How to Use the Zephyr Scale (SmartBear) MCP in LangChain

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Connect Zephyr Scale (SmartBear) MCP to LangChain

Create your Vinkius account to connect Zephyr Scale (SmartBear) 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 Zephyr Scale (SmartBear) with LangChain

Ask AI about this MCP

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Claude

Perplexity

Run complex workflows using the MCP Server

Need to know if a specific feature is ready? Start by listing all available environments with `list_environments`. Then, use that environment name to retrieve full details of a test cycle via `get_test_cycle`, giving you the context needed for later steps. This capability lets your agent follow a clear sequence. It doesn't just check one thing; it uses the output from an initial call—like getting all available test plans with `list_test_plans`—as the direct input for checking individual test case details using `get_test_case`.

Agent-Driven Test Case Discovery

Your agent can discover exactly which tests exist in a project. First, call `list_test_cases` providing the Jira key to get all keys and names. Next, it should check the status of those cases by calling `get_test_case` for each one. This sequence lets you build multi-step reasoning. The agent figures out which tests need checking based on project context, then uses individual calls to gather specific data points.

MCP Server: Gathering Execution Status

When testing complex releases, you'll want to know the full picture. Start by listing all available test executions using `list_executions`. Then, you can narrow down the scope and check specific results for a given test cycle via `get_test_cycle`. This setup lets your chain observe process flow. It gathers a list of runs first, and then drills into the specifics to get full execution details with `get_execution`.

Setup guide

Set up Zephyr Scale (SmartBear) 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 Zephyr Scale (SmartBear) 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({
    "zephyr-scale-smartbear-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 Zephyr Scale (SmartBear) 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 Zephyr Scale (SmartBear). 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 Zephyr Scale (SmartBear) MCP in LangChain

LangChain treats the MCP Server like any other API. It lets you build multi-step chains where the output of one tool call dictates what the next agent action is. You're running structured test logic, not just simple lookups.

You can gather a ton of project configuration data. This includes listing all environments (`list_environments`) or getting the full details for any test case using `get_test_case`. Everything is structured API output.

Absolutely. You can write chains that first list all available folders (`list_folders`), then take one of those folder types and retrieve specific test plans or cycles. It’s built for sequential logic.

It gives you direct access to execution data. You can list all executions and then use `get_execution` to pull the full, detailed report for any given run.

This server primarily manages test execution statuses. You can list all custom test execution statuses using `list_statuses`, which is critical for monitoring project health.

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