Five9 QM MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Five9 QM as an MCP tool provider through the 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 Five9 QM. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Five9 QM?"
)
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 Five9 QM MCP Server
Connect your Five9 Quality Management (QM) account to any AI agent and automate your quality assurance workflows through the Model Context Protocol (MCP). Five9 QM (formerly Virtual Observer) is a powerful tool for monitoring and scoring agent performance across voice and digital channels. Now, you can manage evaluations and review interactions directly through natural conversation.
LlamaIndex agents combine Five9 QM tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the 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
- Evaluation Management — List all completed evaluations, fetch detailed scores and feedback, and submit new evaluations instantly.
- Interaction Review — Access recorded voice and digital interactions available for quality review and retrieve their full metadata.
- Recording Retrieval — Generate temporary, secure links to audio recordings for direct playback and analysis.
- Form Inspection — List all active quality evaluation forms and fetch their specific question and scoring structures.
- Calibration Monitoring — Track active and completed calibration sessions to ensure scoring consistency across your QA team.
- Agent & User Discovery — List agents available for evaluation and manage QM system users for better team context.
- High-level Reporting — Fetch QM summary metrics to understand organization-wide quality trends and performance.
The Five9 QM MCP Server exposes 12 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 Five9 QM to LlamaIndex via MCP
Follow these steps to integrate the Five9 QM 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 12 tools from Five9 QM
Why Use LlamaIndex with the Five9 QM MCP Server
LlamaIndex provides unique advantages when paired with Five9 QM through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Five9 QM tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Five9 QM tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Five9 QM, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Five9 QM tools were called, what data was returned, and how it influenced the final answer
Five9 QM + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Five9 QM MCP Server delivers measurable value.
Hybrid search: combine Five9 QM real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Five9 QM 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 Five9 QM for fresh data
Analytical workflows: chain Five9 QM queries with LlamaIndex's data connectors to build multi-source analytical reports
Five9 QM MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Five9 QM to LlamaIndex via MCP:
create_evaluation
Submit a new evaluation
get_evaluation
Get evaluation details
get_evaluation_form
Get form structure
get_qm_interaction
Get interaction metadata
get_qm_summary
Get QM summary report
get_recording_link
Get audio recording link
list_calibrations
List calibration sessions
list_evaluation_forms
List evaluation forms
list_evaluations
List agent evaluations
list_qm_agents
List evaluatable agents
list_qm_interactions
List recorded interactions
list_qm_users
List QM system users
Example Prompts for Five9 QM in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Five9 QM immediately.
"List the last 5 completed agent evaluations."
"Get the recording link for interaction 'int_12345'."
"Show me all available evaluation forms."
Troubleshooting Five9 QM MCP Server with LlamaIndex
Common issues when connecting Five9 QM to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFive9 QM + LlamaIndex FAQ
Common questions about integrating Five9 QM 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 Five9 QM 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 Five9 QM to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.