"How to run a Windows command" matching MCP servers:

Enables clients to access 21 cloud-hosted financial tools via a token-authenticated forwarding layer that relays tool calls to the Silkj E-Investment skill gateway. Supports stdio, Streamable HTTP, and hosted deployment modes without embedding any financial algorithms locally.

A Model Context Protocol server that exposes Firebase Firestore and Authentication to AI agents, supporting multi-project configurations and read operations.

A MCP server that generates high-quality images using Volcano Engine's JiMeng AI API, supporting various aspect ratios and compatible with different MCP clients.

A basic example demonstrating how to develop and run a serverless Model Context Protocol (MCP) implementation using Netlify Functions and Express.

A secure MCP server example that demonstrates how to deploy to Google Cloud Run with authentication and identity token protection. Serves as a tutorial template for building production-ready MCP servers in the cloud.

Enables AI agents to access Cloudflare Workers AI for serverless LLM chat completions, text embeddings, and image generation via MCP tools.

A Python MCP server that lets MCP-compatible clients inspect and operate AWS through the AWS CLI, with command execution, suggestions, region lookup, and caller identity checks.

Enables AI coding tools to scaffold, build, and deploy Node.js or Python apps to Varity with a single command, automatically provisioning databases and backend services.

Agent-native hosting: your agent deploys any folder to a live URL in one command — no account, no dashboard, no CAPTCHA — and hands the human a link plus a plain-language summary. Full deploy lifecycle as MCP tools (deploy, verify, logs, inspect, rollback), production approval human-only; static sites and full Node.js apps, open source (Apache-2.0).

Intelligent AI routing and integration platform for seamless provider switching

A simple MCP server that provides a basic greeting tool and serves as a starter template for AWS Lambda deployment. Demonstrates how to build and deploy MCP servers with both local development and cloud deployment capabilities.

Enables estimating a BigQuery query's cost before running it, using a free dry run to report bytes processed and the estimated on-demand USD charge. It also flags common sources of wasted spend, such as SELECT * and missing partition filters on partitioned tables.

Publishes what an AI made — a page, a markdown or PDF doc, or a whole site — to a live HTTPS link. Runs locally over stdio from the @drop2run/mcp npm package, or hosted at https://mcp.dropto.run/mcp for chats that connect by URL.

A deterministic proxy for Keboola CLI operations with automatic git-to-Keboola branch mapping, enabling branch management, CLI command execution, and optional proxy to remote Keboola MCP tools with automatic branch injection.

Enables AI agents to run BigQuery queries safely under a configurable policy, with parsing, dry-run byte limits, row caps, and audit logging for every request.

Enables AI assistants to run engineering simulations on cloudHPC, including inspecting cases, getting resource advice, uploading, launching, monitoring, diagnosing errors, and downloading results.

A Model Context Protocol (MCP) server that exposes the EasyDeploy public REST API as tools for Claude, Cursor, Claude Code, and other MCP clients.

Enables AI agents to design cloud architectures, run cost/performance simulations, and draw them on a Pinpole canvas directly from Claude Code, Cursor, and OpenAI Codex.

Validates BigQuery SQL syntax and performs dry-run analysis without executing queries, providing cost estimates, referenced tables, and schema previews.

Enables AI agents to create, modify, and deploy web projects through the AICre8 platform's sandbox environment. It supports project management, code generation, and direct shell command execution for streamlined web development.