How to Use the StarRocks MCP in Pydantic AI

Q: What metadata does Pydantic AI retrieve when listing databases for StarRocks?

The listdatabases tool returns a clean, validated list of database names. Since the output is typed, you never have to worry about malformed strings.

Q: If I use Pydantic AI to check storage usage on StarRocks, what metrics do I get?

The getstorageusage tool returns structured data points regarding resource consumption. Because of Pydantic validation, you know exactly which fields and units to expect.

Q: Can Pydantic AI list materialized views for StarRocks?

Yes. The listmvs tool provides a structured output detailing all the materialized views in your StarRocks cluster, making integration straightforward.

Build reliable StarRocks agents with guaranteed output schemas using Pydantic AI.

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Connect StarRocks MCP to Pydantic AI

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Setup StarRocks with Pydantic AI

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Get predictable data structures.

The agent uses `get_table_schema` to pull the exact structure of a table. This response is validated against a schema, guaranteeing you get field names and types. If the API gives back unexpected fields, Pydantic fails loud with an error—no silent corruption.

Validate cluster metadata.

Check the overall system status by calling `get_cluster_info`. This tool provides structured metrics about node counts and version information. The agent also uses `list_views` to retrieve a validated list of all defined database views.

Track resource usage precisely.

Use `get_storage_usage` to get precise storage statistics. The output format is guaranteed, allowing you to write reliable code for cost analysis. The agent can also track specific tables using `list_tables`, ensuring the names and scopes are always correctly typed.

Setup guide

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

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

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

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Common questions about StarRocks MCP in Pydantic AI

When you run a query, the agent forces the result set to conform to your defined Pydantic model. This means every field has an expected type and structure.

The `list_databases` tool returns a clean, validated list of database names. Since the output is typed, you never have to worry about malformed strings.

The `get_storage_usage` tool returns structured data points regarding resource consumption. Because of Pydantic validation, you know exactly which fields and units to expect.

Yes. The `list_mvs` tool provides a structured output detailing all the materialized views in your StarRocks cluster, making integration straightforward.

The server touches table schema information and resource metrics. Because of validation at runtime, you are assured that the agent only processes data matching the expected structure.

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