How to Use the Fireflies.ai MCP in OpenAI Agents SDK

Use the OpenAI Agents SDK to connect your production pipeline directly to Fireflies.ai meeting data.

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Connect Fireflies.ai MCP to OpenAI Agents SDK

Create your Vinkius account to connect Fireflies.ai to OpenAI Agents SDK 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 OpenAI Agents SDK

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Real-time meeting control with OpenAI Agents SDK

Trigger `add_to_live_meeting` inside your agent workflow to pull live calls into your stack. Your agent handles the invite and monitors the session without manual intervention. This MCP Server exposes the full toolset to your agent. Use `ask_fred` to pull context from active calls for immediate decision-making within your Python environment.

Manage transcripts within OpenAI Agents SDK

Fetch transcripts using `get_transcript` and process the text through your agent's internal guardrails. You get precise control over how meeting data enters your system. Automate your cleanup by calling `delete_transcript` when your agent finishes its task. It’s a direct way to keep your storage clean while maintaining high-fidelity logs.

Analyze meeting output in OpenAI Agents SDK

Run `get_ai_app_outputs` to extract structured insights directly from Fireflies.ai into your agent's memory. This bypasses manual parsing and gets you straight to the data you need. Your agent can list past entries with `list_transcripts` to build a history. It handles the heavy lifting, allowing you to focus on the logic behind your agent's actions.

Setup guide

Set up Fireflies.ai MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Fireflies.ai tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Fireflies.ai tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Fireflies.ai tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Fireflies.ai Agent",
            instructions="You have access to Fireflies.ai tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Fireflies.ai MCP in OpenAI Agents SDK

You manage authentication by passing your API key via environment variables. The server handles the handshake, ensuring your agent only accesses what you explicitly authorize.

Yes. The underlying transport uses async handles, so your agent won't block while waiting for `upload_audio` to finish processing your files.

Absolutely. Since the tools are exposed as standard functions, your existing validation logic checks the inputs and outputs of every tool call automatically.

The tools return structured data, not loose text. By using specific calls like `get_transcript`, you provide the agent with grounded facts rather than raw, ambiguous input.

Your meeting transcripts and audio files stay within your defined scope. The server acts as a bridge, and no data is shared with third parties outside your configured environment.

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