Can I trigger a build for a specific branch through the agent?

Yes! Use the `trigger_cci_pipeline` tool. You need to provide the project slug (e.g., `gh/org/repo`) and the branch name. The agent will instruct CircleCI to start the pipeline immediately.

How do I see why a specific job failed?

Use the `get_job_details` tool with the project slug and job number. Your agent will fetch the execution status and metadata, which often points to the cause of failure.

Where do I find my CircleCI API Token?

Log in to CircleCI, go to **User Settings -> Personal API Tokens**. You can generate and copy a new token from there.

CircleCI MCP Server for LangChain 8 tools — connect in under 2 minutes

Name: CircleCI
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect CircleCI through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "circleci": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using CircleCI, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About CircleCI MCP Server

Connect your CircleCI account to any AI agent and take full control of your CI/CD pipelines and software delivery through natural conversation. Streamline how you monitor and trigger automated builds.

LangChain's ecosystem of 500+ components combines seamlessly with CircleCI through native MCP adapters. Connect 8 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Pipeline Oversight — List and retrieve details for recent CI/CD pipelines across your organizations natively
Trigger Management — Manually trigger new pipeline runs for specific projects and branches flawlessly
Workflow Intelligence — Access detailed information for workflows and their constituent jobs securely
Job Auditing — Retrieve detailed metadata and execution status for specific jobs flawlessly
Context Logistics — List shared environment contexts used for securing sensitive project data flawlessly
Developer Insights — Retrieve your own user profile and organization membership information directly within your workspace

The CircleCI MCP Server exposes 8 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect CircleCI to LangChain via MCP

Follow these steps to integrate the CircleCI MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 8 tools from CircleCI via MCP

Why Use LangChain with the CircleCI MCP Server

LangChain provides unique advantages when paired with CircleCI through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine CircleCI MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across CircleCI queries for multi-turn workflows

CircleCI + LangChain Use Cases

Practical scenarios where LangChain combined with the CircleCI MCP Server delivers measurable value.

RAG with live data: combine CircleCI tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query CircleCI, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain CircleCI tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every CircleCI tool call, measure latency, and optimize your agent's performance

CircleCI MCP Tools for LangChain (8)

These 8 tools become available when you connect CircleCI to LangChain via MCP:

get_job_details

Get detailed information for a specific job

get_my_cci_profile

Retrieve information about the authenticated user

get_workflow_details

Get detailed information for a specific workflow

list_cci_contexts

List shared contexts for an organization

list_cci_pipelines

List recent CI/CD pipelines

list_pipeline_workflows

List all workflows within a specific pipeline

list_workflow_jobs

List all jobs within a specific workflow

trigger_cci_pipeline

Trigger a new pipeline for a project

Example Prompts for CircleCI in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with CircleCI immediately.

"List my last 5 pipelines in CircleCI."

"Trigger a new pipeline for project 'gh/acme/api' on the 'main' branch."

"Show me the status of all jobs in workflow ID 'wf-12345'."

Troubleshooting CircleCI MCP Server with LangChain

Common issues when connecting CircleCI to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

CircleCI + LangChain FAQ

Common questions about integrating CircleCI MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.