AI Incident Reporting MCP
This server provides tools for multi-regime AI incident classification, regulatory reporting, and compliance attestation across frameworks like EU AI Act (Art. 73), DORA, NIS2, and GDPR.
Classify Incidents (
classify_incident): Analyze an incident against multiple regulatory regimes simultaneously. Provide details such as entity type, incident description, whether personal data was breached, economic impact, affected people count, and flags for high-risk AI, financial entities, NIS2 entities, and frontier model developers. Returns a multi-regime decision tree indicating which reporting clocks start, who to notify, on what forms, and by when.List Regime Clocks (
list_regime_clocks): Retrieve a full list of reporting deadlines and relevant regulatory authorities for every compliance regime covered (EU AI Act, DORA, NIS2, GDPR, etc.), providing a quick reference for incident response timelines.Generate Signed Attestations (
sign_incident_response_attestation): Produce a cryptographically signed (HMAC-SHA256) incident response attestation (Pro+ tier) capturing which regimes were notified, SLA adherence, and response scores. Generates a verifiable URL that auditors can use as independent evidence of post-incident regulatory compliance.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@AI Incident Reporting MCPReport a serious AI incident under EU AI Act Article 73"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Ai Incident Reporting MCP
mcp-name: io.github.CSOAI-ORG/ai-incident-reporting-mcp
AI Incident Reporting Compliance MCP
⚖️ Built by MEOK AI Labs / CSOAI. Need this applied to your system fast? Book a 30-min Founder Office Hour (£29) → https://meok.ai/work · Full governance platform → https://meok.ai
Multi-regime AI incident classification + reporting MCP (EU AI Act Art 73, DORA, NIS2, GDPR breach)
Multi-regime AI incident classification + reporting MCP (EU AI Act Art 73, DORA, NIS2, GDPR breach). MIT
🚀 Quick Start
# Install via pip
pip install ai_incident_reporting_mcp
# Or install via Smithery
npx -y @smithery/cli@latest install ai-incident-reporting-mcp --client claudeRelated MCP server: DORA Compliance MCP
✨ Features
Article 73 incident reporting
5-clock broadcast
Escalation workflows
Audit trail
Regulatory submission
📖 Documentation
🛡️ Compliance
This MCP server is built with EU AI Act compliance built-in:
✅ Article 9 — Risk Management System
✅ Article 13 — Transparency & Instructions for Use
✅ Article 15 — Bias Detection & Testing
✅ Article 26 — FRIA Support (where applicable)
✅ Article 50 — AI Content Watermarking (where applicable)
Need help getting compliant? Book a free 15-min diagnostic →
🏢 Enterprise
Need custom development, SLA guarantees, or white-label deployment?
Pro: $99/mo — Full MCP suite + EU AI Act tracking
Enterprise: $499/mo — Custom dev + SLA + Dedicated support
View Pricing → | Contact Sales →
🤝 Part of the MEOK Ecosystem
This server is part of the MEOK AI Labs ecosystem — 300+ MCP servers for sovereign AI governance.
Domain | Purpose |
EU AI Act compliance marketplace | |
AI safety & monitoring | |
Sovereign AI platform | |
Legacy modernization |
📜 License
MIT © CSOAI-ORG
Full catalogue + Anthropic Registry verify links: meok.ai/anthropic-registry
Protocol coverage + Universal PAYG
This MCP is part of MEOK's 47-MCP fleet that bridges every active agent-interop protocol and 30+ regulatory frameworks. See the full coverage matrix at meok.ai/protocols.
