PractiTest 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 PractiTest as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
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 PractiTest. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in PractiTest?"
)
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 PractiTest MCP Server
Connect your PractiTest workspaces to any AI agent and empower it to orchestrate the entire QA lifecycle from physical requirements tracing to defect mapping natively via chat conversations.
LlamaIndex agents combine PractiTest 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 Cases & Sets — Tell the AI to investigate any Test Case or Test Set, discovering exact preconditions and expected results (
list_tests,get_test,list_sets) - Test Instances & Runs — Retrieve deep execution histories pinpointing exactly which step caused a regression bounding PASSED/FAILED statuses (
list_runs) - Requirements Tracking — Audit physical system compliance extracting arrays dictating QA delivery thresholds (
list_requirements) - Issue Mapping — Find exact Software Defects bound natively to QA traces verifying complex failure logic (
list_issues)
The PractiTest 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 PractiTest to LlamaIndex via MCP
Follow these steps to integrate the PractiTest 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 PractiTest
Why Use LlamaIndex with the PractiTest MCP Server
LlamaIndex provides unique advantages when paired with PractiTest through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine PractiTest tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain PractiTest tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query PractiTest, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what PractiTest tools were called, what data was returned, and how it influenced the final answer
PractiTest + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the PractiTest MCP Server delivers measurable value.
Hybrid search: combine PractiTest real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query PractiTest 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 PractiTest for fresh data
Analytical workflows: chain PractiTest queries with LlamaIndex's data connectors to build multi-source analytical reports
PractiTest MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect PractiTest to LlamaIndex via MCP:
get_set
Get full details of a PractiTest test set including name, status, instances count, and execution summary
get_test
Get full details of a PractiTest test case including name, description, preconditions, steps, expected results, custom fields, and requirement links
list_custom_fields
List all custom fields in a PractiTest project. Returns field names, types, applicable entities, and possible values
list_instances
List all test instances in a PractiTest test set. Instances are test-set-specific copies of test cases. Returns instance IDs, test references, and last run statuses
list_issues
List all issues (defects) in a PractiTest project. Returns issue names, statuses, severities, and linked test references
list_requirements
List all requirements in a PractiTest project. Requirements provide traceability to test cases and defects. Returns names, statuses, and linked test counts
list_runs
List all runs for a PractiTest test instance. Runs record actual test execution results. Returns run IDs, statuses (PASSED/FAILED/BLOCKED/NOT_RUN/N_A), durations, and timestamps
list_sets
List all test sets in a PractiTest project. Test sets group test instances for execution. Returns set names, statuses, planned/actual dates, and assigned testers
list_tests
List all test cases in a PractiTest project. PractiTest is an end-to-end test management platform with traceability from requirements to defects. Returns test names, IDs, statuses, custom fields, and traceability links. Uses JSON:API format
list_users
List all users in the PractiTest account. Returns user names, emails, roles, and statuses
Example Prompts for PractiTest in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with PractiTest immediately.
"List all tests inside our active QA regression instance and find the ones mapped as failed."
"Do we have any new custom fields we should be aware of inside the requirements area?"
"Are there any open defects (issues) linked directly to testing scenarios surrounding multi-currency operations?"
Troubleshooting PractiTest MCP Server with LlamaIndex
Common issues when connecting PractiTest to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPractiTest + LlamaIndex FAQ
Common questions about integrating PractiTest 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 PractiTest 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 PractiTest to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.