"How to handle AdMob (Google mobile advertising SDK) integration" matching MCP servers:

Runtime access for AI coding agents to observe and control Android and iOS apps.

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.

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

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.

A stateless MCP server for incident lab operations, exposing MCP and health endpoints, with conformance testing.

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

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.

Enables MCP clients to read console logs and network requests from a connected mobile H5 page and, with explicit user authorization, execute JavaScript in that page.

MCP server that provides structured audit logging for AI agent repair tasks via tools to start, record, end, query, and export event traces, with JSONL persistence and SDK integration.

An AI-powered server that provides rapid debugging of server logs with actionable fixes in under 30 seconds, featuring real-time monitoring and root cause analysis through Google Gemini integration.

Analyzes how search engines and AI systems discover, crawl, cite, and convert traffic via CLI and MCP tools.

MCP server that reports AI agent costs (tokens, latency, dollars) with per-tenant isolation via OAuth, and includes an ADK agent for natural-language queries.

MCP server that exposes WebSocket session management as tools, allowing AI agents to open persistent connections, send typed frames, drain buffered messages, and monitor per-frame Shannon entropy to detect schema divergence.

A Model Context Protocol (MCP) server that provides comprehensive Datadog monitoring capabilities, enabling Claude to manage CI/CD pipelines, analyze logs, query metrics, and handle monitors and SLOs.

16-tool MCP server for Google PageSpeed Insights & Chrome UX Report APIs. Analyze, compare, and optimize web performance directly through Claude, Cursor, or any MCP-compatible AI client.

Relays error logs from other MCP servers when an LLM call fails and the returned error message is unclear, helping the model handle errors more intelligently.

An MCP server that automatically instruments Python AI agents with the ioa-observe-sdk, adding OpenTelemetry-based tracing, metrics, and logs with zero manual effort.

MCP server for measuring web performance with Google PageSpeed Insights, providing median scores with uncertainty and real-user data. It offers tools for scores, diagnostics, and comparisons to identify regressions.

DCL Trust Oracle is a deterministic AI audit layer, natively integrated with MCP, that evaluates LLM and agent outputs against configurable policies before and after action. Every verdict is written to a tamper-evident, hash-chained audit log storing only cryptographic metadata — never raw content — enabling privacy-first forensic review.