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.

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Recent updates

v2.34.x (2026-09)

• i18n / IDE: full tool-copy localization with an instance-level lang, plus auth site / region params so international-site login and region routing resolve correctly
• Cloud API: callCloudApi service allowlist widened to 57 with built-in version mapping (multi-version services such as tke / mongodb / vod require an explicit version)
• Deploy / Env: new appBuild tool with hosting build neutralization; queryEnv reports the region actually applied and domains honors a passed envId
• Skills / Docs: skill fallback reads now point at the official distribution repo with a references address list; SDK-first database decision gate for cloudrun; site doc links moved to the current Markdown addresses; post-deployment share offered after delivery in the expert packs and the deploy skills (opt-in, redacted, at most once)
• Deploy / Apps: the cloud upload channel now completes end to end (deployApp accepts the timestamp getUploadUrl returns, getBuildLog accepts the build ID a deploy returns), and gateway route creation verifies the upstream exists before writing
• Skills / Context: new PostgreSQL access-pattern best-practices skill (batching, indexes, RLS role gating, launch capacity); searchKnowledgeBase's inline skill / OpenAPI catalogs now load on demand, cutting the 43-tool surface by 9.1% on every tools/list
• Runtime / Hosting: CloudRun Function mode must not bind PORT (the function framework does) and gets a credential decision gate naming CLOUDBASE_APIKEY; hosting paths and prefixes are normalized so a leading slash can no longer read as an empty directory

v2.33.x (2026-09)

• Functions / Apps: custom container-image deploy for cloud functions with async status query; cloud upload channel (getUploadUrl + deployApp cosTimestamp)
• Env binding: cloudbaserc.json as field-level fallback for envId / region / site (literal + {{env.KEY}})
• Errors / Skills: centralized error guidance by structured Code; virtual-pay reference; CodeBuddy IDE MCP upgrade skill; WorkBuddy experts
• Cloud API / Deploy: callCloudApi opens monitor & postgres services; declarative deploy with deployPlan / deployApply; cloud-api-operations skill

Releases · Star · 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.

npx plugins add TencentCloudBase/cloudbase-plugin

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

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 evaluation: cloud VM vs CloudBase

Install & connect

Prerequisites

• Node.js v18.15.0+
• A CloudBase environment
• An AI tool that supports Plugin / Skills / MCP

Setup options

1. Plugin (when the tool supports it)
   npx plugins add TencentCloudBase/cloudbase-plugin
2. CloudBase AI CLI
   npm i -g @cloudbase/cli && tcb ai
3. Manual MCP (write the IDE config file)

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

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

Others: IDE setup guide.

MCP connection modes

Remote (recommended): your IDE connects over HTTP to Tencent Cloud MCP. No local Node, and interactive clients finish authorization in the browser.

Pick the endpoint matching the site your environment lives in—the two sites use separate accounts, consoles, and credentials.

Local: runs npx @cloudbase/cloudbase-mcp@latest on your machine—full features, including local filesystem upload and template download.

{
  "mcpServers": {
    "cloudbase": {
      "type": "http",
      "url": "https://tcb-api.cloud.tencent.com/mcp/v1?env_id=<env_id>",
      "headers": {
        "X-TencentCloud-SecretId": "<Tencent Cloud Secret ID>",
        "X-TencentCloud-SecretKey": "<Tencent Cloud Secret Key>"
      }
    }
  }
}

Remote auth takes one of two paths:

• OAuth (interactive, recommended): add the URL only. Your IDE opens the browser for login and consent—no keys in config files.
• Static credentials (CI/CD): add env_id with the X-TencentCloud-* headers above.

Remote trade-off: local-filesystem features (upload, template download) are unavailable, and the international site does not yet expose NoSQL tools.

Remote URLs also accept site (domestic / intl, only needed for a China-site environment in ap-singapore, where the region is ambiguous), plus enable_plugins / disable_plugins to trim tools. Canonical names live in mcp/src/server.ts.

Remote MCP E2E (official SDK client): npm run test:hosted-mcp:e2e — see tests/hosted-mcp-e2e/README.md for apikey/OAuth modes, TLS-insecure staging, and env vars. Missing credentials skip (exit 0).

Self-hosted Cloud Mode: set CLOUDBASE_MCP_CLOUD_MODE=true (or MCP_CLOUD_MODE=true) so local file and process tools are disabled for remote callers.

Example

Online Gomoku: describe the need; get Web + cloud database / realtime and deploy.
Demo: Gomoku · More: tutorials

Docs

• MCP tools (tools.json)
• Getting started
• IDE setup
• AI plugins
• Templates
• FAQ

FAQ

{ "site": "domestic", "region": "ap-singapore", "envId": "your-env-id" }

Community

Activity

Contributors

MIT · TencentCloudBase