"Methods for Accessing Real-Time Data Using Chrome" matching MCP servers:

Enables AI to interact with Chrome DevTools Protocol to inspect and debug live web pages, including DOM, styles, console logs, and network requests.

Bridges Chrome DevTools with Cursor AI to enable debugging and monitoring, including console logs, network requests, screenshots, and element inspection.

Lets coding agents control and inspect a live Chrome browser via the Model-Context-Protocol, providing advanced browser debugging, performance insights, and reliable automation.

Enables coding agents to control and inspect a live Chrome browser for reliable automation, in-depth debugging, and performance analysis via Chrome DevTools.

Enables querying real-user Core Web Vitals field measurements — 75th-percentile LCP, INP, and CLS with distributions and ~40 weeks of history — for any origin or page that has enough Chrome traffic.

Lets coding agents control and inspect a live Chrome browser for reliable automation, in-depth debugging, and performance analysis. Provides access to Chrome DevTools capabilities such as tracing, network inspection, screenshots, and console messages through MCP clients.

Lets AI coding assistants control and inspect a live Chrome browser using full Chrome DevTools capabilities for browser automation, debugging, and performance analysis.

An MCP server that lets AI coding agents control and inspect a live Chrome browser, providing reliable automation, debugging, and performance analysis through Chrome DevTools.

Enables AI coding agents to control and inspect a live Chrome browser, providing reliable automation, in-depth debugging, and performance analysis through Chrome DevTools.

A Model Context Protocol (MCP) server for Chrome Enterprise Premium that exposes DLP rules, content detectors, connector policies, browser telemetry, and license management as MCP tools, enabling any MCP-compatible AI agent to inspect and configure a Chrome Enterprise environment.

An MCP server that gives AI assistants the ability to connect to, query, profile, and monitor data sources — turning any LLM into an interactive data engineering copilot.

Enables LLMs to interact with Kibana through composable toolboxes for building dashboards, managing data, and handling alerts, among other operations.

An unofficial MCP server that connects AI assistants to Akamai APIs, providing tools for edge diagnostics, debugging, and network analysis.

MCP server that unifies real-time telemetry from industrial systems into a single queryable interface, enabling production visibility, anomaly detection, and operational insights.

Enables AI agents to interact seamlessly with Splunk environments through 20+ tools for search, analytics, data discovery, administration, and health monitoring. Features AI-powered troubleshooting workflows and supports multiple Splunk instances with production-ready security.

Enables AI clients to investigate data engineering workflows by exposing tools for job status, logs, schema lookup, read-only SQL, and searching documentation/incidents, along with resources and prompts for ETL failure analysis.

Enables debugging of web applications by connecting Claude to Chrome's developer tools through MCP, allowing network monitoring, console inspection, and performance analysis via natural language.

Enables searching for relevant tables and retrieving data from Microsoft Sentinel's data lake using natural language, supporting security hunting scenarios like password-spray detection and impossible travel checks.

Enables statistical forecasting and anomaly detection over numerical time-series data using Holt linear smoothing, multi-step projections, and variance-based confidence bands.

Enables Claude Code to query fleet telemetry data for device health summaries, anomaly detection, capacity breach forecasts, and incident history via narrow MCP tools.