"Understanding Unstructured Data or Concepts" matching MCP connectors:

Public MCP digital twin with synthetic systems and an agent firewall. No customer data.

DriftOracle - 15 tools for model/data drift monitoring: PSI, KS-test, alerts, evidence packs.

The Google GKE MCP server is a managed Model Context Protocol server that provides AI applications with tools to manage Google Kubernetes Engine (GKE) clusters and Kubernetes resources. It exposes a structured, discoverable interface that allows AI agents to interact with GKE and Kubernetes APIs, enabling them to inspect cluster configurations, retrieve Kubernetes resource YAMLs, monitor operations like cluster upgrades, diagnose issues, and optimize costs—all without needing to parse text output or use complex kubectl commands.

Read monitors, incidents, heartbeats, on-call and status pages; acknowledge or resolve incidents.

An MCP server giving access to Grafana dashboards, data and more.

High-performance array aggregation and metrics clearing engine. Cleans and bucket-groups noisy metric streams via an $O(N)$ single-pass data sweep. Operates natively with the pay-per-call x402 micropayment framework.

Uptime, SSL, DNS and domain monitoring you can talk to from Claude or any MCP client.

Enable secure connectivity between Sentry issues and debugging data, and LLM clients, using a Model Context Protocol (MCP) server.

Mobile observability for AI agents. Investigate crashes, hangs, ANRs, bugs, and app performance, and triage app store reviews, directly from your IDE or terminal.

Real User Monitoring for Core Web Vitals. Query LCP, INP, CLS field data from real visitors.

Log, evaluate, and ground AI decisions against authority context. Returns PASS, WARN, or BLOCK.

Data observability tools for engineering teams: alerts, freshness, schema drift, lineage, quality.

A paid remote MCP for AI SDK data query MCP, built to return verdicts, receipts, usage logs, and aud

Query Honeycomb observability data: traces, events, metrics, SLOs, triggers, and boards.

Data + AI observability — monitor and troubleshoot production-grade agents and the context they use.

Website performance monitoring: scans, Core Web Vitals, RUM data and alerts.

The Polar Signals MCP server enables AI assistants to connect directly with performance profiling data, allowing users to analyze application performance through natural language queries. Key capabilities include querying CPU performance and memory usage, exploring profiling metadata like profile types and labels, and providing AI-driven code optimization suggestions directly within development environments like Claude Code or Cursor.

The Buildkite MCP server exposes Buildkite product data (pipelines, builds, jobs, and test data) to AI tools, editors, and agents through the Model Context Protocol. It provides capabilities including pipeline creation and management, build monitoring with specialized tools like 'wait_for_build', efficient log querying using Apache Parquet conversion and caching, and OAuth-based authentication for both read-write and read-only access to Buildkite's REST API.

Cloud hosted Okahu MCP server that helps you manage genAI trace data

Access New Relic observability data through MCP - query metrics, logs, traces, entities, and more