How to Use the Meltwater MCP in Mastra AI

Build resilient media monitoring workflows in Mastra AI with automated error handling.

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Connect Meltwater MCP to Mastra AI

Create your Vinkius account to connect Meltwater 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 Meltwater with Mastra AI

Ask AI about this MCP

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Claude

Perplexity

Automated media monitoring pipelines

Chain `list_saved_searches` and `search_content` into your Mastra agents. If a search hits a threshold, your workflow can automatically trigger a secondary action. This prevents the manual overhead of tracking news. The agent handles the discovery while your workflow logic manages the response, including retries if the network blips.

Conditional branching for PR crises

Use `get_search_analytics` to set up logic gates. If volume spikes above a defined constant, have Mastra branch into an escalation workflow. This keeps your response consistent. You define the rules once in your Mastra code, and the agent executes the logic whenever the data conditions are met.

Multi-step content export workflows

Automate your reporting by linking `list_content_exports` to your notification agents. The system can poll for new files and move them into your storage buckets. This removes the need for manual file handling. Your agent watches the export list and acts the moment a new file appears, ensuring your team stays updated.

Setup guide

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

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

const result = await agent.generate(
  "List recent Meltwater transactions"
);
console.log(result.text);

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Common questions about Meltwater MCP in Mastra AI

You register the tools within your Mastra agent definition. Once connected, your agents can call `search_content` or `list_tags` as part of their workflow steps.

Yes. Mastra has built-in retry logic. If a request to the server fails, the framework handles the backoff and attempts again before giving up.

It does. You can write conditional logic that checks the output of any tool. If the data meets your criteria, the agent follows the path you defined.

Store them in your environment variables. The MCP server uses these to authenticate your requests, keeping your keys out of the agent logic.

It exposes structured news and social media metadata. This includes search results, analytics, and content tags, all delivered as clean JSON for your agent to process.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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