"Guide to GCP Resource Management and PaaS Setup and Configuration" matching MCP servers:

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 assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.

MCP server enabling AI agents to interact with OpenProject API v3 for project management, including creating and managing work packages, projects, comments, time entries, boards, and user dashboards.

Your agent takes a business live on payments: go_live creates a real merchant account (sandbox + production) the same day; create_checkout returns a payable link. Payments for AI builders, 3.9% flat.

Enables access to Usage and Billing APIs for managing accounts, products, meters, plans, and usage reporting. Supports operations like creating products/plans, reporting usage, and retrieving billing information.

Search the fastest-growing US horse boarding directory: 6,000 facilities and 3,800 trainers across 48 states, plus boarding price data by state.

Landlord and rental-property calculators as MCP tools: cash flow, cap rate, DSCR, Airbnb break-even occupancy, rent late fees, lease notice dates, and IRS mileage. Offline and deterministic, with no dependencies.

Enables named, authenticated callers to run read-only introspection of a Google Cloud project and to read and write per-tenant notes that stay isolated between callers. Every request is scope-checked against a reviewed policy, rate-limited per caller, and audited without ever logging argument values.

Enables querying Rick and Morty characters, locations, and episodes using the Rick and Morty API with no authentication required.

Enables AI agents to manage Google Cloud Platform resources including projects, IAM, service accounts, and APIs directly through GCP REST APIs without requiring gcloud CLI or Terraform.

A streamlined MCP server for XMP metadata embedding with beautiful formatting and smart filename indicators, enabling metadata embedding, reading, validation, and report generation for lifestyle, product, and orbit schemas.

Enables AI assistants to interact with and manage Google Cloud Platform resources including Compute Engine, Cloud Run, Storage, BigQuery, and other GCP services through a standardized MCP interface.

Enables AI assistants to interact with Google Cloud Platform services for log analysis and root cause investigation. Provides tools to query Cloud Logging, detect error patterns, and perform real-time log streaming across multiple GCP projects.

An MCP Server that provides a conversational interface to the UK Open Banking account information API, allowing agents to interact with bank account data through natural language commands.

Provides access to the Reuters Business and Financial News API to retrieve articles, trending news, and market data. It enables searching and filtering financial content by date, author, category, and keywords.

An MCP server for searching, browsing, and downloading game sprites, models, textures, and sounds from The VG Resource network, covering 100k+ assets across 50+ consoles.

Enables interaction with Google Cloud Storage buckets and blobs through an MCP server, supporting operations like listing, uploading, searching, batch operations, and generating signed URLs.

📇 ☁️ - Capture web pages as cryptographically signed, tamper-evident evidence. Ed25519 signatures, RFC 3161 timestamps, and WACZ archives. Four tools: capture_url, get_capture, list_captures, verify_capture.

Manages resource allocation, MCP server lifecycle, and Kubernetes workers in cortex automation systems. Provides tools for requesting/releasing job resources, starting/stopping/scaling MCP servers, and provisioning/destroying burst workers with TTL management.

Model Context Protocol server for Google Cloud Platform, enabling interaction with Resource Manager, Compute Engine, Cloud Storage, BigQuery, Pub/Sub, and Cloud Logging via natural language.

An MCP Server that provides a natural language interface to Google Cloud Resource Settings API, enabling users to view and manage settings for Google Cloud resources through conversation.

Serves read-mostly MCP tools across ten mock verticals and investment use cases, each backed by bundled SQLite databases, with optional proxying of investment tools to a user-supplied banking API. It enables clients to query realistic vertical data through OAuth-protected MCP endpoints over HTTP or WebSocket.

Estimates physical quantum resources (qubit count, runtime) for quantum algorithms on fault-tolerant hardware through natural language queries.

License-aware game development resource discovery MCP server. Helps AI coding agents find reusable game assets with commercial-use filtering, license classification, and attribution generation.

Provides comprehensive design principles and best practices to help LLMs generate modern, accessible web pages through guidance on layouts, colors, and typography. It enables users to review design approaches and access expert recommendations for responsive design, component structure, and current industry trends.

Exposes employee leave-management operations (add employees, check balance, apply/cancel leave, view history) as MCP tools for AI clients like Claude Desktop to call directly.

An MCP server for unified cost tracking and analysis across AWS, OpenAI, and Anthropic. It enables users to query expenditures, compare costs across providers, and analyze usage trends through natural language.

Enables LLM agents to explore and search a 1C:Enterprise configuration codebase, including metadata objects, module procedures, call graphs, and semantic similarity search.

Minimal MCP server for testing client support for resources/subscribe and notifications/resources/updated, simulating an asynchronous status update.

Provides semantic search capabilities over the Plesk Extensions Guide documentation using Retrieval-Augmented Generation (RAG) and vector embeddings. It enables AI assistants to retrieve relevant technical information and answer natural language queries regarding Plesk extension development.