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

Q: Can I use LlamaIndex to find old Zephyr Scale (SmartBear) records?

Yes. By indexing the outputs of tools like listexecutions and gettestexecution, you build a queryable history. You're querying data, not just triggering a live endpoint.

Q: Does Zephyr Scale (SmartBear) support semantic search with LlamaIndex?

It does. The MCP Server provides the raw data—like all listtestplans results—and LlamaIndex handles the semantic layer, letting you ask complex questions about that data.

Turn Zephyr Scale (SmartBear) API results into a searchable knowledge base with LlamaIndex.

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Connect Zephyr Scale (SmartBear) MCP to LlamaIndex

Create your Vinkius account to connect Zephyr Scale (SmartBear) to LlamaIndex 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 LlamaIndex

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Knowledge Indexing using the MCP Server

Don't just run reports—make them queryable. Use LlamaIndex to index historical data from Zephyr Scale (SmartBear). For example, you can pass all results from `list_test_cases` into a vector store, allowing your agent to answer questions like, 'What was the status of this test case last quarter?' This turns live API calls into persistent knowledge. The output from tools like `get_test_case` becomes part of an index you can search against later, avoiding hallucinations entirely.

Retrieving Test Cycle Data with LlamaIndex

Need to find out what was planned for a specific release? Index the details pulled from `get_test_cycle`. You can then query your knowledge base to retrieve historical context, such as which test plans were associated with that cycle. LlamaIndex handles this by making API results searchable. Instead of just getting a JSON object, you get an answer grounded in all past data related to test cycles.

MCP Server: Mapping Test Environments for LlamaIndex

When onboarding new users or debugging, knowing the environment is key. Indexing results from `list_environments` means your AI client can answer questions like, 'Which environments do we use for staging?' without needing to run a live API call. This capability allows you to build RAG applications where documentation and live system data combine into one unified search index.

Setup guide

Set up Zephyr Scale (SmartBear) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Zephyr Scale (SmartBear) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Zephyr Scale (SmartBear) tools.",
)
response = await agent.run("List recent Zephyr Scale (SmartBear) data")

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Common questions about Zephyr Scale (SmartBear) MCP in LlamaIndex

LlamaIndex takes the structured output from the MCP Server and turns it into a searchable knowledge graph. You can query past test execution data or configurations, treating API results as indexed facts.

Yes. By indexing the outputs of tools like `list_executions` and `get_test_execution`, you build a queryable history. You're querying data, not just triggering a live endpoint.

It does. The MCP Server provides the raw data—like all `list_test_plans` results—and LlamaIndex handles the semantic layer, letting you ask complex questions about that data.

You index both document knowledge and API data. For instance, if you have a manual written guide on test plans, you can query it alongside the structured data from `list_test_plans`.

This server deals with test case keys and names. You'll find a wealth of information by indexing the results from `list_test_cases`.

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