CloudBase AI ToolKit

CloudBase AI Toolkit

AI writes the code. CloudBase runs the backend.

The CloudBase integration layer for AI coding tools: Plugin installs the stack, Skills steer how code is written, MCP operates databases, functions, storage, and deploys from chat.

English · 简体中文 · [Docs][docs] · [Changelog][changelog] · [Issues][github-issues-link]

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Recent updates

v2.31.x (2026-08)

• CloudRun: getDeployLog failures (CODING login / image deploy with no build) now rewrite to getProcessLog / getDeployRecords next steps instead of raw English errors

v2.30.x (2026-08)

• Env: cross-region environment query and clearer credential-scope boundary for MCP auth
• Auth: drop redundant login_mode; keep credential_scope as the single credential boundary signal
• Release: Kimi plugin zip uses a whitelist-only, version-free asset name
• Kimi: plugin zip assembles sibling skills into skills/cloudbase/references/ so the archive stays self-contained and in sync with the manifest contract

v2.29.x (2026-08)

• IDE: Kimi Code plugin (kimi.plugin.json) reuses shared CloudBase skills/MCP assets
• Release: pack Kimi plugin zip and attach it to GitHub Release assets automatically
• Auth UX: Kimi login via MCP auth device-code flow; routing skill loads domain knowledge on demand

[Releases][changelog] · [Star][github-stars-link] · Watch → Releases

What it is

AI IDEs (Cursor, Claude Code, Codex, CodeBuddy, and others) are strong at generating code. What usually blocks you is the backend: schemas, permissions, functions, storage, environments, and release.

CloudBase is Tencent Cloud’s AI-native all-in-one backend (database, storage, auth, cloud functions, Cloud Run, and more). This repo is the Toolkit that connects that backend to AI tools:

This repository ships the npm package @cloudbase/cloudbase-mcp, Skills, and AI plugins.

You still need your own CloudBase environment, and you should confirm sensitive actions the AI proposes. The Toolkit provides capability and path—not judgment.

Related repositories

Publishing and sync repos live under TencentCloudBase. Directly related to this Toolkit:

Prefer Plugin for the full stack; Skills alone when you only need knowledge constraints. For marketplace IDEs use this repo—do not also run npx plugins add on the same tool.

Quick start

Fastest way to get started

Copy this AI prompt into your AI IDE. The agent reads skill.md and completes the setup:

Set up CloudBase for me:
1. Open https://docs.cloudbase.net/skill.md and complete the setup following its instructions.
2. Tell me when you're done, and suggest the most relevant next step.

Plugin

npx plugins add TencentCloudBase/cloudbase-plugin

Details and IDE differences: AI plugin docs.

MCP only

{
  "mcpServers": {
    "cloudbase": {
      "command": "npx",
      "args": ["@cloudbase/cloudbase-mcp@latest"]
    }
  }
}

Hosted HTTP, self-hosted Cloud Mode, and plugin scoping: Install & connect.

First prompts

Login to CloudBase

Use CloudBase Skills to build a todo app with login, database and permissions, then deploy

Skills shape structure and practice; MCP handles environment and resources. You should be able to verify data and APIs in your own environment—not only get local source files.

Supported AI IDEs

Full setup: IDE guides.

Capabilities

After setup, the AI can do typical backend work in your environment (confirm critical steps):

• Database: PostgreSQL and document DB, data models, CRUD, permissions and security rules
• Compute: author, deploy, invoke, and debug cloud functions / Cloud Run
• Auth & storage: login methods, object storage, permission linkage with data
• Release & ops: static hosting / Mini Program publish; inspect logs and redeploy

Fits Web, WeChat Mini Programs, and backend services. Platform overview: CloudBase docs.

Evaluation

Under controlled conditions, the same Todo application brief and frontend scaffold were used to compare two backend paths: a traditional cloud VM (self-managed runtime, process, and network exposure) and CloudBase (managed database, anonymous auth, and related backend services). An AI agent performed end-to-end development and verification. Given the model and task setup, the CloudBase path was more favorable in completion latency, token usage, and tool-call count. These outcomes are conditioned on the model, agent framework, and task definition, and should not be generalized beyond that scope.

Methods, data, and limits: [Same-task evaluati

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