Agent interop protocols supported (8 live):
✅ MCP (Anthropic) — native
✅ A2A (Google + Linux Foundation, absorbed IBM ACP Sept 2025)
✅ IBM ACP — covered via A2A merge
◐ Stripe ACP (Agentic Commerce Protocol) — Q3 bridge via agent-commerce-protocol-mcp
◐ AP2 (Google Agent Payments) — partial via agent-commerce-payments-mcp
◐ x402 (Coinbase HTTP 402) — partial via api.meok.ai gateway
→ OASF / AGNTCY (Cisco Outshift + Linux Foundation) — Q3 bridge
👁 ANP (Cisco Agent Network) — watch-list
Pricing options:
Option | Price | Best for |
Self-host (this MCP) | £0 — MIT | Devs |
This MCP Starter | £29/mo | One-MCP teams |
This MCP Pro | £79/mo | Production + 24h SLA |
£29/mo + £0.0002/call | Spiky usage across many MCPs | |
Substrate bundle (this category) | £99-£499/mo | A whole pack |
£1,499/mo | All 47 MCPs, 500K calls |
Each tier above the free self-host adds HMAC-signed attestations verifiable at
verify.meok.ai. Linux Foundation governance on the A2A spine means EU regulated
buyers can deploy without vendor-lock-in objections.
💸 Try MEOK in 30 seconds — instant buy ladder
Tier | Price | What you get | Stripe |
Smoke test | £1 | Signed sample MCP-Hardening report + Article 50 PDF | |
Quick Kit | £9 | EU AI Act Article 50 implementation guide (C2PA + EU-Icon) | |
Founder Call | £29 | 30-min 1-on-1 with the founder |
Refundable. UK Stripe — VAT-clean. Builds on the 81-MCP MEOK fleet. Verify any signed report at https://meok.ai/verify.
Configuration
Add to your claude_desktop_config.json (Claude Desktop) or your MCP client config:
{
"mcpServers": {
"ai-incident-reporting-mcp": {
"command": "uvx",
"args": ["ai-incident-reporting-mcp"]
}
}
}Or: pip install ai-incident-reporting-mcp then run the ai-incident-reporting-mcp command (stdio transport).
Examples
Once configured, ask your assistant, for example:
"Use
classify_incidentto …""Use
list_regime_clocksto …""Use
sign_incident_response_attestationto …"
See also
MEOK compliance MCP fleet:
meok-eu-ai-act-art-26-fria-mcp, nis2-compliance-mcp
Available Tools
3 toolsclassify_incidentB
Classify an incident against every regime in scope. Returns the multi-regime decision tree: which clocks start, who to notify, on what form, by when.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_type | Yes | ||
| incident_description | Yes | ||
| affected_people_count | No | ||
| duration_hours | No | ||
| economic_impact_eur | No | ||
| personal_data_breached | No | ||
| cross_border | No | ||
| is_high_risk_ai | No | ||
| is_financial_entity | No | ||
| is_nis2_entity | No | ||
| is_iso42001_certified | No | ||
| is_frontier_model_developer | No | ||
| api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states what the tool returns but does not disclose side effects (e.g., whether it modifies state), authentication needs, rate limits, or error behavior. The mention of 'which clocks start' hints at possible side effects, but it is ambiguous.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: two sentences that convey purpose and output without extraneous text. However, it could benefit from a more structured format to highlight key points.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 13 parameters and no param descriptions, the description is insufficient for proper use. It fails to explain which regimes are in scope, how inputs affect classification, or provide examples. An output schema exists but is not described, so the agent cannot infer return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. However, it does not explain any parameters or their expected values. Parameter titles are somewhat clear, but without description, an agent may misinterpret fields like entity_type or incident_description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'classify', the resource 'incident', and the scope 'against every regime in scope'. It also specifies the output as a multi-regime decision tree with details on clocks, notifications, etc. This distinguishes it from siblings like list_regime_clocks and sign_incident_response_attestation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives. While the purpose is clear, there is no mention of prerequisites, when not to use it, or how it relates to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_regime_clocksB
List the reporting clocks + authorities for every regime this MCP covers.
