gapup-mcp

ai_act_sandbox_regulatory_sandbox

Read-onlyIdempotent

A legal-focused tool for simulating EU AI Act regulatory sandbox submissions. Provides structured feedback on compliance, risk levels, and required documentation based on EUR-Lex and OECD AI Policy Observatory sources. Accepts AI system descriptions, intended use cases, and technical specifications as input. Returns detailed assessment with warnings, citations, and actionable recommendations for legal teams and AI developers.

Input Schema

TableJSON Schema

Name	Required	Description
`async`	No	If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
`sector`	No	Primary sector of application
`riskLevel`	Yes	Self-assessed risk level of the AI system
`intendedUse`	Yes	Primary and secondary use cases of the AI system
`documentation`	No	List of provided documentation types (e.g., 'technical', 'ethical', 'data')
`systemDescription`	Yes	Detailed description of the AI system including purpose, architecture, and data sources

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`assessment`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds value by detailing the nature of feedback (warnings, citations, recommendations) and the sources used (EUR-Lex, OECD). No contradictions with annotations.

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?

The description is four sentences, each contributing essential information. It is front-loaded with the core purpose and efficiently covers inputs, outputs, and target audience without waste.

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 the tool's complexity (6 params, output schema exists), the description adequately covers the tool's purpose, inputs, and output nature. The return value is described ('detailed assessment with warnings, citations, and actionable recommendations'), and the output schema addresses formal structure.

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 schema already documents parameters well. The description provides general context (e.g., 'technical specifications') but does not add significant meaning beyond what the parameter descriptions offer. 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 the tool's purpose: simulating EU AI Act regulatory sandbox submissions. The verb 'simulating' and resource 'regulatory sandbox submissions' are specific. It distinguishes from sibling AI Act tools (e.g., ai_act_incident_response) by focusing on sandbox simulation.

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 description implies usage for legal teams and AI developers preparing sandbox submissions, but it does not explicitly state when to use this tool versus related alternatives (e.g., ai_act_incident_response). No exclusions or when-not guidance is provided.

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.

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