DB Incident
Delivers MCP access to database incident tracking and response functions. Retrieves incident details, logs new events, and updates resolutions for DB errors, outages, or breaches. Database administrators, SREs, and security teams use it for automated incident handling in production environments.
Overview
The DB Incident MCP server (db-incident-mcp) connects AI models to database incident management via the Model Context Protocol. It supports querying incident records, logging events, and managing response workflows for issues like connection failures, data corruption, query timeouts, or unauthorized access attempts. This enables programmatic integration into monitoring pipelines without custom scripting.
Key Capabilities
No specific tools are enumerated in the available configuration. The server exposes core database incident operations, such as:
- Retrieving lists of open and historical incidents with timestamps, severity levels, and affected resources.
- Creating new incident reports with details like error codes, stack traces, and impacted tables.
- Updating incident statuses (e.g., acknowledged, in-progress, resolved) and attaching resolution notes.
- Generating summaries or alerts based on incident patterns.
These functions allow AI agents to process DB telemetry data directly.
Use Cases
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Outage Response: Query list_open_incidents to fetch current DB outages, assess impact on replicas, and trigger update_status for team notifications.
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Root Cause Analysis: Pull get_incident_details for a specific event ID to review query logs and correlate with incident_history for recurring failures.
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Automated Reporting: Use create_incident from monitoring alerts (e.g., high CPU or replication lag) to log events with auto-populated metadata.
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Compliance Auditing: Export resolved incidents via export_incidents to generate reports for security audits or post-mortems.
Who This Is For
Site reliability engineers (SREs), database administrators (DBAs), and security operations (SecOps) teams managing relational or NoSQL databases in cloud or on-prem setups. It's for those integrating AI into observability stacks like Prometheus, Datadog, or custom alert systems to handle incidents at scale.