How to Use the Lingyi Wanwu MCP in Mastra AI

Q: Can I use Lingyi Wanwu for complex branching in Mastra AI?

You can. Use the output from chatcompletions to trigger conditional logic in your workflow. It allows for advanced, multi-step agent behavior.

Build complex agent workflows with Lingyi Wanwu and Mastra AI for automated decision-making.

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Connect Lingyi Wanwu MCP to Mastra AI

Create your Vinkius account to connect Lingyi Wanwu 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 Lingyi Wanwu with Mastra AI

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Claude

Perplexity

Automate complex Yi workflows

Deploy agents that chain `chat_completions` and `list_models` to handle multi-step tasks. Mastra AI executes these branches automatically based on the results. If a model call fails or hits a rate limit, the framework handles the retry logic. It ensures your agent pipelines stay operational.

Enforce safety with moderation tools

Integrate `check_moderation` into your agent loops to prevent policy violations. The agent pauses or alerts an admin if the input trips a filter. It provides a necessary guardrail for autonomous agents. You control when a human needs to intervene in the process.

Manage agent model selection

Query available endpoints with `list_models` to dynamically pick the right Yi model for specific tasks. Your agent chooses the model based on current availability. This makes your workflow flexible. You can swap models without rewriting your core logic.

Setup guide

Set up Lingyi Wanwu 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 Lingyi Wanwu 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: "lingyi-wanwu-mcp-client",
  servers: {
    "lingyi-wanwu-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Lingyi Wanwu 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 Lingyi Wanwu. 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 Lingyi Wanwu MCP in Mastra AI

Import the client and spread the tools into your agent configuration. Mastra AI handles the transport and tool registration automatically.

Yes. Use the requireToolApproval setting to pause execution. You can manually approve every call made by the server before it finishes.

You can. Use the output from `chat_completions` to trigger conditional logic in your workflow. It allows for advanced, multi-step agent behavior.

The server exposes standard error states that the framework catches. You can configure automatic retries if a specific tool call fails.

All tool traffic is routed through a secure, ephemeral tunnel. We store no history on our side, and your credentials are encrypted at the transport layer.

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