HiveAudit Readiness
Server Details
Multi-jurisdictional AI compliance readiness scoring with sourced penalty math.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- srotzin/hive-mcp-audit-readiness
- GitHub Stars
- 0
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Tool Definition Quality
Average 4.3/5 across 3 of 3 tools scored.
Each tool targets a clearly distinct function: pricing tiers, compliance readiness scoring, and sanctions screening. No overlap in purpose.
All tools start with 'audit_' and mostly follow a verb_noun pattern ('get_tier_pricing', 'sanctions_screen'). 'readiness_score' lacks a verb but is still descriptive and consistent with the prefix.
Three tools is well-scoped for the server's purpose. Each tool addresses a specific aspect of readiness assessment without redundancy or excess.
The set covers key aspects: pricing, readiness scoring, and sanctions screening. Minor gaps like a full audit report or submission tool are acceptable for a readiness-focused server.
Available Tools
3 toolsaudit_get_tier_pricingAInspect
Get the four published HiveAudit tier prices and bracket thresholds: STARTER ($500, <$500K exposure), STANDARD ($1,500, <$5M), ENTERPRISE ($2,500, <$50M), FEDERAL ($7,500/yr, ≥$50M or federal agency). Returns the tier card mapping plus the trial CTA. No backend call — inlined for offline discovery.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that there is no backend call and the tool is inlined for offline discovery, revealing its non-destructive, zero-latency nature. It also describes the return value ('tier card mapping plus trial CTA'). With no annotations, this adds necessary behavioral context.
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: first lists tiers with details, second describes return, third notes offline nature. No wasted words, front-loaded with key 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?
Given no parameters and no output schema, the description fully explains the tool's purpose, output, and behavioral traits. There are no 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?
The input schema has zero parameters, so the baseline is 4. The description does not need to explain parameters, but it adds value by describing the output and behavior, which is sufficient for this simple tool.
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 specifies the verb 'Get' and resource 'HiveAudit tier prices and bracket thresholds', listing all four tiers with prices and exposure brackets. It clearly distinguishes from the sibling tool 'audit_readiness_score' by describing a different function.
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 mentions 'No backend call — inlined for offline discovery', implying it is safe and instant to call, but does not explicitly state when to use this tool versus alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
audit_readiness_scoreAInspect
Compute a multi-jurisdictional AI compliance readiness score for an organization. Returns penalty exposure (EUR + USD), specific compliance gaps citing the regulation article, recommended audit tier (STARTER/STANDARD/ENTERPRISE/FEDERAL), and the nearest enforcement deadline. Penalty math sources EU AI Act Art 99, Colorado AI Act SB 24-205, CCPA, Cal SB 942, NYC LL 144, HIPAA. Free, no auth, rate-limited 10/IP/hr.
| Name | Required | Description | Default |
|---|---|---|---|
| company | No | Organization name (optional; populates the assessment record). | |
| sectors | No | Industries: ["finance", "healthcare", "employment", "education", "lending", "insurance", "criminal_justice", "biometric", "critical_infrastructure"]. High-risk sectors trigger Annex III scoping. | |
| frameworks | Yes | Regulations to score against: ["eu_ai_act", "co_ai_act", "ccpa", "ca_sb942", "nyc_ll144", "hipaa", "gdpr", "nist_ai_rmf"]. | |
| agent_count | Yes | Number of distinct AI agents in production. | |
| jurisdictions | Yes | Where the system operates: ["EU", "US-CO", "US-CA", "US-NY", "US-TX", ...]. Drives which regulations apply. | |
| data_volume_records | Yes | Total records processed (drives CCPA / GDPR scoping). | |
| organization_country | Yes | ISO 3166-1 alpha-2 country code of the organization headquarters (e.g. "US", "DE", "FR", "GB"). | |
| monthly_inference_calls | Yes | Inference call volume per month (drives tier selection). |
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 for behavioral disclosure. It states the tool is free, requires no authentication, and is rate-limited. It also reveals the source of penalty calculations (EU AI Act Art 99, etc.). While not explicitly stating the tool is read-only, the output description implies no modification of data.
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 moderately concise, front-loading the main purpose and then detailing outputs and sources. It is not overly verbose, though it could be slightly more structured (e.g., bullet points or paragraphs). Overall, it conveys necessary information efficiently.
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's complexity and the absence of an output schema, the description provides a good summary of what is returned. It mentions rate limits. However, it lacks details on error handling or validation of inputs, and it doesn't explain potential prerequisites (e.g., what constitutes a valid jurisdiction). Still, it is largely complete for an agent.
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%, so each parameter has a description. The tool description adds information about output and penalty sources but does not further elaborate on parameter meanings beyond what the schema provides. This meets the baseline of 3.
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 computes a multi-jurisdictional AI compliance readiness score, listing specific outputs (penalty exposure, compliance gaps with article references, audit tier, enforcement deadline). This is a specific verb+resource, and it distinguishes itself from the sibling tool 'audit_get_tier_pricing'.
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 implicitly indicates usage for compliance scoring and explicitly states it is free, no auth, and rate-limited. However, it does not provide explicit when-not-to-use guidance or contrast with the sibling tool, though the sibling's name suggests it handles pricing, leaving readiness scoring as the natural use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
audit_sanctions_screenAInspect
Screen one or more entities (organization or individual) against the OpenSanctions consolidated sanctions database. Calls the OpenSanctions free public Match API (https://api.opensanctions.org). No API key required for the public endpoint. Returns per-entity matches with score, dataset (e.g. eu_fsf, us_ofac_sdn), and source URL. Use this as the OFAC / EU FSF / UN consolidated screen step inside a HiveAudit Readiness assessment. Source: https://www.opensanctions.org.
| Name | Required | Description | Default |
|---|---|---|---|
| entities | Yes | Entities to screen. Up to 100 per call. | |
| threshold | No | Match score threshold (0–1). Default 0.7. Returns only matches ≥ threshold. |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses external API call, no API key required, output structure (score, dataset, source URL), and a limit of 100 entities per call. No annotations provided, so the description carries the full burden and does so comprehensively.
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?
Concise and well-structured: states purpose, API details, usage context, and source with no unnecessary 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?
For a tool with 2 parameters and no output schema, the description covers purpose, API behavior, output format, and usage context. It is complete enough for an agent to understand and invoke correctly.
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%, so baseline is 3. Description adds minimal extra detail beyond schema (e.g., threshold default 0.7, up to 100 entities), but does not significantly enhance parameter understanding.
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?
Description clearly states the action ('Screen... entities') and the specific database (OpenSanctions consolidated sanctions database). It distinguishes itself from sibling tools (audit_get_tier_pricing, audit_readiness_score) which serve different purposes.
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?
Explicitly says to use this as part of a HiveAudit Readiness assessment, providing context. Lacks explicit when-not-to-use or alternatives, but siblings are not alternative 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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