How to Use the ezyVet MCP in LangChain

Q: What happens to my ezyVet data privacy when using LangChain?

All clinical records accessed via getanimal or getconsult are handled in memory. We do not persist your patient data or logs once the session terminates.

Chain your clinical data directly into LangChain workflows for automated patient tracking and billing.

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Connect ezyVet MCP to LangChain

Create your Vinkius account to connect ezyVet 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 ezyVet with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Sequence ezyVet data calls in LangChain

Build multi-step pipelines where the output of one tool triggers the next. Your agent pulls an ID using `list_appointments` and immediately passes it to `get_appointment` to fetch specific details without manual intervention. This setup allows you to create complex reasoning chains. You define the logic, and your agent executes the sequence to gather patient history or financial records.

Full visibility into MCP server calls

Monitor every interaction between your agent and the veterinary database. Since these tools integrate into your standard chain, you see exactly what data flows in and out of the ezyVet API. Tracing helps you debug why an agent chose a specific path. You can verify the inputs sent to `get_consult` and ensure the retrieved information is accurate before it moves to the next step.

Contextual state management

Maintain persistent records across your LangChain session. By using the client session, your agent remembers previous lookups from `list_animals` while it works on new tasks. Statelessness is the default, but you control the flow. You decide when the agent needs a fresh start and when it should retain context from earlier tool calls.

Setup guide

Set up ezyVet 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 ezyVet 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({
    "ezyvet-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 ezyVet 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 ezyVet. 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 ezyVet MCP in LangChain

Install the necessary adapters and initialize the server connection. You fetch the tools directly and inject them into your agent definition.

Yes, you can define an agent that selects from the full suite of tools. It evaluates the current state and picks the right tool based on your prompt.

The server uses endpoint tokens for authentication. Your data remains isolated within the V8 sandbox during the entire lifecycle of your agent run.

The tools are designed to work within asynchronous pipelines. You can await the results of any `get` or `list` operation to keep your chain moving.

All clinical records accessed via `get_animal` or `get_consult` are handled in memory. We do not persist your patient data or logs once the session terminates.

Use it with your favorite AI tools

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