"Creating a database of novels and screenplays in Google Drive" matching MCP servers:

MCP server for multiple databases (PostgreSQL, MySQL, MariaDB, SQLite, MongoDB, Redis) with tools for schema inspection, querying, performance diagnostics, and safe write operations, featuring access modes, PII masking, and audit logging.

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 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.

Self-hosted Nightingale (n9e) includes a native Streamable HTTP MCP server at /mcp for querying alerts, rules, metrics, logs, hosts and dashboards with the caller's existing permissions. Users deploy the Nightingale server themselves; the implementation and deployment documentation are in the main repository, and the official website is https://n9e.io.

MITM proxy MCP server that intercepts and verifies AI agent tool calls, detecting fabrication, tracking costs, verifying outcomes, and inferring user satisfaction.

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.

An MCP server that analyzes SQL schemas, generates realistic test data, benchmarks queries, and provides optimization recommendations to improve database performance.

Provides MCP tools to create and query SigNoz trace funnels, enabling agents to measure their own step conversion rates from within the agent loop.

An MCP server that provides cost and reliability observability for LLM and agent workflows. It records model calls and allows querying and aggregating telemetry data through MCP tools.

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.

Enables AI agents to monitor Docker containers, system resources, ports, and logs in real-time.

Lets an assistant read recorded production AI agent runs — steps, model and tool calls with arguments, and outcomes — along with the findings of comparison runs that checked a prompt or model change against those recordings, and verify a signed evidence bundle offline with no account or network. It is strictly read-only: no tool starts a replay or comparison, so nothing can spend money or act without a person deciding.

Enables AI assistants to query and analyze AI agent sessions from observability providers like Shepherd (AIOBS) and Langfuse, allowing users to debug agent runs, compare sessions, track performance, and analyze LLM usage patterns.

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

Read-only MCP server for TrueNAS Scale debugging, providing k3s tools for kubectl operations and whitelisted midclt calls for system info, apps, and pools.

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.

A drop-in Model Context Protocol (MCP) server that lets an AI agent query PostgreSQL — read-only, enforced at the database level, with real SQL validation, timeouts, cost limits, OAuth 2.1, and an audit trail.

An MCP server for Dead Man's Snitch that enables AI assistants to manage monitoring snitches, including creating, updating, checking in, and pausing monitors for scheduled tasks and cron jobs.

Enables querying OpenObserve logs—listing streams, inspecting schemas, and running SearchSQL time-windowed searches—so you can answer what actually happened in production. Optionally correlates those logs against a database through a verified read-only connection via DbSchema and DbQuery tools.

An MCP server that monitors and provides analytics on weekly report submissions in a Google Sheet, allowing users to check missing submissions, view statistics, and track individual reporting status.

Enables local-first observability for MCP agent workflows by recording tool calls, prompts, latency, and token usage in a local SQLite database, and provides tools for inspecting, comparing, exporting, and generating dashboards from traces.

Enables AI assistants to drive the UTMStack SIEM/XDR platform for triaging alerts, searching logs, running SQL, managing incidents, inspecting agents, creating/deleting correlation rules, managing data filters, and running commands on endpoints.

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.

Provides financial audit reporting and ledger query tools for AI agents, enabling generation of compliance, risk, and transaction reports and querying of transaction data, balances, and summaries from an in-memory SQLite database via MCP.

Enables AI agents to query a private MySQL database through a self-maintained SSH tunnel while enforcing read-only SQL by default, with optional opt-in server and Docker operations.

A local gateway that caches LLM prompts and tool results, stores searchable compressed context, and relays downstream MCP tools under deny-by-default rules. It keeps everything in one SQLite database with a single ledger tracking tokens paid upstream versus tokens avoided, plus a web console for live metrics.