How to Use the Cypress Cloud MCP in LangChain

Q: How do I pass Cypress Cloud run data into a LangChain agent?

You initialize the MCP Server using MultiServerMCPClient and pass the tools directly to your agent executor. The agent then calls getruns or getinstance based on the user's prompt, feeding the output directly into the next chain step.

Q: Can I trace Cypress Cloud tool calls using LangSmith with this LangChain setup?

Yes, every tool call like gettests or reportflaky passes through the LangChain adapter. This means LangSmith logs the exact inputs, outputs, and latency of your Cypress Cloud API interactions automatically.

Q: How does LangChain handle a failed Cypress Cloud run inspection?

Your LangChain agent uses a ReAct loop to first call getruns to find the failure. If it detects a failed status, it automatically calls getinstance and gettests to pinpoint the exact error message and screenshot URL.

Q: How do I set up the Cypress Cloud MCP Server in my Python project?

Install langchain-mcp-adapters and langgraph via pip. Connect to the Vinkius endpoint using the MultiServerMCPClient, call gettools(), and register them with your agent.

Run multi-step Cypress Cloud test analysis directly inside your LangChain reasoning loops.

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Setup Cypress Cloud with LangChain

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Trace Cypress Cloud Test Failures with LangChain Agents

The `get_runs` tool pulls your latest test suites directly into your LangChain workflow to isolate failing builds. Your agent starts by fetching the run list, identifies the failed runs, and automatically feeds those run IDs into `get_instance` to pull error logs and screenshots. This multi-step chain uses LangSmith to trace the exact latency and token cost of every Cypress Cloud metadata fetch. You get a clear history of how your agent parsed the spec failures without writing custom API fetching logic.

Isolate Flaky Tests Automatically

The `report_flaky` tool exposes intermittent test failures so your LangChain agent can cross-reference them with recent git commits. The agent calls this tool, finds tests with high flake rates, and immediately branches to `get_tests` to inspect individual test states. By linking these tools in a LangGraph state machine, you build an automated QA triage loop that flags flaky selectors before they merge. Every run-level detail feeds back into the chain's memory for context-aware debugging.

Generate BI Test Reports via MCP Server Tools

The `report_runs` tool aggregates test run metrics starting from a specific date for your LangChain data pipelines. Your agent grabs this enterprise data, passes it to your reporting chain, and combines it with `report_slow` to pinpoint performance bottlenecks. This setup replaces manual dashboard exports by letting your LangChain agent pull raw Cypress Cloud JSON directly into your internal databases. You configure the MCP Server once, and your chain handles the date-based pagination and data formatting.

Setup guide

Set up Cypress Cloud 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 Cypress Cloud 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({
    "cypress-cloud-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 Cypress Cloud transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Cypress Cloud MCP in LangChain

You initialize the MCP Server using `MultiServerMCPClient` and pass the tools directly to your agent executor. The agent then calls `get_runs` or `get_instance` based on the user's prompt, feeding the output directly into the next chain step.

Yes, every tool call like `get_tests` or `report_flaky` passes through the LangChain adapter. This means LangSmith logs the exact inputs, outputs, and latency of your Cypress Cloud API interactions automatically.

Your LangChain agent uses a ReAct loop to first call `get_runs` to find the failure. If it detects a failed status, it automatically calls `get_instance` and `get_tests` to pinpoint the exact error message and screenshot URL.

Install `langchain-mcp-adapters` and `langgraph` via pip. Connect to the Vinkius endpoint using the `MultiServerMCPClient`, call `get_tools()`, and register them with your agent.

Your test results, error messages, and screenshot URLs never touch third-party storage. Vinkius runs the Cypress Cloud MCP Server in an ephemeral, zero-trust V8 isolate sandbox that only proxies API requests directly to Cypress.

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