"Observability" matching MCP servers:

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

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.

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

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

An MCP server that gives AI assistants direct access to Kubernetes clusters for pod management and network debugging (DNS, NetworkPolicy, ingress, service mesh) and correlates Dynatrace observability with OpenTelemetry self-monitoring, which automatically ships traces, metrics, and logs to Dynatrace.

Read-only observability MCP for Windows, WSL, Docker, and WSLC.

MCP-native agent evaluation and observability server. Log traces, evaluate output quality with 12 built-in rules (PII detection, prompt injection, cost thresholds), and track agent costs. Real-time dashboard, OTel-compatible spans. Self-hosted, MIT licensed.

Fetches Langfuse observability traces directly into a VS Code coding agent's context, enabling querying and viewing trace data through natural language.

MCP server for Prometheus metrics and observability. Give Claude (or any MCP-capable agent) read access to your Prometheus instance — query metrics with PromQL, inspect active alerts, and explore scrape targets without leaving the conversation. Tools: prometheus_list_metrics, prometheus_query, prometheus_query_range, prometheus_list_alerts, prometheus_list_targets.

eBPF-based GPU causal observability agent with MCP server. Traces CUDA Runtime and Driver APIs via kernel uprobes and host events via tracepoints to build causal chains explaining GPU latency. 7 tools: get_check, get_trace_stats, get_causal_chains, get_stacks, run_demo, get_test_report, run_sql. Telegraphic compression reduces token usage ~60%. Supports stdio and HTTPS (TLS 1.3) transport.

Enables AI assistants to interact with self-hosted Dynatrace Managed environments to retrieve observability data, security insights, and performance metrics. It allows users to query problems, logs, events, and SLOs through natural language interfaces in both local and remote modes.

Comprehensive MCP server for Langfuse, enabling AI assistants to access and manage traces, observations, scores, datasets, and sessions for observability.

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

Agent observability MCP server for querying OpenTelemetry traces, failures, tool health, security evidence, and regressions.

Game observability and analytics for coding agents: stutters, crashes, custom metrics, dashboards and playtest bots for Unity, Godot, Unreal, Three.js, Phaser and GameMaker games.

Enables local LLMs to perform enterprise-grade code auditing, AST security screening, architectural planning, and real-time Discord observability with zero cloud API costs.

Enables comprehensive management of Tailscale networks including device management, DNS configuration, file sharing via Taildrop, security monitoring, and network automation with built-in Grafana/Prometheus observability stack.

Self-hosted MCP governance gateway that aggregates 150+ tools behind policy profiles (strict/balanced/dev) with a per-call audit trail. Python 3.12/FastAPI, Docker-deployable, built for regulated environments that need observability over agent tool calls.

A Windows observability MCP server that lets AI agents inspect system health, processes, performance, hardware, and Windows diagnostics. WinKit provides real-time system data and diagnostic signals so AI agents can understand and troubleshoot Windows PCs.

Enables users to query and manage data observability through an API, including platform health, daily briefings, alerts and rules, data assets, freshness checks and schedules, and schema drift monitoring.