How to Use the Cube.dev MCP in Pydantic AI

Q: How do I configure Cube.dev inside Pydantic AI?

Install pydantic-ai-slimmcp and initialize MCPToolset with your Vinkius HTTP endpoint. This MCP Server integrates directly by passing the toolset to your Agent's toolsets parameter to auto-discover all fifteen tools.

Q: Can Pydantic AI monitor Cube.dev cache status?

Yes, your agent can call getpreaggregationjobstatus to check active builds. If you need to refresh a cache, the agent triggers triggerpreaggregationjob and validates the response schema.

Q: How does Pydantic AI handle Cube.dev SQL generation over MCP?

The agent uses getsql to fetch the compiled SQL for any given query. Because Pydantic AI enforces strict types, you can guarantee the returned SQL query structure matches your internal logging models.

Q: What happens if a Cube.dev deployment goes offline during a Pydantic AI run?

Your agent runs checklive and checkready to verify status. If the server is offline, Pydantic AI catches the connection failure immediately and raises a clean exception instead of letting the agent hallucinate.

Query Cube.dev with absolute type safety using Pydantic AI to catch schema drift before it breaks your code.

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Connect Cube.dev MCP to Pydantic AI

Create your Vinkius account to connect Cube.dev 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 Cube.dev with Pydantic AI

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Type-safe data loading with Pydantic AI

The `load_query` tool ensures you stop worrying about silent database failures. When your agent calls this endpoint, this MCP Server returns structured JSON that Pydantic AI validates against your exact Python models at runtime. If the semantic layer returns an unexpected data type, the framework raises a validation error immediately. This prevents corrupted data from slipping into your production application unnoticed.

Strict schema validation for cubes and views

The `list_entities` and `get_meta` tools keep your local models perfectly in sync with your database. Your agent uses them to inspect your active cubes and views. The agent parses the output of `get_entity` to ensure every field matches your local Pydantic classes. If a database administrator changes a column type, your agent catches it before running a single query.

Secure token generation and deployment checks

The `list_deployments` and `list_environments` tools manage your environments without risking runtime type errors. Verify your target backend is configured correctly before pushing updates. To authenticate requests securely, the agent runs `generate_meta_token` using your CUBE_CLOUD_API_KEY. The resulting JWT is validated instantly, ensuring your secure metadata access never fails silently.

Setup guide

Set up Cube.dev 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": {
        "cubedev-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Cube.dev MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]` and initialize `MCPToolset` with your Vinkius HTTP endpoint. This MCP Server integrates directly by passing the toolset to your Agent's `toolsets` parameter to auto-discover all fifteen tools.

Yes, your agent can call `get_pre_aggregation_job_status` to check active builds. If you need to refresh a cache, the agent triggers `trigger_pre_aggregation_job` and validates the response schema.

The agent uses `get_sql` to fetch the compiled SQL for any given query. Because Pydantic AI enforces strict types, you can guarantee the returned SQL query structure matches your internal logging models.

Your agent runs `check_live` and `check_ready` to verify status. If the server is offline, Pydantic AI catches the connection failure immediately and raises a clean exception instead of letting the agent hallucinate.

All execution happens inside the zero-trust Vinkius sandbox, ensuring raw SQL from `execute_cube_sql` never leaks. Pydantic AI validates the inputs and outputs locally, so no unencrypted database credentials ever leave your secure VPC.

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