How to Use the Baidu Qianfan MCP in Pydantic AI

Enforce strict type safety for Baidu Qianfan responses in your Pydantic AI agent logic.

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Connect Baidu Qianfan MCP to Pydantic AI

Create your Vinkius account to connect Baidu Qianfan 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 Baidu Qianfan with Pydantic AI

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Perplexity

Pydantic AI agents with Baidu Qianfan

Validate every `chat_completions` response against your Pydantic schemas. If Baidu returns malformed data, your agent catches it immediately. This stops bad data from propagating through your system. You get a clear error instead of a silent failure.

Validate Baidu data in Pydantic AI

Use `list_models` and `list_datasets` to populate your typed objects. Your agent relies on Pydantic to ensure the data structure matches your expectations. It's built for developers who need rigid contracts. You define the shape of the data, and the MCP server fills it.

Embeddings for Pydantic AI and Baidu

Generate vectors with `get_embeddings` and cast them to your defined Pydantic types. This keeps your vector operations consistent across your entire agent stack. Type safety is enforced at runtime. If the vector shape changes, your code knows instantly.

Setup guide

Set up Baidu Qianfan 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": {
        "baidu-qianfan-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Baidu Qianfan MCP in Pydantic AI

Every tool response is parsed into your Pydantic model. If the API output doesn't fit your schema, the agent raises a validation error immediately.

You can. Just update the model endpoint name in your tool call, and your Pydantic model will handle the incoming data structure.

It is. Configure your toolset to use the SSE or HTTP transport, and the server will stream responses into your agent process.

Run the server externally and point your Pydantic AI toolset to the URL. The integration handles the rest via your defined toolsets.

Your schema definitions live entirely in your local Python code. Baidu only receives the raw request payload; your internal logic and field definitions never leave your environment.

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