Which version of Ernie Bot should I use for chat?

For high performance and reasoning, use `ernie-4.0-8k`. For faster response times and cost efficiency, `ernie-speed-128k` or `ernie-lite-8k` are excellent choices.

Can I automatically generate embeddings for RAG?

Yes! Use the `get_embeddings` tool with your text input. The agent will retrieve the vector representations from Baidu's embedding models, ready for indexing in your vector database.

How do I use prompt templates from the console?

Use the `list_prompt_templates` tool to find your configured templates. You can then retrieve specific details using `get_prompt_template` to maintain consistency across your AI workflows.

Baidu Qianfan MCP Server for Pydantic AI 6 tools — connect in under 2 minutes

Name: Baidu Qianfan
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 6 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Baidu Qianfan through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Baidu Qianfan "
            "(6 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Baidu Qianfan?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Baidu Qianfan MCP Server

Connect your AI agents to Baidu Qianfan (百度千帆), the enterprise-grade LLM platform. This MCP provides 10 tools to automate interactions with Ernie Bot and other foundation models, including chat completions, vector embeddings, and prompt engineering.

Pydantic AI validates every Baidu Qianfan tool response against typed schemas, catching data inconsistencies at build time. Connect 6 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Model Interaction — Trigger chat completions with Ernie Bot (Turbo/Speed/4.0) using persistent context
Vector Embeddings — Generate semantic embeddings for text to power RAG and search workflows
Prompt Engineering — Manage and retrieve centralized prompt templates for consistent model outputs
Image Generation — Trigger Text-to-Image tasks using Baidu's advanced diffusion models
Usage Monitoring — Track token consumption and manage model service status programmatically

The Baidu Qianfan MCP Server exposes 6 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Baidu Qianfan to Pydantic AI via MCP

Follow these steps to integrate the Baidu Qianfan MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 6 tools from Baidu Qianfan with type-safe schemas

Why Use Pydantic AI with the Baidu Qianfan MCP Server

Pydantic AI provides unique advantages when paired with Baidu Qianfan through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Baidu Qianfan integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Baidu Qianfan connection logic from agent behavior for testable, maintainable code

Baidu Qianfan + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Baidu Qianfan MCP Server delivers measurable value.

Type-safe data pipelines: query Baidu Qianfan with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Baidu Qianfan tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Baidu Qianfan and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Baidu Qianfan responses and write comprehensive agent tests

Baidu Qianfan MCP Tools for Pydantic AI (6)

These 6 tools become available when you connect Baidu Qianfan to Pydantic AI via MCP:

chat_completions

Requires model endpoint name. Send a message to a Baidu Qianfan model

get_embeddings

Generate vector embeddings for text

list_datasets

List uploaded datasets

list_models

List available model services

list_train_jobs

List model training jobs

text_to_image

Generate an image from a text prompt

Example Prompts for Baidu Qianfan in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Baidu Qianfan immediately.

"Chat with Ernie Bot 4.0 and ask 'Write a formal apology letter for a late shipment'."

"Generate embeddings for the text 'The quick brown fox jumps over the lazy dog'."

"List all my prompt templates in Qianfan."

Troubleshooting Baidu Qianfan MCP Server with Pydantic AI

Common issues when connecting Baidu Qianfan to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Baidu Qianfan + Pydantic AI FAQ

Common questions about integrating Baidu Qianfan MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Baidu Qianfan MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.