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Generate AI Passport

rai_passport_generate
Read-onlyIdempotent

Generate a verifiable AI Passport with trust scores, compliance status, bias summary, and cryptographic hash. Enables Procurement and Legal to assess third-party AI vendor risk.

Instructions

Generate a verifiable AI Passport for a model — a tamper-evident governance card containing trust scores, compliance status, bias summary, and a cryptographic verification hash. Used by Procurement/Legal for third-party AI vendor risk assessment.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerYes
use_caseNogeneral
model_nameYes
bias_summaryNo
privacy_summaryNo
security_summaryNo
trust_dimensionsYesTrust dimension scores (0-1 each)
compliance_summaryNo
hallucination_summaryNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.2.6

TDQS

B3.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the tamper-evident nature and cryptographic hash, which is useful behavioral context, but does not reconcile why a 'generate' tool is read-only, nor mention auth, persistence, or whether the passport is stored.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tightly written sentences with the artifact definition front-loaded and the audience appended. Little wasted text, though the second sentence is context rather than operational instruction.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 9 parameters, 11% schema coverage, and no output schema, the description should do more. It partially covers output content (useful since no output schema exists) but leaves half the input parameters undocumented, so an agent cannot construct a full call confidently.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 11%, so the description carries the burden of documenting parameters, and it largely fails. It references 'trust scores', 'compliance status', and 'bias summary' which loosely map to trust_dimensions/compliance_summary/bias_summary, but leaves model_name, provider, use_case, privacy_summary, security_summary, and hallucination_summary unexplained.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb ('Generate') and resource ('AI Passport'), then enumerates the artifact's contents (trust scores, compliance status, bias summary, verification hash), so the agent knows exactly what this produces. It is fairly distinct from siblings like rai_trust_score or rai_compliance, but never names them, so it stops short of explicit differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Used by Procurement/Legal for third-party AI vendor risk assessment' line implies a usage context, which is better than nothing. However, it gives no when-to-use/when-not guidance or alternative routing (e.g., versus rai_trust_score, rai_compliance, or rai_audit_summary), leaving the agent to infer selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.