How to Use the Woodpecker MCP in Google ADK

Q: How can I analyze campaign metrics with Woodpecker and Google ADK?

Use the getcampaignstats tool. Because you’re in the Gemini ecosystem, your agent doesn't just read the stats; it reasons over them, connecting them to other large datasets like those in BigQuery.

Q: Can I list campaigns using Woodpecker and Google ADK?

Absolutely. The listcampaigns tool provides a complete roster of your sequences. This allows your agent to build operational oversight across multiple parts of the Google Cloud infrastructure.

Q: If I add prospects with Woodpecker, how does Google ADK handle it?

Your agent calls addprospect, and the data is immediately available for long-context processing. The MCP Server ensures the input is accepted by the system, making the new prospect visible to other integrated services.

Q: What happens if I need to manage webhooks with Woodpecker using Google ADK?

You run listwebhooks. The agent gets a definitive list of what's connected. This visibility is crucial when you’re building complex, multi-service pipelines on Google Cloud.

Manage Cold Email Sequences with Google ADK.

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Connect Woodpecker MCP to Google ADK

Create your Vinkius account to connect Woodpecker 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 Woodpecker with Google ADK

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Review Outreach Campaigns

Get a detailed report on any campaign using `get_campaign_stats`. Because this connects to the Google Cloud stack, your agent can process these stats alongside massive datasets in BigQuery. You'll get context that goes way beyond standard reporting. To see all available campaigns, call `list_campaigns`. The ADK framework treats the MCP Server tools as first-class citizens, allowing for deep reasoning over campaign management data.

Build and Add New Leads

Need a new contact in your system? Your agent calls `add_prospect` to build them directly. This integrates the lead into the broader Google Cloud environment, making it immediately accessible for further analysis. If you need an inventory of existing leads, use `list_prospects`. The long-context reasoning capabilities of Gemini models can correlate these prospects with other massive data structures within your enterprise setup.

Pause or Resume Campaigns

When a sequence needs to stop, the agent runs `pause_campaign`. This is critical for controlled deployment in an enterprise setting. The action is recorded and tracked alongside other operational data. To kick off outreach again, use `resume_campaign`. These functions give your agents granular control over Woodpecker's sending schedule without needing manual intervention.

Setup guide

Set up Woodpecker 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 Woodpecker 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="Woodpecker_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Woodpecker tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Woodpecker MCP in Google ADK

Use the `get_campaign_stats` tool. Because you’re in the Gemini ecosystem, your agent doesn't just read the stats; it reasons over them, connecting them to other large datasets like those in BigQuery.

Absolutely. The `list_campaigns` tool provides a complete roster of your sequences. This allows your agent to build operational oversight across multiple parts of the Google Cloud infrastructure.

Your agent calls `add_prospect`, and the data is immediately available for long-context processing. The MCP Server ensures the input is accepted by the system, making the new prospect visible to other integrated services.

You run `list_webhooks`. The agent gets a definitive list of what's connected. This visibility is crucial when you’re building complex, multi-service pipelines on Google Cloud.

The server manages prospect records, campaign configurations, and webhook definitions. Your agent interacts with these structured data points via the specified tools.

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