MaestroQA MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add MaestroQA 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 MaestroQA. "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in MaestroQA?"
)
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 MaestroQA MCP Server
Connect your MaestroQA account to any AI agent to automate your customer service quality assurance and performance reporting. This MCP server enables your agent to list tickets, monitor QA scores, request detailed data exports, and sync external CSAT scores directly from natural language interfaces.
LlamaIndex agents combine MaestroQA tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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
- Score Monitoring — List support tickets and retrieve real-time Internal Quality Scores (IQS) and grading statuses
- Automated Exporting — Initialize asynchronous raw data exports for deep analysis of rubric answers and performance
- Agent Oversight — List all support agents and available evaluation rubrics to organize your QA process
- CSAT Synchronization — Push external customer satisfaction scores into MaestroQA to correlate them with internal QA grades
- Detailed Auditing — Retrieve complete metadata and scoring breakdowns for any individual ticket
The MaestroQA MCP Server exposes 7 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 MaestroQA to LlamaIndex via MCP
Follow these steps to integrate the MaestroQA 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 7 tools from MaestroQA
Why Use LlamaIndex with the MaestroQA MCP Server
LlamaIndex provides unique advantages when paired with MaestroQA through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine MaestroQA tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain MaestroQA tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query MaestroQA, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what MaestroQA tools were called, what data was returned, and how it influenced the final answer
MaestroQA + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the MaestroQA MCP Server delivers measurable value.
Hybrid search: combine MaestroQA real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query MaestroQA 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 MaestroQA for fresh data
Analytical workflows: chain MaestroQA queries with LlamaIndex's data connectors to build multi-source analytical reports
MaestroQA MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect MaestroQA to LlamaIndex via MCP:
get_export_download_links
Retrieve links for a requested export
get_ticket_qa_details
Get QA details for a specific ticket
list_qa_agents
List all agents tracked in MaestroQA
list_qa_rubrics
List all available evaluation rubrics
list_qa_tickets
Use optional params for filtering. List tickets and their QA statuses
push_csat_scores
Sync external CSAT scores into MaestroQA
request_qa_data_export
Requires start_date and end_date. Initialize a raw QA data export (Async)
Example Prompts for MaestroQA in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with MaestroQA immediately.
"List all support tickets awaiting QA review in MaestroQA."
"Request a raw data export for the month of July in MaestroQA."
"Show the QA score for ticket ID 'ticket-54321'."
Troubleshooting MaestroQA MCP Server with LlamaIndex
Common issues when connecting MaestroQA to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMaestroQA + LlamaIndex FAQ
Common questions about integrating MaestroQA 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 MaestroQA 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 MaestroQA to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.