How to Use the Fireflies.ai MCP in Mastra AI

Q: How do I handle transcription failures in my Mastra AI and Fireflies.ai pipeline?

Wrap uploadaudio inside a Mastra workflow step. If the API rate limit hits, the framework's automatic retry logic keeps retrying via the MCP Server tool until the audio processes successfully.

Q: Can I build multi-step agent flows using Fireflies.ai and Mastra AI?

Yes, you can chain listtranscripts to find recent calls, then pass those IDs to getaiappoutputs to extract action items. Mastra handles the data flow between these tools cleanly.

Q: How do I manage user permissions when using Fireflies.ai with Mastra AI?

Call listteamusers to verify user identities inside your agent's execution loop. This prevents unauthorized users from calling destructive tools like deletetranscript.

Q: Can I trigger workflows when a meeting ends?

Yes, register your webhook endpoints and use listwebhooks within Mastra AI to verify the subscription. When the webhook fires, Mastra starts your post-meeting analysis pipeline.

Build resilient multi-step meeting workflows with Mastra AI and automated transcription tools.

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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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Build self-healing meeting ingestion pipelines

`upload_audio` initiates the transcription of raw audio files within your Mastra AI workflow. This MCP Server integration handles the transfer securely, shielding your core app logic from network drops. Once the file processes, `get_transcript` retrieves the raw text. This lets your agent analyze the call content and trigger subsequent steps without manual intervention.

Coordinate team-wide follow-ups using Mastra AI

`list_team_users` pulls your active workspace directory into the Mastra AI agent memory. The agent uses this list to match speakers with actual team profiles before assigning action items. If a team member needs to approve a summary, you can configure Mastra's human-in-the-loop feature to pause execution. After approval, the agent updates the meeting details to keep records clean.

Automate webhook dispatch and live queries

`list_webhooks` checks your active integrations to ensure real-time events route through the MCP Server correctly. When a new meeting ends, your Mastra AI agent receives the event and instantly triggers post-meeting tasks. For immediate queries, `ask_fred` allows your agent to ask specific questions about the meeting content. This lets you build automated Slack alerts that ping the team with answers right after a call wraps.

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-alternative-mcp-client",
  servers: {
    "firefliesai-alternative-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

Wrap `upload_audio` inside a Mastra workflow step. If the API rate limit hits, the framework's automatic retry logic keeps retrying via the MCP Server tool until the audio processes successfully.

Yes, you can chain `list_transcripts` to find recent calls, then pass those IDs to `get_ai_app_outputs` to extract action items. Mastra handles the data flow between these tools cleanly.

Call `list_team_users` to verify user identities inside your agent's execution loop. This prevents unauthorized users from calling destructive tools like `delete_transcript`.

Yes, register your webhook endpoints and use `list_webhooks` within Mastra AI to verify the subscription. When the webhook fires, Mastra starts your post-meeting analysis pipeline.

The MCP Server acts as an ephemeral bridge, passing your transcripts and audio data directly to the API. No meeting data is cached locally or written to disk within the sandbox.

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