"Observability" matching MCP servers:

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.

Seamlessly bring real-time production context—logs, metrics, and traces—into your local environment to auto-fix code faster.

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 AI assistants and LLMs to query SigNoz observability data (metrics, traces, logs, alerts, dashboards) using natural language.

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 LLM observability. Query your Spanlens traces, stats, cost anomalies, and savings from Cursor, Claude Desktop, or any MCP client. Open source (MIT).

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.

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 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.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Connects AI assistants to the Invariance observability platform, enabling natural language queries of traces, monitors, and signals to investigate issues and analyze agent behavior. Supports debugging, monitoring, and evaluation dataset management through Claude Desktop, Cursor, and Claude Code.

Open-source MCP and LLM gateway that aggregates MCP tools behind a unified endpoint with governance, observability, and explicit execution controls.

A comprehensive gateway and registry that unifies MCP, REST, and Agent-to-Agent services into a single secure endpoint for AI clients. It provides advanced capabilities including federation, virtual servers, rate-limiting, and OpenTelemetry observability.

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.

AI-native developer observability MCP (Model Context Protocol) server that gives AI agents full-stack visibility into your development environment — browser, terminal, and processes — all in one unified interface.

Enables AI agents to perform observability analysis across Prometheus-compatible metrics and logs without prior vendor knowledge, including signal discovery, entity topology resolution, root-cause analysis, correlation, and capacity forecasting through natural-language anchors.

Enables AI agents to integrate with IBM Storage Insights for seamless observability and diagnosis of registered storage assets. It provides tools to retrieve alerts, performance metrics, and system configurations via the IBM Storage Insights External APIs.

A remote MCP server that enables real-time interaction with the Dynatrace observability platform, bringing production-level monitoring data directly into development workflows.

Enables interaction with the Bigeye Data Observability platform to query data quality issues, analyze data lineage and dependencies, track AI agent data access patterns, and perform root cause analysis for data issues.

Provides coding agents with a durable, revision-aware project workspace for semantic context, governed source changes, verification, task checkpoints, and observability through an MCP interface.