How to Use the CircleCI MCP in LangChain

Chain your CI/CD operations directly into LangChain agents for automated build monitoring and pipeline control.

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Connect CircleCI MCP to LangChain

Create your Vinkius account to connect CircleCI 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 CircleCI with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Automate pipeline triggers in LangChain

Feed the output of your logic chains directly into `trigger_cci_pipeline`. You can now build agents that kick off new builds based on specific repository events or custom conditions. This MCP Server removes the need for manual console work. Your LangChain agent handles the entire lifecycle without leaving your script.

Observe build status within LangChain

Pipe `list_cci_pipelines` and `get_workflow_details` into your reasoning loops. Your agent parses live status data to decide if a build requires attention. Stop guessing about failure points. The agent inspects the state of every job and reports back exactly what broke.

Context-aware job inspection

Use `get_job_details` alongside `list_workflow_jobs` to map out complex build failures. Your agent navigates the hierarchy of your CI/CD setup to pinpoint the exact failing task. It feeds these details back into your LangSmith traces. You get a clear view of how the agent navigated your infrastructure.

Setup guide

Set up CircleCI 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 CircleCI 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({
    "circleci-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 CircleCI transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CircleCI. 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.

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Common questions about CircleCI MCP in LangChain

You pass the tool definitions into your agent's tool set. Once registered, the agent calls the function whenever your logic dictates a build is necessary.

Yes, by chaining the listing and detail tools together. The agent fetches job logs and summaries to diagnose issues before you even open the dashboard.

Vinkius handles your authentication via a single endpoint token. The server only touches your pipeline metadata and build status, keeping your raw source code private.

Absolutely. You can aggregate this server with others using the MultiServerMCPClient to give your agent a broader view of your dev tools.

Each tool output becomes a link in your chain. The next step in your sequence receives the raw data, allowing for precise, programmatic decision-making.

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