How to Use the Woodpecker MCP in OpenAI Agents SDK

Q: How do I check campaign activity with Woodpecker using OpenAI Agents SDK?

Just call getcampaignstats. Your agent executes this against the MCP Server and returns the necessary metrics. Since it’s built on a production SDK, you'll get full tracing of that API call in your dashboard.

Q: Can Woodpecker manage my webhooks with OpenAI Agents SDK?

Yeah, you can list them using listwebhooks. This lets your agent confirm which integrations are active. The tool is straightforward and integrates right into the standard agent workflow.

Q: What if I need to add a new prospect while building with Woodpecker and OpenAI Agents SDK?

The addprospect tool makes it simple. Your agent sends the necessary details, and Woodpecker handles adding that lead instantly. It's designed for production use, so you don't worry about data corruption.

Q: Are there limitations when using Woodpecker with OpenAI Agents SDK?

The MCP Server exposes eight specific tools, like listcampaigns and getcampaign. The agent only sees what you allow it to see. It's highly contained and safe for deployment.

Manage Cold Email Sequences with OpenAI Agents SDK.

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Connect Woodpecker MCP to OpenAI Agents SDK

Create your Vinkius account to connect Woodpecker to OpenAI Agents SDK 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 OpenAI Agents SDK

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Monitor Campaign Performance

Get a snapshot of your outreach efforts using `get_campaign_stats`. You'll see key metrics for any active sequence without leaving the agent environment. This helps you quickly determine which campaigns need attention. Use `list_campaigns` to review every campaign Woodpecker manages. The SDK auto-discovers these tools, letting your agents execute complex reporting logic straight from the OpenAI dashboard.

Manage Leads and Prospects

Your agent can expand its contact list by calling `add_prospect`. It handles the creation process entirely within Woodpecker. This means new leads are immediately available for sequencing. Need to review who you've talked to? The `list_prospects` tool pulls your entire database of contacts. Your agent can then cross-reference this list with other data sources.

Control Campaign Status

Don't forget the manual controls. If a campaign is running too fast or needs a temporary hold, your agent uses `pause_campaign`. This prevents unintended mass sending. Conversely, if you manually adjust settings and want to restart outreach, `resume_campaign` gets things moving again. These actions keep your entire email workflow contained within the MCP Server.

Setup guide

Set up Woodpecker MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Woodpecker tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Woodpecker tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Woodpecker tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Woodpecker Agent",
            instructions="You have access to Woodpecker tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Woodpecker MCP in OpenAI Agents SDK

Just call `get_campaign_stats`. Your agent executes this against the MCP Server and returns the necessary metrics. Since it’s built on a production SDK, you'll get full tracing of that API call in your dashboard.

Yeah, you can list them using `list_webhooks`. This lets your agent confirm which integrations are active. The tool is straightforward and integrates right into the standard agent workflow.

The `add_prospect` tool makes it simple. Your agent sends the necessary details, and Woodpecker handles adding that lead instantly. It's designed for production use, so you don't worry about data corruption.

The MCP Server exposes eight specific tools, like `list_campaigns` and `get_campaign`. The agent only sees what you allow it to see. It's highly contained and safe for deployment.

The server touches prospect contact information, campaign status details, and webhook configuration data. All actions are logged via the secure endpoint token required by the OpenAI Agents SDK.

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