Enhance your Mastra applications with real-time social media context from Membit. This integration allows your AI agents to access up-to-the-minute discussions, trends, and insights from platforms like X (Twitter), Farcaster, and more.

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Prerequisites

Before you begin, ensure you have:
  • Node.js 20 or higher installed
  • A Membit account with API access
  • Basic familiarity with Mastra agents
  • TypeScript knowledge (recommended)
You’ll need valid Membit API credentials and the MCP remote URL. If you don’t have access yet, get your API key to get started.

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Installation

Install the required packages for Mastra and MCP integration: 
# Install Mastra with MCP support
npm install @mastra/mcp@latest

# Install MCP remote client
npm install -g mcp-remote
Mastra works best with TypeScript. Make sure your project is configured for TypeScript development.

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Quick Start

1

Import required modules

Import the necessary components for Mastra and MCP integration:
import { openai } from "@ai-sdk/openai";
import { Agent } from "@mastra/core/agent";
import { LibSQLStore } from "@mastra/libsql";
import { MCPClient } from "@mastra/mcp";
2

Initialize MCP client

Create an MCP client to connect to Membit’s tools:
export const mcp = new MCPClient({
  servers: {
    membit: {
      command: "npx",
      env: {
        MEMBIT_API_KEY: <your-api-key>,
      },
      args: [
        "mcp-remote",
        "https://mcp.membit.ai/mcp",
        "--header",
        "X-Membit-Api-Key:${MEMBIT_API_KEY}",
      ],
    },
  },
});
Make sure you have mcp-remote installed globally via npm for this to work.
Replace <your-api-key> with your actual Membit API key. Keep this credential secure and don’t share it with unauthorized users.
3

Create your Membit-powered agent

Create a Mastra agent with Membit tools and memory:
export const membitAgent = new Agent({
  name: "Social Media Analyst",
  instructions: `You are a social media analyst with access to real-time data. Use cluster_search to find trending discussions, cluster_info for detailed analysis, and post_search for specific posts.`,
  model: openai("gpt-4o-mini"),
  tools: await mcp.getTools(),
});
4

Use your agent

Execute your agent with social media analysis tasks:
const result = await membitAgent.generate(
  "What are the most trending discussions about AI today? Use cluster_search to find hot topics and provide insights with URLs."
);

console.log(result.text);
Your Mastra agent is now powered by real-time social media context from Membit!

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Complete Example

Here’s a full working example that demonstrates the integration: 
import { openai } from "@ai-sdk/openai";
import { Agent } from '@mastra/core/agent';
import { LibSQLStore } from '@mastra/libsql';
import { MCPClient } from "@mastra/mcp";

// Initialize MCP client for Membit
export const mcp = new MCPClient({
servers: {
membit: {
command: "npx",
env: {
// Replace `<your-api-key>` with your actual Membit API key.
MEMBIT_API_KEY: <your-api-key>,
},
args: [
"mcp-remote",
"https://mcp.membit.ai/mcp",
"--header",
"X-Membit-Api-Key:${MEMBIT_API_KEY}",
],
},
},
});

// Create Membit-powered agent
export const membitAgent = new Agent({
name: 'Membit Social Analyst',
instructions: `You are an expert social media analyst with access to real-time trending data. 
  Use cluster_search to find trending discussions, cluster_info to dive deeper into specific conversations, 
  and post_search to find individual posts. Always provide context and source URLs.`,
model: openrouter("anthropic/claude-3-5-sonnet-20241022"),
tools: await mcp.getTools(),
});

async function main() {
try {
const result = await membitAgent.generate(
"What are the most trending discussions today related to Bitcoin? Use cluster_search to find hot topics and provide the best conversations to follow with URLs."
);

    console.log("Analysis Results:");
    console.log(result.text);

} catch (error) {
console.error("Error during analysis:", error);
}
}

main();

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Troubleshooting