How to Use the Woodpecker MCP in Pydantic AI

Q: How does Pydantic AI ensure accuracy when checking campaign stats with Woodpecker?

It validates every field returned by getcampaignstats against your defined model. If the data is messy or corrupt, the agent fails immediately and tells you exactly why. That's huge for production reliability.

Q: Can Pydantic AI manage campaign listings with Woodpecker?

Yes, listcampaigns works perfectly. The toolset guarantees that the list of campaigns will follow your specified data types, so you don't have to write extra validation code.

Q: When adding a prospect with Woodpecker using Pydantic AI, what happens?

The agent calls addprospect, and the MCP Server validates the input data structure against your model. This means you can trust that the data passed to Woodpecker is clean.

Q: What if I need to list webhooks with Woodpecker using Pydantic AI?

You use listwebhooks. The output will be strictly typed, meaning you'll receive a predictable and reliable data structure every single time. It’s all about preventing silent bugs.

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Connect Woodpecker MCP to Pydantic AI

Create your Vinkius account to connect Woodpecker to Pydantic AI 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 Pydantic AI

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Check Campaign Performance Metrics

The `get_campaign_stats` tool lets your agent pull key campaign data. The major benefit here is that every piece of returned data validates against a Pydantic model at runtime. You never get unexpected fields. When you call `list_campaigns`, the response structure is guaranteed. If Woodpecker changes its API format, your agent fails loudly with a validation error, keeping your system correct.

Add and Track Prospects

If your agent needs to onboard a new lead, it uses `add_prospect`. Because of Pydantic's type checking, the MCP Server guarantees that the data structure matches your expected schema. No hallucinated fields allowed. To review contacts, call `list_prospects`. The tool returns a list where every item adheres to your defined data model, meaning you can trust the output completely.

Control Campaign Status Changes

Use `pause_campaign` when outreach needs a break. This action is predictable because Pydantic validates the tool's parameters before execution. You know exactly what the agent is telling Woodpecker to do. Similarly, if you want to restart efforts, calling `resume_campaign` uses the same type safety. The focus here is correctness over speed.

Setup guide

Set up Woodpecker MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "woodpecker-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Woodpecker tools.",
)

result = await agent.run("List recent Woodpecker transactions")
print(result.output)

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Common questions about Woodpecker MCP in Pydantic AI

It validates every field returned by `get_campaign_stats` against your defined model. If the data is messy or corrupt, the agent fails immediately and tells you exactly why. That's huge for production reliability.

Yes, `list_campaigns` works perfectly. The toolset guarantees that the list of campaigns will follow your specified data types, so you don't have to write extra validation code.

The agent calls `add_prospect`, and the MCP Server validates the input data structure against your model. This means you can trust that the data passed to Woodpecker is clean.

You use `list_webhooks`. The output will be strictly typed, meaning you'll receive a predictable and reliable data structure every single time. It’s all about preventing silent bugs.

The server touches prospect details, campaign statuses, and webhook configurations. Pydantic AI ensures that when these values are passed to your agent, they strictly match the expected Python data models.

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