How to Use the Fireflies.ai MCP in Mastra AI

Automate meeting workflows in Mastra AI. Build agents that act on transcript data and manage your meeting lifecycle.

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Connect Fireflies.ai MCP to Mastra AI

Create your Vinkius account to connect Fireflies.ai 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 Fireflies.ai with Mastra AI

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Automated meeting management in Mastra AI

Trigger actions based on meeting status. Use `add_to_live_meeting` to invite your bot to calls automatically when your workflow engine detects a scheduled event. This keeps your records up to date without manual intervention. The integration handles the connection state so your agents focus on the task.

Conditional transcript analysis

Route your agents based on meeting content. Call `get_transcript` and feed the output into your agent's decision logic to determine the next step in your pipeline. If the meeting contains specific keywords, your Mastra AI workflow can trigger a follow-up task. It makes your agents reactive to actual conversation data.

Programmatic transcript cleanup

Maintain your data hygiene by using `delete_transcript` inside your maintenance workflows. You can prune old data automatically after your agents finish their analysis. Keep your workspace clean by scheduling these deletions. Your workflow engine ensures these tasks run reliably without human oversight.

Setup guide

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

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

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

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Common questions about Fireflies.ai MCP in Mastra AI

You can. By adding the server to your agent definition, you gain access to all 12 tools for meeting and transcript control.

Use the server to list meetings and then execute your workflow logic. You can chain tools together to perform complex multi-step tasks.

It does. You can enable tool approval for sensitive operations like deleting transcripts, forcing a human to review the action before it executes.

It stays within your defined workflow context. You control the flow of transcript information through your agent's memory and state.

Access is restricted to your authenticated environment. Every request for transcript text is logged and scoped to your specific API permissions.

Use it with your favorite AI tools

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

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Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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