How to Use the Chuanglan 253 / 创蓝 MCP in Mastra AI

Wire up resilient SMS pipelines and automated KYC checks using Mastra AI conditional workflows.

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Connect Chuanglan 253 / 创蓝 MCP to Mastra AI

Create your Vinkius account to connect Chuanglan 253 / 创蓝 to Mastra AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Chuanglan 253 / 创蓝 with Mastra AI

Ask AI about this MCP

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Claude

Perplexity

Build fault-tolerant Mastra AI message flows

The `send_intl_sms` tool dispatches global text messages straight from your workflow nodes. Your agent drafts the copy, fires the request, and evaluates the response. If the network drops the message, the built-in exponential backoff automatically tries again. Hardcoding retry logic is a massive waste of time. You just drop the tool into your agent graph and let the framework handle the failures. It guarantees critical alerts actually reach the recipient's phone.

Automate Chinese identity validation

Running `verify_identity` and `verify_phone` lets your system validate users before granting access. Your workflow hits the Chuanglan database to check the name, ID, and mobile number. A failed check routes the user to a manual review queue instead of rejecting them outright. Fraudsters move fast, so your verification loops need to keep up. We added `flash_check` to speed up mobile logins. The agent handles the entire authentication branch without human input.

Track campaign delivery at scale

The `pull_sms_reports` tool grabs bulk delivery data from your recent campaigns. You configure a scheduled node to fetch these logs every hour. The agent parses the raw data and updates your internal database with the exact status of every text. Blindly sending messages ruins your sender reputation. Adding `query_sms_status` into the mix lets you isolate specific bounces. You can map out a workflow that flags dead numbers and removes them from future lists.

Setup guide

Set up Chuanglan 253 / 创蓝 MCP in Mastra AI

Prerequisites

Node.js 18+ and a TypeScript project
@mastra/mcp + @mastra/core packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run npm install @mastra/mcp @mastra/core plus your preferred model provider (e.g. @ai-sdk/openai).
2

Configure the MCPClient
Create an MCPClient with your Vinkius endpoint as a URL object. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and inject tools
Call mcpClient.listTools() and spread the result into your agent's tools object. All Chuanglan 253 / 创蓝 tools become native Mastra tools.
4

Run with any model
Swap openai("gpt-4o") for any AI SDK-compatible provider. Call agent.generate() and the agent routes tool calls through MCP automatically.

agent.ts

import { MCPClient } from "@mastra/mcp";
import { Agent } from "@mastra/core/agent";
import { openai } from "@ai-sdk/openai";

const mcpClient = new MCPClient({
  id: "chuanglan-253-mcp-client",
  servers: {
    "chuanglan-253-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

const agent = new Agent({
  name: "Chuanglan 253 / 创蓝 Agent",
  model: openai("gpt-4o"),
  instructions: "You have access to Chuanglan 253 / 创蓝 tools.",
  tools: {
    ...(await mcpClient.listTools()),
  },
});

const result = await agent.generate(
  "List recent Chuanglan 253 / 创蓝 transactions"
);
console.log(result.text);

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Chuanglan 253 / 创蓝. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Chuanglan 253 / 创蓝 MCP in Mastra AI

Install @mastra/mcp@latest and initialize a new MCPClient. Pass your Vinkius URL into the server config. Call listTools() and spread them into your agent definition.

That is exactly what the workflow engine does. If a text bounces, the system uses exponential backoff to attempt delivery again. You set the max retries, and it handles the rest.

You can enforce a human-in-the-loop step. Set requireToolApproval on the sending tools. The workflow pauses and waits for your confirmation before spending credits.

You define the conditional logic. If the phone verification tool returns a mismatch, your agent routes the session to a fallback node. The user gets asked for secondary documentation automatically.

Your workflow processes Chinese national IDs and mobile numbers in memory. The Vinkius MCP Server receives this payload in a zero-trust sandbox and forwards it to the API. We wipe the container entirely once the verification completes.

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