"OpsGenie AI - Product Requirements Document (PRD) for AI-Powered Incident Management Platform" matching MCP servers:

Enables AI incident-response agents to connect over MCP to a simulated service environment, receive alerts, gather diagnostic evidence, execute safe SOP actions, and verify recovery.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

Provides telemetry tools for retrieving recent logs and system metrics to support root-cause analysis of infrastructure incidents. Enables autonomous incident triage with grounded verification and human-in-the-loop remediation.

Enables autonomous investigation of production incidents through read-only MCP tools for incidents, metrics, and deployments, with human-approved rollback and recovery verification.

An incident management MCP server for on-call engineers, providing tools to search incidents, review deploys, query logs, analyze latency metrics, and acknowledge or summarize incidents.

AI-powered incident management MCP server that enables investigation, root cause analysis, and response actions for production incidents using mocked data for demo purposes.

Connects AI assistants to the Vectra AI security platform to enable intelligent analysis of threat detection data and automated incident response workflows. It allows users to investigate threats, take response actions, and generate security reports using natural language.

Incident Triage MCP is a Model Context Protocol (MCP) server for incident triage. It provides safe, auditable tools for evidence retrieval, deterministic summaries, ticket workflows, and notifications.

Connects AI-powered tools to the Confident AI platform for evaluating, observing, and iterating on AI quality, enabling cloud evaluations, dataset management, prompt versioning, production tracing, and human annotations directly from editors like Cursor and Claude Code.

Enables AI assistants to query system health, metrics, service reconnaissance, run multi-node patrols, and send incident alerts across distributed infrastructure through MCP tools.

Enables incident detection and analysis by identifying anomalies in metric time series and surfacing root-cause candidates and recommended actions. Supports both mock (synthetic) and VictoriaMetrics backends with identical MCP tool contracts for seamless development-to-production switching.

Role-aware MCP server over real HPC and cloud log corpora (BGL, OpenStack, Zookeeper) in SereneDB, where every answer cites the exact SQL that ran, backed by a tamper-evident, hash-chained evidence ledger.

Python-based MCP server for full-cycle incident management, enabling detection, root-cause analysis, and response guidance through tools like detect_incidents, analyze_incident, and suggest_response.

Enables diagnosing synthetic payment incidents using narrowly scoped MCP tools: read-only lookups for incidents, payments, and logs, plus a confirmation-gated retry action for failed jobs.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

Enables observability and governance for local LLMs via Ollama, including auditing model usage, scanning prompts for secrets/PII, and enforcing allow/deny policies.

MCP server for measuring, tracking, scoring, and improving AI agent reliability with tools for recording interactions, scoring reliability, analyzing failures, recommending improvements, generating audit reports, and checking MCP health.

Perimeter security for your AI Infrastructure.

Enables a local AI agent to investigate service incidents by querying read-only telemetry such as service health, metrics, logs, and traces, then produce verifiable assessments with evidence links and uncertainty.

MCP server that provides guarded, audited, read-only access to ops tooling (alerts, metrics, logs, deploys, runbooks) and a triage agent that diagnoses incidents end-to-end with CI-verified root cause analysis.

Enables incident responders to query and correlate observability and DevOps evidence across Grafana, Prometheus, Loki, Uptime Kuma, CloudWatch, IAM, and PostHog from a local MCP client, with bounded reads and redacted audit records.

Enables read-only investigation of service incidents by retrieving metrics, logs, and deployment history, and correlating evidence for root-cause analysis.

Enables local-first tracking of AI token usage and costs from Claude Code and OpenCode, answering queries about tokens, models, sessions, and cost through MCP tools and a CLI, with no network calls.

AI Ops - MCP server providing AI-powered tools and automation by MEOK AI Labs

Exposes service health and log search tools for incident triage, enabling AI agents to investigate and summarize operational issues.

Provides MCP-compatible safe read tools for incident investigation, enabling evidence collection and operational data access while keeping risky actions under human approval.

Unified local MCP AI Gateway that routes across Groq, OpenRouter, Mistral, and local Ollama providers, with OpenAI-compatible APIs, MCP tools, fallback/racing router, monitoring, and web dashboard.

An AI-native incident response server that exposes diagnostic tools (system status, error logs, ticket creation) via MCP, enabling LLM agents to autonomously assess and respond to incidents.

Compresses log files into templates and statistics using Drain3, and exposes them to AI assistants via an MCP server for efficient log monitoring and anomaly detection.

MCP server that exposes AI credit usage data from OpenMeter to the TAIS AI chat service, automatically scoped to the calling tenant and restricted to admin users.