How to Use the Apache Superset MCP in Pydantic AI

Run type-safe SQL queries and inspect Apache Superset dashboards with strict runtime validation in Pydantic AI.

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Connect Apache Superset MCP to Pydantic AI

Create your Vinkius account to connect Apache Superset 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 Apache Superset with Pydantic AI

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Run type-validated SQL queries

Execute database queries safely using `execute_sql_query`. The returned data is validated against your Pydantic models at runtime, preventing malformed SQL results from breaking your application. If the database schema changes or returns unexpected null values, the framework catches it instantly. Your agent gets clean, structured data every single time.

Discover datasets with this type-safe MCP Server

Explore your BI environment without risking runtime crashes. The agent calls `list_databases` and `list_datasets` to map out your available data sources. Every database connection and dataset metadata object is parsed into strict Python types. This guarantees your agent always works with valid schemas before generating queries.

Extract validated dashboard structures in Pydantic AI

Pull live charts and dashboard layouts using `list_dashboards` and `get_chart_details`. The system validates the visual layout and metric definitions before passing them to the model. This ensures your agent never tries to read a missing chart or hallucinate dashboard components that do not exist in your Superset instance.

Setup guide

Set up Apache Superset 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": {
        "apache-superset-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Apache Superset MCP in Pydantic AI

Install the slim package with MCP support, then instantiate `MCPToolset` with your Vinkius HTTP URL. Pass this toolset into the `toolsets` parameter of your `Agent`.

The framework will fail loudly with a validation error. This prevents your agent from processing corrupt SQL payloads or passing hallucinated data to down-stream tasks.

Yes. Pydantic AI supports external MCP tools natively, meaning you can run your Superset operations using local models, OpenAI, Anthropic, or Gemini while maintaining strict type safety.

Yes. The integration supports both Streamable HTTP and SSE transports, allowing you to stream query execution results and dashboard details efficiently.

All database metadata and query payloads are processed in Vinkius's secure sandbox. The server only fetches data requested by the agent and does not store your query history or database credentials.

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