How to Use the Fireflies.ai MCP in Google ADK

Integrate Fireflies.ai with Google ADK to drive enterprise workflows using Gemini.

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Connect Fireflies.ai MCP to Google ADK

Create your Vinkius account to connect Fireflies.ai to Google ADK 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 Google ADK

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Google ADK and Fireflies.ai meeting ingestion

Use `upload_audio` to push files into your Google Cloud pipeline for analysis. Your agents can then access these files to fuel long-context reasoning with Gemini models. This MCP Server provides the plumbing to move data from recordings into your BigQuery datasets. It turns raw audio into searchable, structured entries without extra middle-ware.

Search meetings through Google ADK

Call `list_transcripts` to pull meeting history into your agent's context window. Gemini can then reason over thousands of tokens to find the specific insights you need. Use `list_soundbites` to isolate key moments. It provides a quick way for your agent to grab relevant clips without scanning entire meeting files.

Automate team workflows in Google ADK

The `list_team_users` tool lets your agent map meeting participants to your internal Google Cloud identity store. This keeps your records accurate across platforms. You can also use `update_meeting_title` to keep your project logs organized. Your agent manages the metadata, so you don't have to touch the dashboard.

Setup guide

Set up Fireflies.ai MCP in Google ADK

Prerequisites

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

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Fireflies.ai tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Fireflies.ai_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Fireflies.ai tools via MCP.",
    tools=mcp_tools,
)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Fireflies.ai. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Fireflies.ai MCP in Google ADK

You can restrict which tools are visible to your agent by passing a list to the toolset configuration. This keeps your agent focused on specific tasks like `ask_fred`.

The server uses efficient HTTP transport, which plays well with Google Cloud's networking. You can set timeouts within your agent's tool configuration to manage response times.

Yes. Once the MCP Server is connected, your agent treats the tools as native capabilities. This lets your Vertex AI models act on meeting data immediately.

Data is encrypted in transit between the server and your client. You maintain control over your API keys and the environment where your agent executes.

The toolset returns standard error codes. Your agent can catch these and retry or log the failure, preventing silent data loss during your pipeline runs.

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