How to Use the Five9 QM MCP in LlamaIndex

Q: Does LlamaIndex support filtering Five9 QM data?

You can use the allowedtools filter to control which endpoints your agent can query. This keeps your data access focused and prevents the agent from running unnecessary lookups.

Index your contact center data with Five9 QM and LlamaIndex. Query your quality history using natural language, backed by live API data.

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Connect Five9 QM MCP to LlamaIndex

Create your Vinkius account to connect Five9 QM 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 Five9 QM with LlamaIndex

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Build a searchable index of Five9 QM records

Turn your Five9 QM metadata into a queryable knowledge base by passing `list_qm_interactions` output into your LlamaIndex pipeline. Your agent can now search through thousands of calls based on semantic meaning rather than just keywords. This allows for rapid discovery of coaching opportunities. You tell the agent what to find, and it pulls the exact interaction records from the server.

Ground your answers in Five9 QM history

Use `list_evaluations` to feed past performance data into your index. Your agent will reference actual historical scores when answering questions about agent trends or team performance. This stops your agent from hallucinating metrics. It draws directly from the live API results, ensuring your reports are grounded in reality.

Query evaluation forms with LlamaIndex

Index your `list_evaluation_forms` data so your agent understands your internal standards. It can compare specific call outcomes against the exact requirements listed in your scorecards. By using `get_evaluation_form` to pull the latest structure, your index stays updated automatically. You never have to manually update the agent's knowledge regarding scorecard changes.

Setup guide

Set up Five9 QM 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 Five9 QM 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 Five9 QM tools.",
)
response = await agent.run("List recent Five9 QM 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 Five9 QM. 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 Five9 QM MCP in LlamaIndex

The server provides a tool spec that LlamaIndex converts into an indexable format. Once the tools are integrated, the agent can fetch live data and add it to its current knowledge graph.

Yes, by pulling your evaluation history into a vector store, you can ask the agent to summarize performance patterns. It fetches the data via the MCP Server and processes it through your index.

You can use the allowed_tools filter to control which endpoints your agent can query. This keeps your data access focused and prevents the agent from running unnecessary lookups.

Security is handled at the connection level, where your endpoint token authenticates every request. The data flows directly from the Five9 API to your local index without ever hitting third-party storage.

Interaction data is retrieved only when your agent performs a query. The information is transient and exists only within your local index memory for the duration of the task.

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