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Get an AI Governance Unit

get_governance
Read-onlyIdempotent

Get one AI governance unit by slug (includes scope, key requirements, implementable controls, a checklist and common pitfalls). Use this once search or list_governance has given you a slug.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesGovernance unit slug, e.g. 'eu-ai-act'.
localeNoLanguage of the returned body. Default: en.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNo
bodyNo
nameNo
slugYes
tagsNo
domainYes
localeNo
statusNo
aliasesNo
api_urlYes
localesNo
relatedYes
summaryNo
updatedYes
versionYes
categoryYes
evidenceYesEvidence-First provenance: weight claims by this.
fallbackNo
featuredNo
patternsNo
knowledgeNo
frameworksNo
referencesYes
how_to_citeNoThe citation, already formed — use it verbatim.
resource_uriNo
technologiesNo
canonical_urlYes
resolved_localeNo
requested_localeNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedOutput schema / properties / how_to_cite
      Added value: +{
      +  "description": "The citation, already formed — use it verbatim.",
      +  "type": "string"
      +}
  2. First observed

TDQS

A4.3/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety and repeat-call profile is fully covered without the description. The description adds a preview of the returned content, but since a structured output schema exists this is largely redundant; it says nothing about error behavior for unknown slugs or any auth/rate considerations.

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

Conciseness5/5

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

Two sentences, zero filler. The purpose and payload preview come first, then the prerequisite, so the most decision-relevant information is front-loaded.

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

Completeness5/5

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

For a single-resource fetch with strong annotations, a 100%-covered schema, and an existing output schema, the description supplies everything an agent needs: what it fetches, what it returns, and when to reach for it. No material gap remains.

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

Parameters3/5

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

Schema description coverage is 100% and both parameters (slug, locale enum) are documented in the schema with an example and default. The description only restates 'by slug' and omits the locale parameter entirely, so it adds no meaning beyond the schema — the baseline 3 for fully covered params is correct.

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

Purpose5/5

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

States a specific verb+resource ('Get one AI governance unit by slug') and even previews the payload's content (scope, requirements, controls, checklist, pitfalls). This clearly separates it from list_governance (plural, listing) and search, so an agent can pick it without opening schemas.

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

Usage Guidelines5/5

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

Explicitly sequences the workflow: 'Use this once search or list_governance has given you a slug.' This names the alternative tools and the condition under which this one is the right call, leaving nothing to inference.

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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