How to Use the OceanBase MCP in Pydantic AI

Q: How do I add the OceanBase MCP Server to my Pydantic AI agent?

Install the slim package and use MCPToolset to import the server. Pass the toolset to your agent, and it will handle the communication via HTTP or SSE.

Deploy type-safe OceanBase management in Pydantic AI with runtime validation for every database tool response.

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Connect OceanBase MCP to Pydantic AI

Create your Vinkius account to connect OceanBase 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 OceanBase with Pydantic AI

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Type-safe OceanBase cluster discovery

The server exposes `list_clusters` and `get_cluster_details` with strict Pydantic models. If the database returns malformed data, your agent stops immediately. This prevents silent corruption in your automation pipelines. You get a guarantee that the data your agent processes matches the schema you defined.

Validate tenant and resource data

Use `get_tenant_details` and `get_resource_stats` to pull metrics directly into your type-checked workflow. Every response is validated at runtime. This ensures your agent never acts on invalid stats. It's the standard for production code where correctness is the priority.

Organize projects with verified schemas

Manage your workspace and project lists using `list_projects` and `get_workspaces`. Pydantic AI keeps every interaction within your strict type definitions. This creates a clean handoff between the database and your application logic. You stop guessing what the output looks like and start working with known structures.

Setup guide

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

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

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

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Common questions about OceanBase MCP in Pydantic AI

It provides runtime validation for all database tool responses. You avoid the risk of hallucinated fields or unexpected types crashing your application.

Install the slim package and use `MCPToolset` to import the server. Pass the toolset to your agent, and it will handle the communication via HTTP or SSE.

Your Pydantic AI agent will raise a validation error immediately. This forces a loud failure rather than letting the agent proceed with bad information.

It does. Since it is model-agnostic, you can swap between OpenAI, Anthropic, or local models without changing your tool integration code.

The server operates in a secure, ephemeral sandbox. It only exposes the specific database metadata you authorize, ensuring sensitive connection strings remain isolated.

Use it with your favorite AI tools

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