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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
asyncNoIf 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.
sectorNoPrimary sector of application
riskLevelYesSelf-assessed risk level of the AI system
intendedUseYesPrimary and secondary use cases of the AI system
documentationNoList of provided documentation types (e.g., 'technical', 'ethical', 'data')
systemDescriptionYesDetailed description of the AI system including purpose, architecture, and data sources

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
assessmentNo

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds behavioral context about returning structured feedback with citations and recommendations, which goes beyond the annotations. No contradiction.

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 a single paragraph of five sentences, front-loading the purpose and then listing inputs and outputs. It is concise and well-structured, though could be slightly tighter.

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?

Given the tool has 6 parameters and an output schema, the description covers the main purpose and outputs adequately. It does not explain the async parameter or output schema details, but the schema and annotations fill those gaps. Complete enough.

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 description adds no additional meaning beyond the schema. The description mentions accepting system descriptions and use cases, but this is already clear from the schema. Baseline 3.

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 simulates EU AI Act regulatory sandbox submissions, specifies it is legal-focused, and distinguishes it from sibling tools like ai_act_incident_response and ai_act_training_data_audit 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 Guidelines4/5

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

The description implies use for sandbox submissions, but does not explicitly state when not to use it or mention alternatives. However, the context is clear and the purpose is well-defined, earning a 4.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

Resources