How to Use the Five9 QM MCP in LangChain

Build automated QA pipelines with Five9 QM and LangChain. Chain your evaluation logic directly into your agent's reasoning process.

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Connect Five9 QM MCP to LangChain

Create your Vinkius account to connect Five9 QM to LangChain 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 LangChain

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Automate interaction reviews in LangChain

Feed interaction data from `list_qm_interactions` directly into your LangChain agent. Your agent processes the raw metadata to decide which calls need human attention versus those ready for automated scoring. Trigger `create_evaluation` as the final step in your chain. By routing the agent's logic through this MCP Server, you ensure every evaluation follows your defined business rules.

Chain recording access for deep analysis

Use `get_recording_link` within your LangChain pipeline to pipe audio playback URLs into downstream analysis tools. You can now process long-form calls without manual transcript retrieval. This workflow forces your agent to pull the link and verify the file status before attempting any summary. It removes the guesswork from your quality management data pipeline.

Sync evaluation forms across your agents

Call `list_evaluation_forms` to pull your active scorecards into the agent's memory. Your agents will know exactly what criteria to check before they submit a result. Use `get_evaluation_form` to inspect the structure of these cards. This ensures your agent's reasoning aligns perfectly with your existing internal scorecard logic.

Setup guide

Set up Five9 QM MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Five9 QM tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "five9-qm-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Five9 QM transactions"
    })
    print(result["messages"][-1].content)

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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 LangChain

Install the necessary adapters and initialize the MultiServerMCPClient with your endpoint. Once configured, you pull the tools and pass them into your agent constructor to enable live API interaction.

Yes, by chaining multiple tools together, your agent can list interactions, retrieve recordings, and submit evaluations in a single pass. The agent manages the state between each call to ensure the sequence remains logical.

Absolutely. Every time your LangChain agent invokes an MCP tool, the input and output are captured in your LangSmith traces. You can debug the reasoning path behind every specific evaluation.

You can use the client session feature to maintain context across multiple tool calls. This is useful for long-running chains that need to track an agent's progress through a queue of interactions.

The server operates within a strict sandbox, passing only the data you request from the API to your local agent. No PII is stored or cached on the Vinkius infrastructure, and all traffic remains encrypted.

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