Connectez Accident Investigation Prover à ChatGPT et Claude

• 01 / 02Générer des recommandations
• 02 / 02Cartographier les chaînes causales

Jour le plus rapide

763ms

Jour le plus lent

1047ms

Tendance sur 14 jours

En ralentissement+17%

Accident Investigation Prover Connector

Tout est prêt. Choisissez votre client MCP et suivez les instructions de configuration.

Lien du connecteurhttps://edge.vinkius.com/vk_preview_lW5eq02uyQWDJiNGsxX901icN7Fl8AQ8SwMOpToG/mcpCopier

Claude Desktop

Suivez les étapes ci-dessous pour vous connecter en quelques secondes.

1. 1In Claude Desktop, open Settings → Connectors.
2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
3. 3Click Add and start a new chat — Accident Investigation Prover capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopier

{
  "mcpServers": {
    "accident-investigation-prover-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_lW5eq02uyQWDJiNGsxX901icN7Fl8AQ8SwMOpToG/mcp"
    }
  }
}

• Claude
• ChatGPT
• Cursor
• VS Code
• Windsurf
• Claude Code
• JetBrains
• Cline

Les instructions pas à pas pour chaque client sont dans le guide. Comment connecter

Configuration guidée pour Claude ? Configurer Accident Investigation Prover avec Claude et les autres IA

Voyez tous les clients IA avec lesquels ce connector fonctionne ↑

• 01

  Enquêteurs en sécurité aérienne

  Pour mener des investigations qui dépassent la simple attribution de faute et qui identifient les failles structurelles.

• 02

  Responsables de la conformité (Compliance)

  Pour s'assurer que les rapports d'accidents respectent les méthodologies ICAO Annex 13 et NTSB.

• 03

  Ingénieurs de sûreté (Safety Engineers)

  Pour effectuer des analyses de risque approfondies en intégrant les facteurs humains et organisationnels.

• 01

  Does this MCP just tell me who was at fault?

  No. The MCP is designed to prevent assigning blame. It focuses on the systemic failure. It forces the analysis to look at organizational decisions, like scheduling pressure or training gaps, rather than just pilot error.

• 02

  What kind of data does it need to run?

  It requires highly specific data sources, including FDR parameters, CVR transcripts with timestamps, ATC recordings, and detailed maintenance logs. Narrative descriptions alone are insufficient.

• 03

  What is the difference between this and a standard AI prompt?

  A standard prompt accepts a single conclusion. This MCP forces the AI to use the NTSB/ICAO methodology, demanding a causal chain, four-level human factor classification, and evidence correlation across multiple sources.

• 04

  Can it analyze non-aviation incidents?

  The MCP is built around aviation standards (ICAO Annex 13). Its capabilities and methodologies are specific to aviation safety and accident investigation.

• 05

  What does 'HFACS' mean?

  HFACS stands for Human Factors Analysis and Classification System. The MCP uses it to classify every contributing factor into one of four levels: unsafe acts, preconditions, supervision, or organizational issues.

• 06

  How does this prevent 'pilot error' as a root cause conclusion?

  The engine maintains a semantic trap list of blame-language signals: 'pilot error,' 'crew error,' 'human error,' 'judgment error,' 'failed to,' 'negligence.' If the LLM uses any of these in the HFACS taxonomy field, the classification is rejected. Instead, the LLM must classify each factor at the correct HFACS level: Level 1 (what the pilot did. skill error, decision error, perceptual error, or violation), Level 2 (what conditions enabled it. fatigue, CRM failure, environment), Level 3 (what supervision allowed it. scheduling, training gaps), Level 4 (what organizational decisions created it. budget cuts, staffing, regulatory gaps). 'Pilot error' is Level 1 only. the investigation must reach Level 4.

• 07

  What evidence sources must be cross-referenced?

  Five mandatory sources: (1) Flight Data Recorder. minimum 88 parameters per ICAO Annex 6, with timestamps. (2) Cockpit Voice Recorder. last 2 hours of audio, transcribed with timestamps, correlated with FDR parameter changes. (3) ATC recordings. radar track, clearances, handoffs, weather advisories. (4) Maintenance logs. last A/B/C/D checks, MEL items, deferred defects, AD compliance, component life (TSN/TSO/CSN/CSO). (5) Wreckage analysis. impact signatures, fire patterns, fracture analysis (fatigue vs overload), metallurgical examination. The engine rejects speculative language like 'it appears that' or 'evidence suggests' when hard data from these sources exists.

• 08

  Why does this require recommendations to be addressed to specific authorities?

  Because 'improve training' with no addressee has zero accountability. The NTSB model requires each recommendation to name the authority responsible for implementation. the FAA, the operator, the manufacturer, or an international body. Each recommendation must include: what specific action to take, measurable success criteria, a response deadline (typically 90 days), a verification mechanism (audit, inspection, data review), and a direct link to a specific investigation finding. This is how aviation achieved its extraordinary safety record. not through vague wishes, but through tracked, accountable, evidence-linked changes.

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