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

Enables AI assistants to scan project dependencies and Infrastructure as Code files for security vulnerabilities and misconfigurations. It also provides automated fixing capabilities to remediate identified security issues.

Enables querying a dataset of Japanese advertising-law enforcement actions, offering search, retrieval, recent listings, and statistical summaries so models can cite real orders rather than inventing them.

Enables MCP clients to manage Tines cases via the Cases v2 API, supporting create, read, update, close, and comment operations. Runs in a container via podman/docker and defaults to read-only for safety.

Provides penetration testing tools including nmap, nikto, sqlmap, wpscan, and exploit database searches for educational and authorized security testing purposes using Kali Linux tools.

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

Code-aware browser testing agent — reads your codebase, understands functionality, tests every element, reports with screenshots. Works as MCP server for Cursor/Claude Code or standalone CLI.

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

Enables listing, retrieving, creating, and updating DMCA.com takedown, DIY, and compliance cases, plus fetching site reports, through the DMCA.com API with token authentication. It also supports minting an API token via login and returns upstream JSON responses as-is.

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

Launches multiple Browser-Use agents to automatically test websites for UI bugs, broken links, accessibility issues, and other technical problems on both live and localhost sites.

MCP server that enables AI assistants to query Grafana/Loki logs and Thanos/Prometheus metrics directly from MCP-compatible clients like Cursor or Claude Desktop.

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

Enables AI assistants to write and debug integration tests by providing live access to DOM structure and executing code directly in test environments.

Provides testing and quality assurance tools for AI agents via MCP, enabling generation of test cases, mock data, API mocks, coverage analysis, and assertions.

MCP server for testing GitHub Actions workflows locally using nektos/act.

Governed Prometheus + Grafana operations — firing-alert and scrape-target RCA, alert noise/flapping analysis, silences, and dashboards, with unbypassable audit logging (MCP + CLI), budget/runaway guards, dry-run, and undo/rollback.

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 developers to investigate request failures by querying Prometheus metrics, Loki logs, and Tempo traces without opening Grafana, while supporting read-only access and optional Grafana annotations.

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.

Enables submitting FMF-based test requests to Testing Farm and listing available OS composes for testing.

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.

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

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

Enables AI agents to query Grafana dashboards, alerts, and datasources for observability insights and incident investigation.

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.

An LLM-driven API testing platform exposed over MCP, enabling declarative test definition and DAG-based execution.

Enables automated testing and coverage reporting for MCP services with test execution, file generation, and mock service creation. Provides comprehensive testing infrastructure including Jest integration, coverage reports, and health checks for the MCP ecosystem.

MCP server that enables AI agents to programmatically run tests, query results, and receive intelligent recommendations about test execution strategy.

Provides coding agents with visibility into test health through tools for flaky test detection, test quality linting, and LLM evaluation harness, enabling them to triage failures, review test quality, and check prompt changes for regressions.