"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 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.

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

Provides a framework for running multiple MCP servers using FastAPI, stdio, and streamable-http transports, with an echo tool for testing and integration with MCP Inspector.

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.

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.

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.

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.

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

An MCP server that enables AI assistants to control a web browser through natural language commands, allowing them to navigate websites and extract information via SSE transport.

A full-stack API automation testing server that parses OpenAPI/Swagger/Postman/HAR specs, generates comprehensive test scenarios and executable code, and provides AI-powered review and auto-fix.

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

A learning MCP server that demonstrates tools and resources using FastMCP, with a greet tool and UI capabilities.

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

Provides security audit primitives for MCP servers, agent tool schemas, and system prompts, along with pentest utilities like JWT inspection and HTTP diffing.

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.