How to Use the Woodpecker MCP in CrewAI

Q: What data types are exposed through Woodpecker to CrewAI?

The server exposes prospect records (addprospect), campaign metrics (getcampaignstats), and webhook configuration data. These different data streams allow for complex decision-making.

Build autonomous teams: Woodpecker management with CrewAI.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Woodpecker MCP to CrewAI

Create your Vinkius account to connect Woodpecker to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Woodpecker with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Autonomous prospect generation and tracking

You can assign a specialized 'Prospect Manager' agent to handle contact list operations. This agent uses `list_prospects` to research contacts, and another agent can use `add_prospect` to onboard them. The shared memory ensures the new prospects are immediately available for the next step. This collaborative setup means you don't just run a single tool call; you build an autonomous operation that researches, verifies, and then adds leads in sequence.

Multi-agent campaign lifecycle management

Set up specialized agents for campaign oversight. One agent can use `list_campaigns` to review all sequences, while a 'Moderator' agent uses the data from `get_campaign_stats` to determine if action is needed. If stats are low, it triggers a 'Campaign Manager' agent to call `pause_campaign`. This hierarchical execution models real-world operations: monitoring identifies the problem, and the moderator executes the corrective tool call.

Comprehensive sequence audit trail

Need to know what marketing sequences are running? A specialized 'Auditor' agent can use `list_webhooks` to check all active tracking points. Another agent can then run through campaign statuses using `get_campaign` and `list_campaigns`. The results feed into a final summary document. The multi-agent approach ensures that every single tool call—from listing webhooks to checking status—is part of a defined, traceable operation.

Setup guide

Set up Woodpecker MCP in CrewAI

Prerequisites

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

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke Woodpecker tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="Woodpecker Analyst",
    goal="Access and analyze Woodpecker data via MCP.",
    backstory="Expert analyst with direct Woodpecker access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent Woodpecker transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="Woodpecker Analyst",
        goal="Access and analyze Woodpecker data via MCP.",
        backstory="Expert analyst with direct Woodpecker access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent Woodpecker transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Woodpecker. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Woodpecker now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Woodpecker MCP in CrewAI

You pass the MCP Server URL to your CrewAI setup. This allows you to define roles and tasks where specialized agents use Woodpecker tools, like `list_prospects`, to perform their jobs collaboratively.

Yes. You can create an agent workflow where one role checks campaign stats via `get_campaign_stats` and another role takes action, such as calling `resume_campaign`, based on the findings.

The server exposes prospect records (`add_prospect`), campaign metrics (`get_campaign_stats`), and webhook configuration data. These different data streams allow for complex decision-making.

The framework supports autonomous operations by running specialized agents in sequence or hierarchy. This means you can orchestrate tools like `list_prospects` and then immediately follow up with `add_prospect` without manual intervention.

The core data types are prospect information, campaign status (e.g., paused/resumed), and the list of active webhooks. Agents process this operational data.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript