Zephyr Scale (SmartBear) MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zephyr Scale (SmartBear) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Zephyr Scale (SmartBear). "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Zephyr Scale (SmartBear)?"
)
print(response)
asyncio.run(main())
* 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 Zephyr Scale (SmartBear) MCP Server
Connect your Zephyr Scale (SmartBear) account to any AI agent and manage your enterprise quality assurance infrastructure through natural conversation.
LlamaIndex agents combine Zephyr Scale (SmartBear) tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Test Case Discovery — List and browse all test cases within a Jira project and retrieve specific keys (e.g., PROJ-T1) for deep inspection
- Cycle Monitoring — Browse test cycles to see how test runs are grouped for specific releases, sprints, or regression cycles
- Execution Tracking — Monitor real-time test execution results (Pass, Fail, Blocked) and retrieve step-by-step progress details
- Test Planning — List high-level test plans to understand your overall testing strategy and project scope
- Folder Navigation — Explore the organizational hierarchy of your test cases, cycles, and plans to find specific work items
- Environment Audit — List configured test environments (Staging, Production) and custom statuses available for your project
- Step-by-Step Insights — Retrieve full objective, preconditions, and detailed test scripts for any individual test case
The Zephyr Scale (SmartBear) MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 Zephyr Scale (SmartBear) to LlamaIndex via MCP
Follow these steps to integrate the Zephyr Scale (SmartBear) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Zephyr Scale (SmartBear)
Why Use LlamaIndex with the Zephyr Scale (SmartBear) MCP Server
LlamaIndex provides unique advantages when paired with Zephyr Scale (SmartBear) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zephyr Scale (SmartBear) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zephyr Scale (SmartBear) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zephyr Scale (SmartBear), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zephyr Scale (SmartBear) tools were called, what data was returned, and how it influenced the final answer
Zephyr Scale (SmartBear) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zephyr Scale (SmartBear) MCP Server delivers measurable value.
Hybrid search: combine Zephyr Scale (SmartBear) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zephyr Scale (SmartBear) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zephyr Scale (SmartBear) for fresh data
Analytical workflows: chain Zephyr Scale (SmartBear) queries with LlamaIndex's data connectors to build multi-source analytical reports
Zephyr Scale (SmartBear) MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Zephyr Scale (SmartBear) to LlamaIndex via MCP:
get_execution
Retrieves full details of a Zephyr Scale test execution
get_test_case
Retrieves full details of a Zephyr Scale test case
get_test_cycle
Retrieves full details of a Zephyr Scale test cycle
list_environments
g. Staging, Production). Lists all test environments in a Zephyr Scale project
list_executions
Lists all test executions in a Zephyr Scale project
list_folders
Folder type must be TEST_CASE, TEST_CYCLE, or TEST_PLAN. Lists all folders for a specific type within a project
list_statuses
Lists all custom test execution statuses in a project
list_test_cases
Provide a Jira project key (e.g. "PROJ"). Returns test case keys, names, and statuses. Paginated. Lists all test cases in a Zephyr Scale project
list_test_cycles
Test cycles group test runs for a release or sprint. Lists all test cycles in a Zephyr Scale project
list_test_plans
Lists all test plans in a Zephyr Scale project
Example Prompts for Zephyr Scale (SmartBear) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zephyr Scale (SmartBear) immediately.
"List all test cases in project 'PROJ'."
"What are the details for test cycle 'PROJ-R42'?"
"Show me the results for execution ID '12345678'."
Troubleshooting Zephyr Scale (SmartBear) MCP Server with LlamaIndex
Common issues when connecting Zephyr Scale (SmartBear) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZephyr Scale (SmartBear) + LlamaIndex FAQ
Common questions about integrating Zephyr Scale (SmartBear) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zephyr Scale (SmartBear) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zephyr Scale (SmartBear) to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.