"Observability" 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.

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

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.

A read-only MCP server that exposes Azure incident-investigation tools for a single App Service and its Application Insights telemetry, enabling an agent to correlate deployment history, platform metrics, and transaction traces.

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

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

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.

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.

A portable, read-only Model Context Protocol server for turning observability data into bounded evidence that AI agents can inspect safely.

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.

A read-only MCP server that enables agents to query and analyze Langfuse observability data, including traces, sessions, observations, scores, and metrics.

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.

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.

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.

Enables AI agents to inspect Kubernetes pods and cluster events, query Prometheus and Loki, and diagnose pod health with suggested actions through MCP tools.

Enables MCP clients to query seeded logs, latency/error-rate metrics, and alerts for five fictional microservices over a remote StreamableHTTP endpoint, with bearer-token access control.

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

Enables LLM clients to query AWS CloudWatch Logs and Alarms through MCP tools.

Semantic log clustering for incident triage, exposed as an MCP server.

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

Enables AI assistants to access full Datadog observability, including log search, APM trace filtering, smart sampling, and cross-correlation between logs, traces, and metrics.

Server for interacting with Honeycomb observability data. This server enables LLMs like Claude to directly analyze and query your Honeycomb datasets.

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

Maintenance, recovery, and observability for Claude Code. CLI + MCP server + dashboard.

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

A comprehensive Model Context Protocol (MCP) server for New Relic monitoring, observability, and management operations.

Enables LLM agents to query Dynatrace SaaS for observability data (logs, metrics, traces, entities, problems, vulnerabilities) and manage configurations (dashboards, notebooks, SLOs, synthetic monitors, settings).