How to Use the Twelve Data MCP in Mastra AI

Q: How can I ensure reliable historical data fetching with Twelve Data and Mastra AI?

Use gettimeseries within your workflow. Because Mastra handles failure, if the connection drops while pulling OHLCV candle data, it will automatically retry until successful.

Q: Can Mastra AI handle multiple indicator checks using Twelve Data?

Absolutely. You can build a conditional path: check getbollingerbands first; if the price is overbought, then run getmacd to confirm momentum before taking action.

Q: Does Twelve Data support multiple asset types in Mastra AI?

Yes. You can build workflows that check forex rates (getexchangerate) and then proceed to check stock data, all within a single, robust process.

Build complex financial workflows that retry automatically using Mastra AI.

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Connect Twelve Data MCP to Mastra AI

Create your Vinkius account to connect Twelve Data 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 Twelve Data with Mastra AI

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Run multi-step market analysis

Need a process that checks multiple signals before moving on? You can build this flow with `get_rsi` and `get_stochastic`. If one indicator is oversold, the workflow continues to check another. If fetching the full profile fails, Mastra's auto-retry logic kicks in. It will automatically attempt the call again using a different method or backoff interval.

Manage complex asset lookups

The process starts by listing assets: use `get_stock_list` to find symbols, then `search_symbols` for specifics. This sequence of calls ensures the workflow only proceeds with valid inputs. If the initial symbol search fails, you can trigger a fallback step that checks crypto availability using `get_crypto_list` instead.

Handle currency exchange sequences

Currency workflows are common. Define a path: first get the list of forex pairs with `get_forex_list`. Then, if needed, call `get_exchange_rate` to convert an amount. Mastras handles failure here gracefully. If the rate fetch times out, it automatically notifies an admin and retries later.

Setup guide

Set up Twelve Data 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 Twelve Data 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: "twelve-data-mcp-client",
  servers: {
    "twelve-data-mcp": {
      url: new URL(
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
      ),
    },
  },
});

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

const result = await agent.generate(
  "List recent Twelve Data 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 Twelve Data. 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 Twelve Data MCP in Mastra AI

Use `get_time_series` within your workflow. Because Mastra handles failure, if the connection drops while pulling OHLCV candle data, it will automatically retry until successful.

Absolutely. You can build a conditional path: check `get_bollinger_bands` first; if the price is overbought, then run `get_macd` to confirm momentum before taking action.

The workflow won't crash. Mastra detects the failure and can execute a defined fallback step, like listing available stocks using `get_stock_list` instead.

Yes. You can build workflows that check forex rates (`get_exchange_rate`) and then proceed to check stock data, all within a single, robust process.

This server handles public financial market data. The types include symbol lists, exchange rates, and technical indicator values.

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