Perfecto 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 Perfecto 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 Perfecto. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Perfecto?"
)
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 Perfecto MCP Server
Control quality testing automation instances seamlessly connecting LLM parameters directly bounding your Perfecto Cloud. Retrieve precise matrix tracking devices explicitly parsing metadata, lookup explicitly bounded execution logs driving advanced tracking boundaries securely seamlessly efficiently. Automate evaluation boundaries querying Smart Reporting natively natively analyzing testing matrices confidently bypassing manual legacy UI navigation.
LlamaIndex agents combine Perfecto 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
- Device State Discovery — Explore testing grids tracking device topologies fetching available Android/iOS instances tracking native limits gracefully
- Execution Diagnostics — Evaluate structural loops mapping testing histories checking bounds isolating failed loops parsing native status parameters perfectly
- Repository Traceability — Read explicit bounds searching exact artifacts tracking storage buckets safely seamlessly verifying native dependencies natively
- Smart Reporting Audits — Extract logical limits processing advanced JSON outputs mapping comprehensive test validations checking explicit step counts confidently
The Perfecto 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 Perfecto to LlamaIndex via MCP
Follow these steps to integrate the Perfecto 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 Perfecto
Why Use LlamaIndex with the Perfecto MCP Server
LlamaIndex provides unique advantages when paired with Perfecto through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Perfecto tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Perfecto tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Perfecto, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Perfecto tools were called, what data was returned, and how it influenced the final answer
Perfecto + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Perfecto MCP Server delivers measurable value.
Hybrid search: combine Perfecto real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Perfecto 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 Perfecto for fresh data
Analytical workflows: chain Perfecto queries with LlamaIndex's data connectors to build multi-source analytical reports
Perfecto MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Perfecto to LlamaIndex via MCP:
get_device_details
Get full details of a Perfecto device including model, OS, firmware, manufacturer, resolution, location, and current status
get_execution_details
Get status of a Perfecto execution by ID. Returns status, progress, device assignments, and timestamps
get_license_info
Get Perfecto license information. Returns license type, device limits, concurrent executions, and expiration
get_report_summary
Get Smart Reporting summary for a Perfecto execution. Returns test results, pass/fail counts, video/screenshot links, and detailed step data
list_artifacts
List artifacts in Perfecto repository at a given path. Includes apps, scripts, images, and data files
list_device_groups
List device groups on Perfecto. Groups organize devices by type/OS/team. Returns group names and member devices
list_devices
List all available devices on Perfecto Cloud. Perfecto (by Perforce) is an enterprise mobile and web testing cloud with real devices and browsers. Returns device IDs, models, OS versions, manufacturers, locations, and availability statuses
list_executions
List current/recent executions on Perfecto. Returns execution IDs, statuses, script names, devices used, and timestamps
list_reservations
List device reservations on Perfecto. Returns reservation IDs, devices, users, start/end times
list_users
List all users on the Perfecto cloud. Returns usernames, roles, emails, and access levels
Example Prompts for Perfecto in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Perfecto immediately.
"Check matrices explicitly parsing structural targets querying `list_devices` globally discovering bounded limits seamlessly tracking iPhones safely."
"Execute validation tracking executions fetching deeply the report natively checking explicit execution ID 'exe_909' bounds accurately gracefully carefully natively."
Troubleshooting Perfecto MCP Server with LlamaIndex
Common issues when connecting Perfecto to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPerfecto + LlamaIndex FAQ
Common questions about integrating Perfecto 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 Perfecto 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 Perfecto to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.