Skip to main content
Glama

Stratalize Governance

by com.stratalize

get_nist_ai_rmf_requirements

Read-only

Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
function_filterNo

TDQS

A4/5.0
Behavior4/5

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 useful behavioral context by describing the returned content structure (four functions, categories, subcategories, implementation guidance) and providing a concrete example of GOVERN content. This goes beyond the annotations without contradicting them.

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?

The description is relatively long but well-structured, front-loaded with usage scenarios, followed by regulatory context, return content, an example, and a source citation. Every sentence adds value, but it could be trimmed slightly without losing essential information.

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

Completeness4/5

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

The tool is simple (one optional param, no output schema), and the description covers use cases, regulatory alignment, the full structure of the return data, and an example. The only missing piece is clarification of the filter parameter's effect, which is already penalized under parameter semantics. Overall, it's quite complete for an agent to understand the tool's purpose and output.

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?

The description does not mention the function_filter parameter at all, despite schema description coverage being 0%. It says 'Returns all four functions' which could mislead the agent into thinking the filter is inoperative or unnecessary. The schema shows an optional enum filter, but the description fails to explain when or how to use it, leaving a significant gap.

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?

The description clearly identifies the tool's function: returning NIST AI RMF 1.0 requirements with all four functions and implementation guidance. It names the specific resource (NIST AI RMF) and distinguishes it from sibling tools that cover other regulatory frameworks.

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

Usage Guidelines4/5

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

The description explicitly states when to use the tool ('Use when conducting an AI risk management gap assessment...') and provides multiple relevant scenarios. However, it does not mention when not to use it or explicitly compare to alternatives, so it misses the 'explicit exclusions/alternatives' threshold for a 5.

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

All Stratalize Governance toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation5/5

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.

Naming Consistency5/5

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.

Tool Count4/5

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.

Completeness4/5

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.

Resources