"How to create new files and save them to Google Drive" matching MCP servers:

Enables natural language queries of the Chrome UX Report API to fetch Core Web Vitals (LCP, INP, CLS, FCP, TTFB), compare form factors, benchmark pages against site averages, and analyze up to 40 weeks of performance trends.

Enables AI agents to access New Relic logs and APM data through the NerdGraph API. It allows users to execute NRQL queries, retrieve application performance metrics, and analyze transaction traces using natural language.

A comprehensive Model Context Protocol (MCP) server for New Relic monitoring, observability, and management operations.

An MCP server implementation for accessing Google Analytics 4 (GA4) data, built using the Model Context Protocol TypeScript SDK.

Run NRQL, NerdGraph, and REST v2 operations to query data, manage incidents, create synthetics, and annotate deployments — all from your MCP client.

Enables natural language access to New Relic for monitoring, querying, and managing dashboards, entities, alerts, and deployments via the Model Context Protocol.

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.

Enables interaction with Google Cloud services including billing cost analysis, log querying, and metrics monitoring through natural language commands. Provides comprehensive tools for managing GCP resources, analyzing costs, detecting anomalies, and retrieving operational insights.

Enables AI agents to query Google Analytics 4 data, including listing accounts and properties, running historical and real-time reports with customizable metrics and dimensions.

Connects AI assistants to New Relic with read-only access to NRQL, logs, metrics, traces, alerts, and more. Offers optional, gated write operations with a dry-run and confirmation workflow.

Provides comprehensive access to the New Relic observability platform through 55 MCP tools, enabling APM, infrastructure, logs, traces, alerts, dashboards, synthetics, workflows, notifications, service levels, tags, entities, and workloads via a single GraphQL endpoint.

Enables users to query and manage New Relic account data and features through natural language or specific commands, including NRQL queries, entity search, APM, Synthetics, and alerts management.

A persistent activity log server that allows MCP-compatible AI assistants to log events, decisions, and system logs over HTTP, with a built-in web dashboard for monitoring and searching logs.

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

Enables autonomous AI agents to safely inspect, optimize, and manage live search campaigns through 38 purpose-built tools covering bids, budgets, keywords, RSAs, assets, and negative keyword harvesting. Bundles two-layer Pydantic execution shields with bounded bid ceilings and fixed stepping, a preflight gatekeeper, and W3C distributed tracing that separates upstream Google Ads API latency from local execution overhead.

Enables precise measurement of live HTTP and WebSocket services while maintaining an append-only, evidence-based ledger of claims that agents can query with explicit timestamps, staleness indicators, and instructions for re-verification.

Provides tools and context for interacting with Google Cloud services, enabling natural language management of billing, monitoring, logging, IAM, Spanner, and more.

Enables AI assistants to query, search, and analyze logs across Google Cloud Platform projects. It supports advanced filtering by severity or resource type and provides detailed log entry retrieval and project listing capabilities.

A production-oriented reliability, benchmarking, and evaluation system that layers multi-version protocol negotiation, adaptive streaming, connection pooling, and resilience onto an MCP agent runtime.

Provides governed MCP tools for querying optionally restricted Microsoft Sentinel/Log Analytics tables and optional Defender XDR hunting data, retrieving incidents and entities, and—for agent identities only—adding attributed incident comments, with Entra ID authentication, KQL validation, rate limits, and redacted audit logging.

An MCP server that provides monitoring tools for Codex agents, allowing them to create and wait on conditions using commands, log files, file events, and custom probes, replacing polling loops with event-driven blocking.

Enables AI agents to analyze ROS2 log files across multiple nodes by merging them into a unified timeline and providing smart query tools, without requiring ROS2 installation.

A read-only MCP server that enables AI agents to act as GCP platform engineers, allowing them to investigate incidents, take inventory, and find cost-optimization opportunities in Google Cloud projects without mutating any infrastructure.

MCP server for log file analysis. Gives LLMs the ability to efficiently analyze large log files without loading them into context.

MCP server that stream-parses NDJSON log files without loading them into memory — filter by pattern, detect error spikes via Z-score analysis, summarize severity timelines by time window.

Enables AI assistants to interact with Uptime Agent monitoring system to check uptime status, manage incidents, create new monitors, and analyze downtime through natural conversation.

MCP server allowing AI agents to query New Relic for debugging incidents.

MCP server for the Production Master incident-investigation service, enabling any MCP client to drive investigations via tool calls with pass-through authentication.

Records runtime events from any app via zero-code instrumentation and exposes them to LLMs through MCP for autonomous debugging.

Enables an MCP-capable agent to operate a heterogeneous cluster of Debian/Armbian Linux nodes over SSH from a single inventory, inspecting status and logs, managing packages, services, files, storage and NFS, and deploying new capabilities to nodes through installable modules. Built-in cluster tools fan out to nodes by explicit names, roles or tags with bounded concurrency, while modules can be installed on compatible nodes and run on demand or as systemd services.