"Observability" matching MCP servers:

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

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 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.

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.

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

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.

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.

Radar is an open-source Kubernetes observability and diagnostics MCP server. It exposes cluster health, workload diagnosis, logs, events, topology, audit findings, and remediation actions to AI agents through the Model Context Protocol.

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

Lets AI agents query, manage, and operate their LLM observability data directly from the conversation. Provides 87 tools for cost analysis, alerting, anomaly detection, and runtime control gates.

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.

Semantic tool-routing gateway for MCP servers that cuts 70-90% of context tokens, with RBAC, API-key auth, response caching, hot-reload config, and a real-time observability dashboard.

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.

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

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.

Enables local-first observability for MCP agent workflows by recording tool calls, prompts, latency, and token usage in a local SQLite database, and provides tools for inspecting, comparing, exporting, and generating dashboards from traces.

Connects AI agents to New Relic observability data through the NerdGraph GraphQL API, running locally over stdio with a standard User API key. Enables NRQL queries, entity and alert lookups, log and trace investigation, deployment correlation, and metric comparisons while staying read-only by default.

A production-grade, extensible Python template for building Model Context Protocol servers with support for Streamable HTTP and stdio transports. It provides a structured framework for implementing tools, resources, and prompts with built-in authentication, observability, and background task management.

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

Enables language models to access LangSmith observability platform features including fetching conversation history, managing prompts, retrieving traces and runs, working with datasets and examples, and analyzing experiments.

Enables AI agents to query New Relic observability data, including NRQL queries, entity searches, incident tracking, deployment history, and golden signals, via the Model Context Protocol.

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