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Observability

Tools and platforms for comprehensive system observability including logs, metrics, and traces. Enables monitoring, troubleshooting, and performance analysis of applications and infrastructure.

MCP ServersBrowse all →

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    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.
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    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.
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    Provides read-only access to Jaeger distributed tracing data through the Model Context Protocol. Enables Claude and other MCP-capable agents to search traces, inspect spans, and analyze service dependencies directly within conversations.
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    Enables AI assistants to access Scout Monitoring performance and error data through Scout's API. Provides traces, errors, metrics, and insights for Rails, Django, FastAPI, Laravel and other applications to help identify and fix performance issues like N+1 queries, slow endpoints, and memory bloat.
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    Enables interaction with Foreman instances to manage systems through the Model Context Protocol. It provides access to Foreman resources and tools, such as security update reports, directly within AI-powered environments like VSCode and Claude Desktop.
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    A Model Context Protocol server that enables LLMs to retrieve and analyze OpenTelemetry traces and metrics from Logfire, supporting exception tracking and custom SQL queries against telemetry data.
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    Enables AI assistants to interact with Rollbar error tracking and monitoring service, allowing them to diagnose issues, view item details, list deployments, retrieve session replays, and update item properties through natural language commands.
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    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.
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    Enables interaction with Beszel system monitoring tool to query system and container statistics, manage alerts, and monitor infrastructure through its PocketBase backend.
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    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.
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    A Model Context Protocol server that enables AI assistants to query Prometheus metrics, discover available data, and analyze system performance through natural language interactions.
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    Enables AI assistants to interact with New Relic monitoring and observability data through programmatic access to New Relic APIs. Supports APM management, NRQL queries, alert policies, synthetic monitoring, dashboards, infrastructure monitoring, and deployment tracking.
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    Enables sending rich notifications to Discord and/or Slack webhooks with automatic service detection, retry logic, and support for embeds, blocks, and attachments. Provides secure webhook management with comprehensive input validation and rate limiting.
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    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.
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    A TypeScript-based MCP server that enables users to interact with Prometheus metrics using PromQL queries and discovery tools. It allows LLMs to retrieve time-series data, metadata, alerts, and system status directly from a Prometheus instance.
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    A comprehensive MCP server providing over 26 tools for querying, monitoring, and analyzing NewRelic data through NRQL queries and entity management. It enables interaction with NewRelic's NerdGraph API for managing alerts, logs, and incidents directly within Claude Code sessions.
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    An MCP server for perfSONAR that enables querying historical network measurements, discovering global testpoints, and scheduling active network tests. It provides tools for monitoring throughput, latency, and packet loss through integration with measurement archives and pScheduler.
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    An MCP server for AI-powered log analysis that enables parsing, searching, and debugging across nine log formats directly within Claude. It features automated error extraction, sensitive data scanning, and streaming support for large log files.
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    An MCP server that exposes GPU-accelerated anomaly detection to AI assistants via the Model Context Protocol. Provides two MCP tools: waveguard_scan (send training + test data in one call, returns per-sample anomaly scores and top explanatory features) and waveguard_health (check API and GPU status). Works on time series, JSON, numbers, text, and images — fully stateless.
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