How to Use the Fireflies.ai MCP in Google ADK

Q: How do I configure Fireflies.ai with Google ADK?

Use McpToolset with your Vinkius HTTP server parameters. Pass this toolset to your LlmAgent constructor. This exposes all Fireflies tools, like listtranscripts and gettranscript, to your Gemini model.

Q: Can Gemini analyze audio analytics using Fireflies.ai and Google ADK?

Yes, the agent can call getanalytics to retrieve conversation metrics like silence duration and talk ratios. The Google ADK agent can then process this data or export it to BigQuery for long-term reporting.

Q: How do I restrict which Fireflies.ai tools Google ADK can access?

You can use the optional toolnames filter in your McpToolset configuration. This restricts the agent to specific actions, like only allowing listtranscripts while blocking destructive tools like deletetranscript.

Combine Gemini's long context with Fireflies.ai through the Google ADK to analyze hours of meeting transcripts.

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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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Process massive meeting transcripts with Gemini

Large meetings produce massive text files that choke smaller models. This MCP Server lets your Google ADK agents pull complete records using `get_transcript` and feed them directly into Gemini's million-token context window. By combining this raw Fireflies.ai text with your existing BigQuery datasets, you can build enterprise Google ADK workflows that audit verbal commitments against actual database records. Your Google ADK agents can search through history using `list_transcripts` to find the exact Fireflies.ai call they need to analyze.

Inject Google ADK agents into live calls

Do not wait for Fireflies.ai meetings to end before taking action inside Google ADK. Your Google ADK agents can use `add_to_live_meeting` to dispatch the recording assistant to any live link. You can monitor ongoing Fireflies.ai sessions inside Google ADK by calling `list_active_meetings`. If a call runs long or changes focus, your Google ADK agent can use `update_meeting_title` to keep your Google Cloud Storage buckets and BigQuery records synchronized.

Query AskFred directly from your Google Cloud agent

Dig into Fireflies.ai meeting analytics without writing complex SQL queries in Google ADK. Your Google ADK agent can call `get_analytics` to pull speaker metrics directly into Vertex AI, or use `create_ask_fred_thread` to ask specific questions about the conversation. You can also list and inspect existing Fireflies.ai threads within Google ADK using `list_ask_fred_threads` and `get_ask_fred_thread`. This allows your Google ADK enterprise agents to build a historical knowledge base of verbal decisions directly inside your Google Cloud environment.

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,
)

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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

Use `McpToolset` with your Vinkius HTTP server parameters. Pass this toolset to your `LlmAgent` constructor. This exposes all Fireflies tools, like `list_transcripts` and `get_transcript`, to your Gemini model.

Yes, the agent can call `get_analytics` to retrieve conversation metrics like silence duration and talk ratios. The Google ADK agent can then process this data or export it to BigQuery for long-term reporting.

You can use the optional `tool_names` filter in your `McpToolset` configuration. This restricts the agent to specific actions, like only allowing `list_transcripts` while blocking destructive tools like `delete_transcript`.

Yes, it supports both. For cloud-deployed Google ADK agents, using the streamable HTTP transport is highly recommended to maintain persistent, secure connections.

Absolutely. Your meeting transcript text and speaker analytics are transmitted securely via HTTPS. Vinkius executes the server in an isolated sandbox, ensuring your voice data is never stored or used to train public models.

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