How to Use the Woodpecker CI MCP in OpenAI Agents SDK

Q: How do I check the current status of a build using Woodpecker CI and OpenAI Agents SDK?

You call listpipelines to see all active pipelines for a given repository. If you need specific details on one run, use getpipeline. The agent delivers this state information directly.

Q: What is the best way to delete an old pipeline using Woodpecker CI and OpenAI Agents SDK?

Just call deletepipeline with the pipeline ID. It’s a clean, direct command that removes the job from the system. This ensures your agent doesn't waste time trying to track dead jobs.

Q: Can I list all repositories managed by Woodpecker CI and OpenAI Agents SDK?

Yep. The tool listrepos pulls a comprehensive list of every repository on the server. You can then pass those names to other tools, like getrepo, for further investigation.

Q: Does Woodpecker CI and OpenAI Agents SDK handle credential management?

Yes. It manages both global secrets (listglobalsecrets) and repository-level secrets (listreposecrets). The agent keeps these actions secure, ensuring only authorized processes can view or modify credentials.

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

This server manages sensitive repository settings and system-level secrets. When your agent interacts with it, be mindful that both the current user's identity (getuser) and stored secret values are exposed.

Manage CI/CD pipelines and resources with confidence using OpenAI Agents SDK.

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Connect Woodpecker CI MCP to OpenAI Agents SDK

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

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Control the full pipeline lifecycle.

You can trigger manual runs or restart failed jobs instantly. Use `trigger_pipeline` to kick off a test run, or call `restart_pipeline` when things get stuck. This gives your agent total control over the CI/CD process.

Audit and manage all system resources via MCP Server.

Need to know what repos exist? Run `list_repos`. Want to check a specific setup? Use `get_repo` for details. The agent handles the API calls, giving your production system clear visibility into every repository.

Administer agents and sensitive data securely.

The server lets you manage credentials directly. Your agent can create a new resource using `create_agent`, or list existing ones with `list_agents`. You don't have to guess who has access; use `get_org_permissions`.

Setup guide

Set up Woodpecker CI 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 CI tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Woodpecker CI 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 CI 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 CI Agent",
            instructions="You have access to Woodpecker CI 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 CI MCP in OpenAI Agents SDK

You call `list_pipelines` to see all active pipelines for a given repository. If you need specific details on one run, use `get_pipeline`. The agent delivers this state information directly.

Just call `delete_pipeline` with the pipeline ID. It’s a clean, direct command that removes the job from the system. This ensures your agent doesn't waste time trying to track dead jobs.

Yep. The tool `list_repos` pulls a comprehensive list of every repository on the server. You can then pass those names to other tools, like `get_repo`, for further investigation.

Yes. It manages both global secrets (`list_global_secrets`) and repository-level secrets (`list_repo_secrets`). The agent keeps these actions secure, ensuring only authorized processes can view or modify credentials.

This server manages sensitive `repository settings` and system-level secrets. When your agent interacts with it, be mindful that both the current user's identity (`get_user`) and stored secret values are exposed.

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