"Observability" matching MCP servers:

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.

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.

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

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.

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.

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

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

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.

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

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 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.

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.

Enables seamless interaction with the IBM Instana observability platform, allowing access to real-time observability data directly within development workflows.

8 standalone MCP servers for SMBs — Guardrails (prompt injection, PII, secrets detection), FinOps (AI cost tracking and budgets), Observability (agent tracing), Smart Router (17+ models, 9 providers), Trust Score (governance scoring 0-100), Memory (persistent agent memory), ThinkSecure (runtime security + SOC2/GDPR audit), and A2A Lite (agent-to-agent protocol). 37 tools total, TNC credits

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

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.

Enables AI assistants to interact with a Qdrant vector database by exposing collection, point, vector, payload, snapshot, search, recommendation, discovery, and observability operations as MCP tools.

Gives AI assistants read-only access to a Kubernetes cluster's observability stack — Loki, Prometheus, Tempo, the Kubernetes API — plus AWS telemetry from CloudTrail, Config, ECR, and RDS. 23 tools for querying logs, metrics, traces, Kubernetes events, deployments, and audit history in plain language.

A local MCP server for SkyPortal and Fritz that exposes astronomical transient data (photometry, spectra, classifications, observability) as tools for AI coding assistants.

Enables LLMs to answer Berserk observability questions by calling verified KQL tools instead of hand-authoring queries, with role-based tool filtering and automated query discovery.

MCP server for managing store replenishment, including listing stores, raising orders, checking order status, and viewing SKU reports with observability and key rotation handling.

An MCP server for the Uptrace observability platform that enables querying traces, spans, logs, and metrics through natural language. It provides tools for error analysis, service discovery, and trace visualization within MCP-compatible clients.

Safety-first GitOps operations for ArgoCD via the Model Context Protocol. Enables listing, diagnosing, syncing, and managing ArgoCD applications with progressive disclosure and defense-in-depth security.

Bridges AI coding assistants with the OpenTelemetry ecosystem, providing real-time access to repositories, documentation, examples, semantic conventions, and instrumentation scoring for high-quality observability.

Enables data observability operations with the Sifflet platform. Supports exploring assets, monitors, incidents, generating monitor-as-code YAML from descriptions, and performing impact analysis.