How to Use the MaestroQA MCP in LlamaIndex

Q: Can LlamaIndex filter the MaestroQA tools it exposes?

You can use the allowedtools filter when initializing the spec. If you only want your RAG app to read data, just expose listqaagents and listqarubrics.

Q: How do I query MaestroQA agent performance in LlamaIndex?

Your agent uses listqaagents to map the team, then pulls specific records with getticketqadetails. It indexes those records so you can ask natural language questions about performance trends.

Q: Does the LlamaIndex MaestroQA integration support data exports?

Yes. You can configure your agent to call requestqadataexport and then retrieve the files via getexportdownloadlinks. You will need to write a custom loader to parse the resulting CSVs into your index.

Index support evaluations and build queryable RAG applications with LlamaIndex and MaestroQA.

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Connect MaestroQA MCP to LlamaIndex

Create your Vinkius account to connect MaestroQA 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 MaestroQA with LlamaIndex

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Semantic Search for QA Rubrics in LlamaIndex

Support guidelines change constantly. By connecting LlamaIndex to this MCP Server, your application can pull every current grading standard using `list_qa_rubrics` and index them into a vector store. When a manager asks how refunds are graded, the system doesn't guess. It queries the actual index built from live MaestroQA API data, delivering answers grounded in your exact criteria.

Knowledge-Augmented Ticket Reviews

Standard LLMs hallucinate past performance reviews. Your LlamaIndex application fixes this by calling `list_qa_tickets` and `get_ticket_qa_details` to build a localized knowledge base of real agent interactions. You can query past sessions to find patterns in how specific agents handle escalations. The tool output becomes raw material for your semantic search, linking QA scores directly to customer outcomes.

Syncing CSAT Context to the Index

You need customer sentiment data sitting right next to your quality assurance metrics. Your RAG system can push external feedback into MaestroQA using `push_csat_scores`. After syncing, you can trigger `request_qa_data_export` to pull the combined dataset back out. Pass the results from `get_export_download_links` straight into your document loaders to enrich your vector store.

Setup guide

Set up MaestroQA 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 MaestroQA 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 MaestroQA tools.",
)
response = await agent.run("List recent MaestroQA data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by MaestroQA. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about MaestroQA MCP in LlamaIndex

Install llama-index-tools-mcp. Create a BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and call await mcp_tool_spec.to_tool_list_async() to feed your FunctionAgent.

You can use the allowed_tools filter when initializing the spec. If you only want your RAG app to read data, just expose list_qa_agents and list_qa_rubrics.

Your agent uses list_qa_agents to map the team, then pulls specific records with get_ticket_qa_details. It indexes those records so you can ask natural language questions about performance trends.

Yes. You can configure your agent to call request_qa_data_export and then retrieve the files via get_export_download_links. You will need to write a custom loader to parse the resulting CSVs into your index.

The MCP Server operates inside a zero-trust, ephemeral environment. When you pull internal grading rubrics and customer satisfaction scores, the transport layer is fully encrypted and Vinkius caches nothing.

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