How to Use the eCellar MCP in Pydantic AI

Q: How do I initialize the eCellar MCP Server in Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset with your HTTP endpoint and pass it into your Agent's toolsets array.

Connect eCellar to Pydantic AI via this MCP Server to enforce strict type safety on all winery operations and customer data lookups.

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Connect eCellar MCP to Pydantic AI

Create your Vinkius account to connect eCellar to Pydantic AI 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 eCellar with Pydantic AI

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Perplexity

Type-safe CRM lookups

The `search_customers` and `get_customer` tools return complex buyer profiles, and Pydantic AI validates every single field at runtime. If the API returns a missing lifetime spend value, your agent fails loudly instead of silently corrupting your database. You get absolute certainty that your customer data matches your expected schema. Managing recurring revenue requires the `list_clubs` and `list_club_members` endpoints. You initialize the connection using `MCPToolset` and pass it to your agent. The framework ensures that every club tier and member ID strictly conforms to your Python models.

Audit fulfillment with Pydantic AI

Your agent pulls line items and payment statuses using the `list_orders` and `get_order` tools. It cross-checks these transactions against the `list_inventory` endpoint to verify multi-location stock levels. Because Pydantic enforces the schema, your agent never hallucinates a missing bottle or invents a fake order number. This setup prioritizes correctness over raw speed. You install the slim package with MCP support, and the agent handles the rest. It works flawlessly with any underlying LLM, whether you prefer Anthropic for reasoning or a local model for privacy.

Sync daily tasting room operations

The `list_reservations` tool filters your daily bookings by date. Your agent reads this schedule and queries `list_products` and `get_product` to confirm the exact wine catalog available for pourings. Every reservation object and product SKU gets validated before the agent takes action. The MCP server runs externally, keeping your application logic clean. You rely on standard Streamable HTTP or SSE transports. Your tasting room staff gets accurate daily plans without dealing with broken JSON payloads.

Setup guide

Set up eCellar MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "ecellar-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to eCellar tools.",
)

result = await agent.run("List recent eCellar transactions")
print(result.output)

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Common questions about eCellar MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Create an `MCPToolset` with your HTTP endpoint and pass it into your Agent's `toolsets` array.

Pydantic forces strict runtime validation on every response. If an order record is missing a required field, the system throws an error immediately.

Yes. You can configure the toolset to use either Server-Sent Events or Streamable HTTP depending on your infrastructure requirements.

The framework is completely model-agnostic. You can query the server using OpenAI, Gemini, or a local model running on your own hardware.

The server processes guest names and reservation times through an isolated sandbox. We handle the authentication layer completely, ensuring that your scheduled bookings never persist in memory after the execution finishes.

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