gapup-mcp

ai_governance_full_report_async

Read-only

Audit EU AI Act complet (Règlement UE 2024/1689) — implémentation native audit-grade. Classifie le système IA selon les 4 tiers de risque (unacceptable/high_risk/limited_risk/minimal_risk/gpai) sur la base de l'Annexe III et de l'Article 5. Produit : (1) classification tier + justification + articles applicables, (2) checklist conformité Articles 9-15 + 50 + 53-55, (3) gaps documentation Annexe IV, (4) mapping ISO 42001, (5) deadlines EU AI Act 2025-2029, (6) estimation coût et effort, (7) top 10 recommandations P0/P1/P2. Retourne immédiatement (<300ms) un job_id. Poller avec ai_governance_full_report_result(job_id) après eta_seconds (~90s). Cache 7 jours pour inputs identiques. Async tool — register a webhook via webhooks_manage(register, url, [job.completed]) to receive callbacks instead of polling. Faster + lighter. DISCLAIMER : non substitutif à un avis juridique professionnel.

Input Schema

TableJSON Schema

Name	Required	Description
`company_size`	No	Taille entreprise : startup (≤50), smb (51-250), mid (251-1000), large (1001-5000), enterprise (>5000)
`data_sources`	No	Sources de données utilisées par le système IA
`affected_persons`	No	Catégories de personnes affectées par les décisions du système (ex: candidats, employés, clients)
`geographic_scope`	No	Zones géographiques de déploiement (ex: 'EU', 'France', 'Global')
`intended_purpose`	Yes	Finalité prévue du système IA : à quoi sert-il concrètement
`deployment_context`	No	Contexte de déploiement : interne (usage employés), public, B2B, B2C
`ai_system_description`	Yes	Description détaillée du système IA : ce qu'il fait, comment il fonctionne, quelles décisions il prend

Output Schema

TableJSON Schema

Name	Required	Description
`job_id`	Yes	Identifiant unique du job — passer à ai_governance_full_report_result
`status`	Yes
`eta_seconds`	Yes	Durée estimée avant disponibilité du résultat
`submitted_at`	Yes	Timestamp ISO-8601 de soumission

TDQS

A4.6/5.0

Behavior5/5

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

Discloses async nature (<300ms return), caching duration (7 days), and disclaimer. No contradiction with annotations; readOnlyHint aligns with non-mutating audit.

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?

Well-structured with bullets but slightly verbose. Could be more concise while retaining key info.

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?

Given 7 parameters and output schema, description thoroughly explains async workflow, caching, webhooks, and disclaimer. No gaps.

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% with descriptions; description adds no extra parameter detail. Baseline of 3 is appropriate.

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 states it audits EU AI Act, classifies risk tiers, and lists 7 specific outputs. It distinguishes from sibling tool ai_governance_full_report_result by noting async polling.

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 explains when to use: submit async and poll with result tool or register webhook. Provides alternatives (polling vs callback) and mentions caching behavior.

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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TDQS

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

Resources

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