How to Use the Woodpecker CI MCP in LangChain

Q: How do I check the status of a build using Woodpecker CI and LangChain?

You can get details on any running job by calling getpipeline with the pipeline ID. If you need to see all recent builds for a repo, use listpipelines. This gives your LangChain agent the necessary context to report status accurately.

Q: Can Woodpecker CI and LangChain handle multiple organizations?

Yes. You can first call listorgs to see all available organizational containers. Then, you use tools like listagents or listrepos combined with the organization context to scope your searches down.

Q: What if I need to fix webhooks after using Woodpecker CI and LangChain?

The dedicated repairrepo tool fixes broken webhooks. Your agent should call this right after an update, especially if you've used the updaterepo tool, ensuring that external services still point to a working endpoint.

Q: Does Woodpecker CI and LangChain support monitoring agents?

Absolutely. You can list all active workers using listagents. To see what tasks an agent is currently running, call listagenttasks for a specific agent ID.

Build complex CI/CD workflows with Woodpecker CI using LangChain.

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Create your Vinkius account to connect Woodpecker CI to LangChain 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 LangChain

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Automating Pipeline Triggers via LangChain

You can manually start a build or restart a failed job by calling `trigger_pipeline` or `restart_pipeline`. This is key for building observable, multi-step chains. The agent determines the exact repository and pipeline ID needed to execute the action. If you need to audit why a pipeline failed, first use `get_pipeline_config` to pull the build settings. Then, passing those specific details into the next step of your chain lets the agent decide if it should attempt a fix or just report the problem.

Managing Credentials with LangChain and MCP Server

The `list_global_secrets` tool allows you to audit sensitive keys from an administrative perspective. An agent can check which secrets exist across the whole system, then use `get_repo_secret` for specific access checks. This capability lets your LangChain application manage credentials like a vault. You don't just get data; you prove that the required secret exists before attempting to run a process, making the workflow much safer.

Full Repository Lifecycle Control with LangChain

When a project moves or needs cleanup, your agent can handle the entire repo lifecycle. It starts by using `get_repo` to confirm details, then maybe calling `update_repo` if settings changed. If the project is deprecated, you don't just delete it. The chain first calls `activate_repo` (to prepare for shutdown) and finally uses `delete_repo`, ensuring all necessary cleanup steps happen in order.

Setup guide

Set up Woodpecker CI MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Woodpecker CI tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "woodpecker-ci-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Woodpecker CI transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Woodpecker CI MCP in LangChain

You can get details on any running job by calling `get_pipeline` with the pipeline ID. If you need to see all recent builds for a repo, use `list_pipelines`. This gives your LangChain agent the necessary context to report status accurately.

Yes. You can first call `list_orgs` to see all available organizational containers. Then, you use tools like `list_agents` or `list_repos` combined with the organization context to scope your searches down.

The dedicated `repair_repo` tool fixes broken webhooks. Your agent should call this right after an update, especially if you've used the `update_repo` tool, ensuring that external services still point to a working endpoint.

Absolutely. You can list all active workers using `list_agents`. To see what tasks an agent is currently running, call `list_agent_tasks` for a specific agent ID.

This server handles repository metadata, pipeline configurations (stored as JSON/YAML), user credentials (secrets), and live operational status metrics. The primary data type is structured configuration and state.

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