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by com.onrender.b2b-contract-parser-api
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Federal AI Governance, OMB M-24-10/M-24-18 & NIST AI RMF 1.0 Compliance Package

generate_ai_governance_nist_ai_rmf_omb_m24
Read-onlyIdempotent

Evaluate AI/ML/LLM solutions proposed for federal and SLED agencies against OMB Memoranda M-24-10 & M-24-18, NIST AI RMF 1.0 (Govern, Map, Measure, Manage), and DoD Responsible AI (RAI) Toolkit, outputting a Safety-Impact Classification & Model Card. Returns a strictly validated AiGovernanceOmbResponse JSON object containing omb_safety_and_rights_impact_overview, nist_ai_rmf_govern_map_measure_manage_matrix, omb_m_24_10_and_m_24_18_minimum_practices_audit, and federal_ai_system_card_and_hitl_safeguards grounded in OMB Memorandum M-24-10, OMB Memorandum M-24-18 (Advancing the Responsible Acquisition of AI in Government), NIST AI 100-1 (AI RMF 1.0), NIST AI 600-1, & DoD CDAO RAI Shield. This is a read-only, stateless, non-destructive GovCon compliance and capture analysis call (requires X-RapidAPI-Key or X-SignalTender-Key; proposed_ai_system_and_agency_use_case_text accepts 15 to 45,000 characters, while target_ai_governance_frameworks and deployment_enclave_and_training_data_policy calibrate the regulatory regime and threshold assumptions). Use this tool when executing Federal AI Governance, OMB M-24-10/M-24-18 & NIST AI RMF 1.0 Compliance Package workflows; use scan_far_dfars_clauses instead for general FAR/DFARS clause flow-down audits, or generate_compliance_matrix for general Section L/M 'shall' requirement extraction.

Input Schema

NameRequiredDescriptionDefault
target_ai_governance_frameworksNoTarget regulatory regime, statutory authority, or evaluation framework (defaults to 'OMB_M_24_10_OMB_M_24_18_NIST_AI_RMF_1_0_NIST_AI_600_1_GenAI_And_DoD_CDAO_RAI_Toolkit').OMB_M_24_10_OMB_M_24_18_NIST_AI_RMF_1_0_NIST_AI_600_1_GenAI_And_DoD_CDAO_RAI_Toolkit
deployment_enclave_and_training_data_policyNoOperational context, threshold parameters, or compliance posture assumptions (defaults to 'Stateless RAG on Agency Tenant (Zero Training on Government CUI/PII; Open-Format Export to Prevent Vendor Lock-In)').Stateless RAG on Agency Tenant (Zero Training on Government CUI/PII; Open-Format Export to Prevent Vendor Lock-In)
proposed_ai_system_and_agency_use_case_textYesPrimary solicitation text, clause excerpts, deliverable schedule, or technical/financial context for Federal AI Governance, OMB M-24-10/M-24-18 & NIST AI RMF 1.0 Compliance Package (15 to 45,000 characters).

Output Schema

NameRequiredDescriptionDefault
statusNoExecution status ('success').
omb_safety_and_rights_impact_overviewNoExecutive determination overview, regulatory citations, and readiness score.
federal_ai_system_card_and_hitl_safeguardsNoStatutory governance protocol and actionable submission verification checklist.
nist_ai_rmf_govern_map_measure_manage_matrixNoStructured line-by-line regulatory audit, classification, or quantitative schedule rows.
omb_m_24_10_and_m_24_18_minimum_practices_auditNoPaste-ready compliance narratives, control specifications, or submission sections.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnly/openWorld=false/idempotent/non-destructive, and the description goes beyond them by disclosing the required auth headers (X-RapidAPI-Key or X-SignalTender-Key) and the stateless execution model. It repeats the annotation-derived safety traits and enumerates output field names even though an output schema exists, which is redundant rather than additive.

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

Conciseness3/5

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

The capability and alternatives are front-loaded, but the description is a single bloated block that repeats the full citation string three times (M-24-10, M-24-18, NIST AI RMF, AI 600-1, DoD RAI) and restates limits already in the schema. Sentences are dense and would benefit from trimming rather than adding.

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?

An output schema exists, so return-value explanation is not required, and the description still names the response type and its top-level sections. Auth requirements, character bounds, defaults, and the routing alternatives cover what an agent needs to invoke it correctly; only the redundant framework recitation holds it back.

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 coverage is 100%, so the baseline applies: all three parameters are documented in the schema itself. The description adds only the calibrating role of target_ai_governance_frameworks and deployment_enclave_and_training_data_policy plus a character range that duplicates the schema's minLength/maxLength.

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 (evaluate) plus resource (AI/ML/LLM solutions for federal/SLED agencies) against named frameworks and a named deliverable (Safety-Impact Classification & Model Card). It explicitly names the two sibling tools it is not (scan_far_dfars_clauses, generate_compliance_matrix), so an agent can route without opening a schema.

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?

Names explicit alternatives with the conditions that select them (FAR/DFARS flow-down audit vs Section L/M 'shall' extraction), which is genuinely useful routing. The positive trigger sentence ('Use this tool when executing Federal AI Governance... workflows') largely restates the title, so the when-to-use side is weaker than the when-not-to-use side.

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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