply other screening sources.
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.
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.