I built CloudHike: a backend your AI coding agent can set up over MCP

说明与用途

CloudHike 让 AI 代理能够自动部署 Postgres、S3 存储、Redis、身份验证和电子邮件服务，消除手动管理密钥和多标签浏览器的麻烦。

问题

AI coding agents can write full-stack code quickly but struggle with backend provisioning—creating databases, storage, auth, email, and managing secrets—leading to momentum loss.

结果

CloudHike streamlines backend setup for AI agents, reducing manual provisioning to a single prompt and eliminating the need for multiple dashboards and key management.

工作流

• 1. Connect CloudHike MCP server to the editor (e.g., Claude Code or Cursor).
• 2. Agent reads the app code to determine required services (Postgres, Auth, Storage, etc.).
• 3. Agent calls list_projects or create_project to obtain a project.
• 4. Agent calls deploy_services for the needed services and then wait_for_services until they are ready.
• 5. Agent calls get_environment and writes the returned variables to .env.local (ensuring the file is git‑ignored).
• 6. Agent calls get_docs for relevant CloudHike documentation and writes connection code (e.g., pg pool for DATABASE_URL).
• 7. Developer reviews the diff, runs the app, and continues development without opening multiple browser tabs.

证据

My AI agent can write a whole Next.js app in an afternoon. Then it stops and hands the hard part back to me: make a Postgres database, set up a storage bucket, find a Redis instance, add auth, pick an email provider, and copy a dozen connection strings into .env.local without committing any of them.

来源

CloudHike is a backend-as-a-service built for people who code with AI agents. Each project can have: Postgres, S3-compatible storage, Redis, Auth, Transactional email, Background jobs.

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With the server connected, open your app's folder and paste this prompt (it's from the docs): Use the cloudhike MCP tools to set up CloudHike.dev as the backend for this app.

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The part I care about most is the remote MCP server. Once you add it to your editor, your agent can do the backend setup itself.

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