How to Use the SleekFlow MCP in Google ADK

Q: How do I check contact details with SleekFlow and Google ADK?

Just call getcontactdetails. This retrieves all necessary info on one specific person. Your agent can then feed that data directly into BigQuery for analysis.

Q: What channels does the MCP Server support for SleekFlow and Google ADK?

The listchannels tool shows connections to WhatsApp, Instagram, and other platforms. This confirms which endpoints your agent can use to communicate.

Q: How do I track conversations with SleekFlow and Google ADK?

The listconversations tool is what you need. It tracks every chat thread across all connected channels, giving the full context needed for complex reasoning in Vertex AI.

Q: Can I list labels with SleekFlow and Google ADK?

Yes. Use listcontactlabels to see all tags applied to contacts. This helps your agent categorize users accurately, which is vital for accurate BigQuery reporting.

Action customer data in BigQuery with Google ADK and SleekFlow.

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Connect SleekFlow MCP to Google ADK

Create your Vinkius account to connect SleekFlow to Google ADK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup SleekFlow with Google ADK

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Map out all communication channels using MCP Server

The `list_channels` tool shows every communication endpoint connected to SleekFlow. You'll immediately see if WhatsApp, Instagram, or another channel is live and ready for action. For a full overview of available automation processes, check out `list_automation_flows`. This lets your agent know what workflows are possible.

Manage conversation history with Google ADK

`list_conversations` provides a record of every chat thread across all platforms. Your agent can use this to understand the full context before acting on behalf of the user. If you need to write back, `send_message` handles sending replies into an existing conversation using its unique ID.

Get specific contact data with MCP Server

Use `get_contact_details` to pull deep information on a single person. This is crucial for updating records in BigQuery before the agent takes action. You can also list all contacts using `list_contacts`, giving your model a complete dataset of who's talking to you.

Setup guide

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

Just call `get_contact_details`. This retrieves all necessary info on one specific person. Your agent can then feed that data directly into BigQuery for analysis.

The `list_channels` tool shows connections to WhatsApp, Instagram, and other platforms. This confirms which endpoints your agent can use to communicate.

The `list_conversations` tool is what you need. It tracks every chat thread across all connected channels, giving the full context needed for complex reasoning in Vertex AI.

Yes. Use `list_contact_labels` to see all tags applied to contacts. This helps your agent categorize users accurately, which is vital for accurate BigQuery reporting.

The server deals with contact details (names, IDs) and conversation messages. If you're mapping this to BigQuery, make sure you structure your schema around these key identifiers.

Use it with your favorite AI tools

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