How to Use the FlowiseAI MCP in Google ADK

Q: How do I register the FlowiseAI MCP Server in Google ADK?

Use McpToolset with StreamableHttpServerParameters pointing to your Vinkius URL. Pass this toolset directly into your LlmAgent tools list to make all 12 visual flow tools available to Gemini.

Q: Can I restrict which FlowiseAI tools my Google ADK agent can access?

Yes, you can use the optional toolnames filter when initializing your toolset. This lets you expose only executechatflowprediction while hiding administrative tools like listflowisecredentials.

Q: How does Google ADK handle long-context data with FlowiseAI?

Your Gemini model can ingest large payloads and use upsertvectordata to chunk and write them. The MCP Server processes these requests efficiently, letting you move data from Google Cloud into your vector stores.

Q: Can my agent retrieve user feedback from visual flows?

Yes, your agent uses listchatfeedback to read user ratings and comments. This allows your Google ADK application to analyze performance metrics and adjust its behavior dynamically.

Q: Where are my FlowiseAI credentials stored when using this MCP Server?

All API tokens and keys accessed via listflowisecredentials are managed within Vinkius's zero-trust V8 sandbox. They are never logged or stored on Google Cloud, keeping your visual flow credentials isolated.

Give your Google ADK agents direct access to FlowiseAI visual pipelines and vector stores using our high-performance MCP Server.

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Connect FlowiseAI MCP to Google ADK

Create your Vinkius account to connect FlowiseAI 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 FlowiseAI with Google ADK

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Connect Google ADK to visual FlowiseAI chatflows

The `execute_chatflow_prediction` tool lets your Gemini agent invoke visual pipelines from your Python code. This lets you combine Gemini's 1M token context window with complex visual RAG structures built inside FlowiseAI. Your agent inspects the available pipelines using `list_chatflows` to decide which visual flow fits the current user request. This creates a flexible routing layer where Google ADK handles the high-level reasoning and FlowiseAI executes the underlying steps.

Feed BigQuery data into FlowiseAI vector stores

The `upsert_vector_data` tool allows your Google ADK agent to push processed enterprise data directly into your vector databases. You can pull raw data from BigQuery using Google's native tools, then pass it to this MCP Server to handle the embedding. The agent verifies the target vector store setup using `get_chatflow_details`. This ensures that your enterprise data lands in the correct collection without manual database configuration or visual editing.

Manage enterprise credentials via Google ADK

The `list_flowise_credentials` tool exposes configured service accounts and API keys to your Gemini agent. This allows your Google Cloud pipelines to interact with external services without hard-coding credentials in your repository. Your agent checks system variables with `list_flow_variables` to maintain state across long-running enterprise tasks. This keeps your visual flows synchronized with your Google Cloud infrastructure.

Setup guide

Set up FlowiseAI 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 FlowiseAI 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="FlowiseAI_agent",
    model="gemini-2.0-flash",
    instruction="You have access to FlowiseAI 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 FlowiseAI. 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 FlowiseAI MCP in Google ADK

Use `McpToolset` with `StreamableHttpServerParameters` pointing to your Vinkius URL. Pass this toolset directly into your `LlmAgent` tools list to make all 12 visual flow tools available to Gemini.

Yes, you can use the optional `tool_names` filter when initializing your toolset. This lets you expose only `execute_chatflow_prediction` while hiding administrative tools like `list_flowise_credentials`.

Your Gemini model can ingest large payloads and use `upsert_vector_data` to chunk and write them. The MCP Server processes these requests efficiently, letting you move data from Google Cloud into your vector stores.

Yes, your agent uses `list_chat_feedback` to read user ratings and comments. This allows your Google ADK application to analyze performance metrics and adjust its behavior dynamically.

All API tokens and keys accessed via `list_flowise_credentials` are managed within Vinkius's zero-trust V8 sandbox. They are never logged or stored on Google Cloud, keeping your visual flow credentials isolated.

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