How to Use the Agendor MCP in Google ADK

Q: How do Gemini models interact with Agendor pipeline data?

Gemini pulls your sales data using listdeals and processes it using its long-context window. This allows the model to spot bottlenecks across hundreds of active negotiations.

Connect Gemini's 1M token context to your Agendor CRM using the Google ADK for deep pipeline analysis.

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Connect Agendor MCP to Google ADK

Create your Vinkius account to connect Agendor 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 Agendor with Google ADK

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Deep Pipeline Analysis via Google ADK

The `list_deals` tool feeds your active sales opportunities and negotiation pipelines directly into Gemini's massive context window. This lets your enterprise agent analyze months of deal history in a single turn. You can combine this MCP Server with your BigQuery datasets to run predictive models on your sales cycle. The agent pulls live pipeline data and matches it against historical trends automatically.

Cloud-Scale Contact Creation

The `create_person` and `create_organization` tools allow your Google Cloud agents to write new leads directly into your CRM. You can trigger these writes from incoming emails processed by Vertex AI. Setting this up requires passing the transport parameters to McpToolset in your Python code. You can also use the tool names filter to restrict your agent to write-only operations if needed.

Structured Directory Queries

The `list_organizations`, `list_people`, and `list_tasks` tools expose your entire directory through our MCP setup. Your agent reads these endpoints to schedule follow-ups based on real-time availability. Because the ADK integrates natively with Google Cloud, you can run these lookups securely within your existing VPC. The agent uses the fetched records to update calendar invites and task queues.

Setup guide

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

Use McpToolset with StreamableHttpServerParameters pointing to your Vinkius URL. Pass that toolset directly to your LlmAgent instance in Python.

Yes, you can use the optional tool names filter when creating your toolset. This lets you expose only list_tasks while hiding write operations like create_person.

It does, but for hosted Vinkius environments, you will use the streamable HTTP transport. This keeps your agent serverless and easy to deploy on Cloud Run.

Gemini pulls your sales data using list_deals and processes it using its long-context window. This allows the model to spot bottlenecks across hundreds of active negotiations.

Your CRM payloads pass directly through ephemeral V8 sandboxes without persistent caching. We secure your credentials using single-token authorization to ensure no unauthorized Google Cloud services can access your pipeline.

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