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It mentions 'reporting clocks + authorities' but gives no details about read-only nature, authentication requirements (api_key optional but unexplained), rate limits, or side effects. The description fails to add sufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at one sentence, front-loading the verb and object. Every word earns its place. However, it could be slightly expanded without harming conciseness to cover parameter usage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that an output schema exists (not shown), the description need not detail return values. However, it lacks context about what 'regimes' are, prerequisites for the optional api_key, and any behavioral caveats. For a simple list tool, the coverage is adequate but not thorough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (api_key) with 0% description coverage. The tool description does not mention the parameter, its purpose, or when to provide it. The description adds no meaning beyond the schema's bare structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and the resource ('reporting clocks + authorities') with explicit scope ('every regime this MCP covers'), effectively differentiating it from sibling tools like classify_incident and sign_incident_response_attestation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives, but the tool's name and description make its purpose clear enough given the distinct siblings. A statement about prerequisites or context would improve the score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
sign_incident_response_attestationC
Generate a cryptographically signed AI incident-response attestation (Pro+).
Captures: which regimes were notified, within which SLAs, what the response score was. Auditors consume the verify_url as evidence of post-incident compliance.
| Name | Required | Description | Default |
|---|---|---|---|
| entity_name | Yes | ||
| incident_id | Yes | ||
| response_score | Yes | ||
| regimes_notified_csv | No | ||
| findings_csv | No | ||
| include_pdf_base64 | No | ||
| api_key | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states it generates a signed attestation and captures specific data, but does not disclose authentication needs, potential side effects, error behaviors, or whether it requires special permissions. The mention of 'Pro+' hints at licensing but is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences plus a bullet-style list, concise and to the point. It avoids unnecessary detail, but could benefit from slightly better structure to separate what it does from what it captures.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters, no schema descriptions, no annotations, and an output schema, the description is insufficient. It covers high-level purpose and output but fails to explain parameter semantics, usage context, or how it fits with siblings, leaving significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 7 parameters with 0% description coverage, and the tool description does not mention any parameter. This leaves the meaning of each parameter entirely unclear, forcing agents to guess.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool generates a signed attestation for incident response, specifying it captures regimes notified, SLAs, and response score. This distinguishes it from classification (classify_incident) and listing (list_regime_clocks), but does not explicitly differentiate.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use after incident response for compliance evidence, but gives no explicit when-to-use or when-not-to-use guidance, nor any mention of alternatives like classify_incident or list_regime_clocks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
v1.1.2- First observed
classify_incident - First observed
list_regime_clocks - First observed
sign_incident_response_attestation
TDQS
Scored across 3 tools
Each tool targets a distinct operation: classification, listing clocks, and signing attestations. No overlap or ambiguity.
All tools follow a consistent verb_noun pattern in snake_case, making them predictable and easy to distinguish.
Three tools is slightly lean but appropriate for the narrow domain of AI incident reporting compliance. Each tool serves a clear purpose without redundancy.
The toolset covers the full workflow: classification against regimes, awareness of reporting clocks, and generation of signed attestations for auditors. No obvious gaps.
Maintenance
Related MCP Connectors
EU compliance corpus across 8 frameworks (NIS2, DORA, AI Act, ISO 27001 + more) via MCP.
IncidentOracle - 12-tool incident management MCP: triage, BaFin DORA reporting, RCA.
AI governance MCP server for EU AI Act compliance and jurisdiction verification
10,065 source-verified compliance nodes, 39 pillars, 25 MCP tools (EU AI Act, GDPR, NIST, MITRE).
Related MCP Servers
- AlicenseAqualityBmaintenanceNIS2 Compliance - MCP server providing AI-powered tools and automation by MEOK AI Labs766 PyPIMIT
- AlicenseAqualityCmaintenanceDORA Compliance - MCP server providing AI-powered tools and automation by MEOK AI Labs9378 PyPI1MIT
- AlicenseAqualityDmaintenanceMCP server for EU AI Act compliance, providing risk classification of AI features and Article 50 transparency notices.21Apache 2.0
- FlicenseNot gradedqualityCmaintenanceUK AI regulation compliance MCP server enabling AI regulation tracking, risk classification, and impact assessment for AI systems deployed in the UK.37 PyPI-