How to Use the Alation MCP in Pydantic AI

Q: How does Pydantic AI ensure data from the Alation listcolumns tool is correct?

When your Pydantic AI agent calls listcolumns, the JSON response from the MCP server is immediately parsed into a Pydantic model. If the response doesn't exactly match the model's structure and types, your code gets a validation exception.

Q: Can I use Pydantic AI with Alation to find saved queries by a certain user?

Yes. The listsavedqueries tool returns a list of query objects. Your agent can then filter these results in Python, confident that each object has the expected fields because Pydantic AI has already validated the entire list.

Q: What happens if the Alation API changes and a field is deprecated?

Your Pydantic AI agent will raise a ValidationError the moment it gets data that doesn't fit its model. This is a feature, not a bug. It forces you to address the change instead of letting your agent operate on bad data.

Query your Alation catalog with total type-safety. Pydantic AI validates every response so your agent never works with corrupted or unexpected data.

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Setup Alation with Pydantic AI

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Get Validated Catalog Search Results

Let your agent search the catalog with `search_catalog` or list contents with `list_tables`. The key difference here is that Pydantic AI guarantees the response will match a predefined data model. Your agent gets structured objects, not just raw JSON. This means your agent fails loudly and immediately if the API returns something unexpected. If a field is missing or has the wrong type, you get a `ValidationError` right away. It's a simple way to prevent silent data corruption and makes debugging much faster.

Audit Metadata with Confidence

When your agent calls `get_object_metadata` or `list_columns`, it gets back a strongly-typed Pydantic object. You don't have to write defensive code checking if a key exists or if a value is null. The validation is handled for you. This completely changes how you work with the API data. If your code receives a `Table` object, you know for a fact that the `owner` and `description` fields are present and correctly typed. It eliminates a whole class of common bugs.

A Type-Safe Alation MCP Server

Your agent can trace data history with `get_lineage` or pull query data using `get_query_results`, and every response is validated on the fly. You get the benefits of a rich data catalog without the risks of an unpredictable API. It doesn't matter which LLM you use with Pydantic AI—OpenAI, Anthropic, Gemini, or a local model. This MCP Server provides a consistent, type-safe interface to your Alation catalog for any of them. Correctness is the constant, regardless of the model.

Setup guide

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

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

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

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Common questions about Alation MCP in Pydantic AI

When your Pydantic AI agent calls `list_columns`, the JSON response from the MCP server is immediately parsed into a Pydantic model. If the response doesn't exactly match the model's structure and types, your code gets a validation exception.

Yes. The `list_saved_queries` tool returns a list of query objects. Your agent can then filter these results in Python, confident that each object has the expected fields because Pydantic AI has already validated the entire list.

Your Pydantic AI agent will raise a `ValidationError` the moment it gets data that doesn't fit its model. This is a feature, not a bug. It forces you to address the change instead of letting your agent operate on bad data.

There is a slight overhead for the runtime validation, but it's usually negligible. You're trading a few milliseconds for a massive increase in reliability and fewer bugs from unexpected API responses. It's almost always worth it.

The server only requests and processes metadata from your Alation catalog, like table schemas and custom fields, plus any cached query results. No raw data from your connected databases is ever touched. Vinkius isolates every request in a sandboxed, ephemeral environment to protect your metadata.

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