"TypeScript MCP servers for testing observability and security use cases" matching MCP servers:

Unified observability gateway for AI agents — one MCP server for Prometheus, Loki, and any backend.

MCP server exposing AI agent observability tools: list, inspect, and deterministically replay recorded agent execution traces for debugging failures.

Unified MCP server for observability and monitoring, providing tools to query metrics, logs, and traces through Prometheus, Grafana, Loki, and Jaeger.

Provides comprehensive monitoring and observability for MCP server ecosystems with real-time health checks, performance metrics, distributed tracing, anomaly detection, and automated performance reports using OpenTelemetry and Prometheus.

MCP server for querying observability data from Elasticsearch, SkyWalking, and Prometheus/VictoriaMetrics, enabling AI models to search logs, traces, and metrics across environments.

MCP server for observability that provides tools for log search, metrics inspection, SQL querying, incident summaries, and service discovery.

Enables live tracing and observability of MCP tool calls, persisting execution traces to MongoDB and streaming them in real time to a dashboard.

MCP server for AI agent observability, providing trace and span logging, search, latency/tokens/cost metrics, and anomaly detection using an in-memory buffer.

A comprehensive Model Context Protocol (MCP) server for Grafana, Prometheus, Kafka UI, and Datadog. Features a secure "Bring Your Own Key" (BYOK) architecture where credentials stay local. Provides tools for metrics querying, dashboard inspection, Kafka lag monitoring, and unified health checks.

Provides AI assistants with direct access to Red Hat OpenShift AI observability data, enabling querying of Prometheus metrics, Alertmanager alerts, Loki logs, Grafana dashboards, and Kubernetes cluster state to troubleshoot vLLM inference workloads.

Provides unified AI agent observability including tracing, cost tracking, performance monitoring, anomaly detection, and audit trails via MCP.

MCP server for monitoring, diagnosing, and auto-correcting observability issues across platform services, integrating with Sentry and PostgreSQL for error tracking and billing pipeline health.

Enables exploration of Kubernetes metrics, logs, traces, and service graph data via simple tools.

Provides tools for accessing Alibaba Cloud observability products including SLS (Log Service) and ARMS (Application Real-time Monitoring Service), allowing any MCP-compatible AI assistants to quickly interact with these services.

MCP servers for real-time development monitoring and interactive pair programming.

Provides tools for DeepTempo AI SOC including findings and case management, investigation workflow orchestration, action approval workflows, and MITRE ATT&CK layer generation.

Enables secure interaction between LLMs and MCP tools by applying zero-trust security controls, including sensitive data masking, file system protection, and policy enforcement.

Exposes an observability REST API as MCP tools, enabling incident investigation through automated correlation of deploys with errors, log and metrics queries.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.