mcp-knowledge

ai_act_incident_response

Read-onlyIdempotent

Generates EU AI Act incident response playbooks with regulator notification templates for risk management teams. Inputs include incident severity, AI system type, and affected stakeholders. Outputs structured playbook steps, regulator notification drafts, and compliance checklists. Essential for high-risk AI system breaches requiring formal EU notification — pass async:true REQUIRED to avoid x402 timeout. Keywords: AI Act compliance, incident response, regulator notification, risk management, ISO 27035, NIST SP 800-61.

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.
`severity`	Yes
`incident_type`	Yes
`ai_system_type`	No
`incident_description`	No
`affected_stakeholders`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`next_steps`	No
`playbook_steps`	No
`compliance_checklist`	No
`regulator_notification`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds value by noting the async requirement and describing outputs (structured playbook steps, notification drafts, compliance checklists), which supplements the annotations without contradiction.

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 two sentences with an additional keyword tag. It is front-loaded with the main purpose and efficient, containing no redundant 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?

Given the tool has 6 parameters and an output schema (not shown), the description adequately covers purpose, usage context, and key requirements. However, it could better complement the schema by explaining all parameters and their roles.

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 description coverage is only 17%, so the description should compensate. It mentions 'incident severity, AI system type, and affected stakeholders' as inputs, but omits incident_type and incident_description. This adds partial context but is incomplete for the 6 parameters.

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 generates EU AI Act incident response playbooks with regulator notification templates for risk management teams. Verb ('Generates') and resource ('playbooks') are specific, and the scope is well-defined, distinguishing it from siblings like 'ai_act_sandbox_regulatory_sandbox' and 'ai_act_training_data_audit'.

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 specifies that the tool is 'Essential for high-risk AI system breaches requiring formal EU notification' and includes a critical usage note about passing 'async:true' to avoid timeouts. While it doesn't explicitly mention when not to use or list alternatives, the context is strong enough for correct invocation.

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.

ai_act_incident_response

Input Schema

Output Schema

TDQS

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TDQS

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