How to Use the ezyVet MCP in LlamaIndex

Index your ezyVet patient records into LlamaIndex to build a searchable knowledge base.

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Connect ezyVet MCP to LlamaIndex

Create your Vinkius account to connect ezyVet 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 ezyVet with LlamaIndex

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Vectorize ezyVet data for LlamaIndex

Convert your clinical records into a format your agent can actually search. By running `list_animals` and `list_consults`, you pull raw data that gets indexed into your vector store. This turns your static database into a living knowledge base. You can ask questions about patient history and get answers based on actual clinical entries.

Ground your agent in ezyVet reality

Stop the guesswork by forcing your agent to query the index. Instead of relying on internal training, it checks the latest info from your practice management system. When you use `get_invoice` or `get_contact`, the retrieved data becomes part of the context. This minimizes hallucinations and keeps your clinical summaries grounded in facts.

Unified querying across documents

Combine your existing documents with live API data. LlamaIndex allows you to treat your ezyVet appointments as just another source of truth. Your agent can query across both local files and live API endpoints simultaneously. This provides a complete picture of your practice's operations.

Setup guide

Set up ezyVet 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 ezyVet 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 ezyVet tools.",
)
response = await agent.run("List recent ezyVet data")

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Common questions about ezyVet MCP in LlamaIndex

Use the MCP tool spec to pull the data into your indexer. Once the tools are initialized, you can build your vector store directly from the retrieved records.

You can apply filters to the tool list. This limits the agent to only the specific data points you want it to access.

Your API token secures the connection to the server. The data retrieved is transient and exists only to populate your local index.

Yes, once indexed, historical appointment data becomes searchable. You can query for specific dates or patient names.

We enforce strict read-only access for all indexed content. Your patient files are never cached on our servers, ensuring your clinic retains full control over sensitive information.

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