Five9 QM MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Five9 QM through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"five9-qm": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Five9 QM, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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.
LangChain's ecosystem of 500+ components combines seamlessly with Five9 QM through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain via MCP
Follow these steps to integrate the Five9 QM MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 12 tools from Five9 QM via MCP
Why Use LangChain with the Five9 QM MCP Server
LangChain provides unique advantages when paired with Five9 QM through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Five9 QM MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Five9 QM queries for multi-turn workflows
Five9 QM + LangChain Use Cases
Practical scenarios where LangChain combined with the Five9 QM MCP Server delivers measurable value.
RAG with live data: combine Five9 QM tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Five9 QM, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Five9 QM tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Five9 QM tool call, measure latency, and optimize your agent's performance
Five9 QM MCP Tools for LangChain (12)
These 12 tools become available when you connect Five9 QM to LangChain 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 LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Five9 QM to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFive9 QM + LangChain FAQ
Common questions about integrating Five9 QM MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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 LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.