How do I get my Woodpecker API key?

Log in to your Woodpecker account at **app.woodpecker.co**. Navigate to **Settings** (gear icon). Click on **API keys** in the left menu. Click **Create new key**, give it a descriptive name, and copy the generated key. Paste it below. API access is available on all Woodpecker plans.

Can I automate multi-step follow-up sequences?

Yes. Woodpecker specializes in automated follow-up sequences. Your AI agent can create campaigns with up to 7 follow-up steps, each with customizable delays and personalization variables.

Does it protect my sender reputation?

Yes. Woodpecker includes built-in bounce detection, auto-blacklisting, and sending throttling. Your AI agent can monitor these safety metrics and alert you if deliverability drops below healthy thresholds.

Can I use it for agency clients?

Yes. Woodpecker offers an Agency plan with multi-client management. Each API key is scoped to your account, and you can manage campaigns for all clients from your AI agent.

Woodpecker MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Woodpecker
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Woodpecker through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Woodpecker "
            "(8 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Woodpecker?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Woodpecker MCP Server

Connect Woodpecker to your AI agent and manage your B2B cold email automation platform conversationally.

Pydantic AI validates every Woodpecker tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Campaign Management — Create, run, pause, and stop email campaigns with multi-step follow-up sequences.
Prospect Tracking — Add prospects, check reply statuses, and manage bounces and opt-outs.
Analytics — Pull open rates, click rates, reply rates, and bounce metrics per campaign.
Deliverability Monitoring — Track sending limits, warm-up progress, and inbox placement.

The Woodpecker MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Woodpecker to Pydantic AI via MCP

Follow these steps to integrate the Woodpecker MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from Woodpecker with type-safe schemas

Why Use Pydantic AI with the Woodpecker MCP Server

Pydantic AI provides unique advantages when paired with Woodpecker through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Woodpecker integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Woodpecker connection logic from agent behavior for testable, maintainable code

Woodpecker + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Woodpecker MCP Server delivers measurable value.

Type-safe data pipelines: query Woodpecker with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Woodpecker tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Woodpecker and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Woodpecker responses and write comprehensive agent tests

Woodpecker MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Woodpecker to Pydantic AI via MCP:

add_prospect

Add prospect

get_campaign

Get campaign

get_campaign_stats

Get campaign stats

list_campaigns

List campaigns

list_prospects

List prospects

list_webhooks

List webhooks

pause_campaign

Pause campaign

resume_campaign

Resume campaign

Example Prompts for Woodpecker in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Woodpecker immediately.

"Show campaign stats for 'VP Engineering Outreach'."

"Add 20 new prospects to my active campaign."

"Who replied to my campaigns this week?"

Troubleshooting Woodpecker MCP Server with Pydantic AI

Common issues when connecting Woodpecker to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Woodpecker + Pydantic AI FAQ

Common questions about integrating Woodpecker MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Woodpecker MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.