How to Use the Woodpecker CI MCP in Pydantic AI

Q: How do I check a repository’s current configuration using Woodpecker CI and Pydantic AI?

The getpipelineconfig tool provides the exact files used for any pipeline. You can pair this with listpipelines to narrow down which config set you need.

Q: Can I see all active agents connected to Woodpecker CI and Pydantic AI?

Yes. Use listagents for a comprehensive overview of every agent running on the server. You can then target specific workers using tools like getagent.

Q: How does Woodpecker CI and Pydantic AI handle state changes?

If you need to change repo settings, call updaterepo. If the data returned isn't exactly what your schema expects—say it's missing a required field—the agent fails loudly with a type error.

Q: What kind of data does this MCP Server touch regarding privacy?

This server interacts heavily with repository settings and various forms of secrets. The Pydantic validation ensures that when you read or write these sensitive attributes, the structure remains correct.

Ensure data correctness when managing CI/CD with Pydantic AI for guaranteed validation.

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Connect Woodpecker CI MCP to Pydantic AI

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

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Claude

Perplexity

Handle credentials and permissions with strict types.

Working with secrets requires precision. Use `list_repo_secrets` to fetch credential names, or `get_org_permissions` to validate access rights. Since every response is typed, you never have to worry about unexpected keys.

Get a clear view of resource states.

Need the latest repo details? Call `get_repo`. Want to see what configuration files were used for a build? Use `get_pipeline_config`. Pydantic validation means you get clean, predictable data every time.

Control agent and system deployment.

The framework lets you manage the CI infrastructure itself. You can create new specialized workers with `create_agent`, or delete old ones using `delete_agent`. This control is critical for reliable, type-safe operations.

Setup guide

Set up Woodpecker CI 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-ci-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Woodpecker CI MCP in Pydantic AI

The `get_pipeline_config` tool provides the exact files used for any pipeline. You can pair this with `list_pipelines` to narrow down which config set you need.

The system separates global secrets from repository-level ones. Your agent can list both, ensuring that credential access follows strict, validated protocols.

Yes. Use `list_agents` for a comprehensive overview of every agent running on the server. You can then target specific workers using tools like `get_agent`.

If you need to change repo settings, call `update_repo`. If the data returned isn't exactly what your schema expects—say it's missing a required field—the agent fails loudly with a type error.

This server interacts heavily with `repository settings` and various forms of secrets. The Pydantic validation ensures that when you read or write these sensitive attributes, the structure remains correct.

Use it with your favorite AI tools

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