How to Use the Winevybe MCP in Pydantic AI

Q: How does Pydantic AI validate Winevybe data?

The server ensures that every response—whether it's from searchwines or getusercellar—conforms to a strict, pre-defined Python schema. If the underlying API sends anything off-spec, your agent fails instantly.

Q: Can Winevybe reliably handle wine pairings with Pydantic AI?

Yes. The getpairings tool returns data that is validated against a model you define. This means the pairing recommendations are always structured correctly for your application logic.

Q: What specific type of wine knowledge does Winevybe offer with Pydantic AI?

It covers everything from appellation details (getregioninfo) to historical harvest data via getvintagescores. You get structured access to deep, technical viticultural information.

Q: What specific data type does Winevybe touch when using Pydantic AI?

This MCP Server deals with structured wine metadata, including bottle specifications, pairing recommendations, and user cellar manifest records.

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Connect Winevybe MCP to Pydantic AI

Create your Vinkius account to connect Winevybe 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 Winevybe with Pydantic AI

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Validate Cellar Inventory Transactions

Building a reliable agent means knowing the data is clean. When you call `add_wine_to_cellar`, Pydantic ensures the response structure is correct—no phantom fields or missing IDs. It fails loudly if the API sends bad data. Use `get_user_cellar` and trust that every record returned validates against your defined schema, preventing silent corruption.

Structured Wine Comparisons

Comparing bottles is straightforward with `compare_wines`. Because the response adheres to Pydantic models, you can write code that *expects* exactly what the API returns. No unexpected data structures. Need more context? Use `get_wine_detail` and get a validated object containing all the profound tasting notes and stats.

Validated Global Search & Info

When you run `search_wines`, Pydantic validates the results, ensuring every returned wine record has expected fields like vintage or region. This is critical for type-safe production code. Want to expand your knowledge base? Call `get_winery_info` and receive a validated profile of the vineyard and producer.

Setup guide

Set up Winevybe 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": {
        "winevybe-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Winevybe MCP in Pydantic AI

The server ensures that every response—whether it's from `search_wines` or `get_user_cellar`—conforms to a strict, pre-defined Python schema. If the underlying API sends anything off-spec, your agent fails instantly.

Yes. The `get_pairings` tool returns data that is validated against a model you define. This means the pairing recommendations are always structured correctly for your application logic.

It covers everything from appellation details (`get_region_info`) to historical harvest data via `get_vintage_scores`. You get structured access to deep, technical viticultural information.

The limitation is that it only exposes wine-related tools. It can't handle non-wine data like user billing or operational metrics, but it validates whatever it *does* return.

This MCP Server deals with **structured wine metadata**, including bottle specifications, pairing recommendations, and user cellar manifest records.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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