How to Use the Woodpecker CI MCP in AutoGen

Q: How do I troubleshoot a build failure using Woodpecker CI and AutoGen?

You can assign multiple agents roles: one to check getpipeline for the error code, another to call listreposecrets for potential missing keys, and a third to suggest remediation. They debate until they pinpoint the root cause.

Q: Can AutoGen help plan repository restructuring?

Yes. An 'Architect Agent' can propose a move using deleterepo, which is then challenged by an 'Impact Agent' that first checks all associated pipelines via listpipelines to ensure nothing breaks.

Q: What if I need to check multiple agent permissions?

You can use the getorgpermissions tool. AutoGen lets agents cross-check these permissions against proposed actions, ensuring that no single agent makes an unauthorized change.

Q: Does Woodpecker CI and AutoGen support testing multiple environments?

The system supports managing distinct repositories (listrepos). You can set up agents to simulate environment promotion: Agent A runs tests on Repo Alpha; Agent B validates the results before triggering a deployment to Repo Beta.

Debate complex CI/CD decisions with Woodpecker CI using AutoGen.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Woodpecker CI MCP to AutoGen

Create your Vinkius account to connect Woodpecker CI to AutoGen 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 CI with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Consensus-Driven Pipeline Management via AutoGen

AutoGen allows multiple agents to debate the best action for a failing build. You can set up a 'Security Agent' and a 'Performance Agent.' The Security Agent might flag an outdated secret retrieved by `list_global_secrets`, while the Performance Agent insists on running a faster, but riskier, restart via `restart_pipeline`. The agents negotiate until they converge on a single, agreed-upon action that maximizes system safety and speed.

Simulating Repo Changes with AutoGen and MCP Server

Need to know if changing ownership is safe? You can set up an 'Owner Agent' and a 'Risk Agent.' The Owner Agent proposes using `chown_repo`, but the Risk Agent challenges it, requiring confirmation via `get_user` details first. The debate process forces the system to validate permissions and impacts across multiple tool calls before making any changes.

Complex Secret Management with AutoGen

Managing secrets requires more than just listing them. A 'Compliance Agent' can call `list_org_secrets` and then challenge the proposed use of a secret by demanding an explicit reference to its purpose in the pipeline configuration from `get_pipeline_config`. The negotiation ensures that no sensitive data is used without full, documented consensus among the collaborating AI agents.

Setup guide

Set up Woodpecker CI MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Woodpecker CI tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Woodpecker CI_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Woodpecker CI data")
print(result.messages[-1].content)

Get your connection token →

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

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

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Woodpecker CI_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Woodpecker CI data")
    print(result.messages[-1].content)

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 CI. 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 CI 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 CI MCP in AutoGen

You can assign multiple agents roles: one to check `get_pipeline` for the error code, another to call `list_repo_secrets` for potential missing keys, and a third to suggest remediation. They debate until they pinpoint the root cause.

Yes. An 'Architect Agent' can propose a move using `delete_repo`, which is then challenged by an 'Impact Agent' that first checks all associated pipelines via `list_pipelines` to ensure nothing breaks.

You can use the `get_org_permissions` tool. AutoGen lets agents cross-check these permissions against proposed actions, ensuring that no single agent makes an unauthorized change.

The system supports managing distinct repositories (`list_repos`). You can set up agents to simulate environment promotion: Agent A runs tests on Repo Alpha; Agent B validates the results before triggering a deployment to Repo Beta.

This MCP Server manages operational state, organizational boundaries, user permissions, repository settings, and pipeline execution history. The core concept is shared, debatable system context.

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