Baidu Qianfan MCP. Orchestrate Ernie Bot, RAG, and Image Generation.
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Baidu Qianfan. This MCP lets your AI client manage entire workflows across Baidu's enterprise LLM ecosystem. You can trigger chat completions with Ernie Bot, generate vector embeddings for RAG, manage prompt templates, or create images using advanced diffusion models, all from a single agent connection.
What your AI agents can do
Chat completions
Sends a message to a Baidu Qianfan model, requiring you to specify the model endpoint name.
Get embeddings
Generates vector embeddings for any given text input.
List datasets
Retrieves a list of datasets that have been uploaded to the Qianfan platform.
Your agent sends a message to a specified Baidu Qianfan model to get a chat response, maintaining conversation context.
Your agent takes raw text and generates a vector embedding, preparing the data for use in search or RAG systems.
Your agent retrieves a list of datasets that have already been uploaded to the Qianfan platform.
Your agent lists all model services available through the Baidu Qianfan platform.
Your agent retrieves the status and list of model training jobs currently running on the platform.
Your agent takes a text prompt and generates a corresponding image using Baidu's diffusion models.
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Supported MCP Clients
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Baidu Qianfan MCP Server: 6 Tools for AI Workflow Ops
Use these tools to manage model endpoints, generate search vectors, list datasets, and orchestrate advanced AI tasks on the Baidu Qianfan platform.
019d841achat completions
Sends a message to a Baidu Qianfan model, requiring you to specify the model endpoint name.
019d841aget embeddings
Generates vector embeddings for any given text input.
019d841alist datasets
Retrieves a list of datasets that have been uploaded to the Qianfan platform.
019d841alist models
Fetches a list of all model services available on the Baidu Qianfan platform.
019d841alist train jobs
Lists the status and details of model training jobs.
019d841atext to image
Generates a visual image based on a simple text prompt.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Baidu Qianfan, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
You connect your agent directly to Baidu Qianfan, which lets you manage an entire suite of enterprise LLMs. You can trigger chat completions with a specific Baidu Qianfan model, maintaining conversation context. You can generate vector embeddings from raw text, setting up data for search or RAG systems. You'll get a list of all model services available on the platform by calling list_models.
You can check which datasets you've uploaded using list_datasets, and you can see the status and details of any model training jobs by running list_train_jobs. You also generate visual images from text prompts using text_to_image.
How Baidu Qianfan MCP Works
- 1 Subscribe to the server, then log into the Baidu Qianfan Console to set up your application.
- 2 Obtain your unique API Key and Secret Key, and ensure the necessary models (like Ernie-4.0-8K) are enabled.
- 3 Insert your credentials into the connection fields to allow your AI agent to manage Baidu AI workflows.
The bottom line is: You connect your AI client with specific credentials so it can execute complex, multi-step AI tasks on Baidu's platform.
Who Is Baidu Qianfan MCP For?
This is for the AI developer who needs to build production-grade applications using Chinese LLMs. It's for the system integrator bridging enterprise systems with Baidu's high-performance models. It's for the knowledge manager who needs to build reliable RAG pipelines and manage model assets.
Builds and tests multi-model applications, automating the comparison of different Ernie model versions.
Connects existing enterprise applications to Baidu's LLMs, treating the Qianfan platform as a core service layer.
Creates robust RAG pipelines by managing embeddings and chat services across internal datasets.
What Changes When You Connect
- You get full control over the entire model stack. Need to compare Ernie Turbo vs. Ernie 4.0? Use
chat_completionsto test different model versions side-by-side. - Build RAG systems quickly. By using
get_embeddings, you convert raw text into search vectors, making your knowledge base searchable by meaning, not keywords. - Keep model outputs consistent. Instead of relying on developers to remember prompt formats, use the prompt engineering tools to manage and retrieve centralized templates.
- Manage your assets programmatically. Use
list_datasetsandlist_modelsto check exactly which data sources and model services are available before building a workflow. - Automate image creation. You can trigger Text-to-Image tasks using
text_to_imagewith just a simple text prompt, bypassing manual UI steps. - Track usage without effort. Programmatically monitor token consumption and model service status, giving you visibility into costs and uptime.
Real-World Use Cases
Creating a Knowledge Retrieval System
A knowledge manager needs to build a search system over 10,000 internal documents. They first use list_datasets to confirm the source data, then run get_embeddings on the text, and finally feed those vectors into the agent to chat with Ernie Bot using chat_completions for summarized answers.
Generating Marketing Visuals for a Campaign
A marketing team needs 5 unique images for a product launch. They use text_to_image with a detailed prompt, and then they use chat_completions to iterate on the prompt, refining the style until the output is perfect.
Auditing Model Deployment Status
A system integrator needs to verify if the client has enabled the necessary Ernie model (e.g., 4.0-8K) before deployment. They call list_models to check service availability, then use list_train_jobs to confirm if any background training is currently running.
Developing a Multimodal AI Feature
A developer wants to build a feature that generates code documentation and an accompanying image. They use chat_completions to generate the text, then use the resulting text in get_embeddings and finally pass the prompt to text_to_image for a visual summary.
The Tradeoffs
Treating Models as Black Boxes
Manually entering the correct model endpoint name into the UI, hoping it works, or just calling chat_completions without knowing the supported models.
→
Before calling chat_completions, run list_models to get the exact, supported endpoint names. This prevents failures and ensures you are always using the right model service.
Forgetting Data Preparation
Feeding raw text directly into a search query or chat prompt, knowing that the AI will miss context or retrieve outdated information.
→
Always use get_embeddings first. This converts your raw text into dense vectors, which is what your RAG system needs for accurate search.
Ignoring Job Status
Assuming a model is ready for use and calling chat_completions, only to find out later that the model is still undergoing a long-running training job.
→
Use list_train_jobs to check the status of any background training. Wait until the job reports 'Completed' before trying to use the model via chat_completions.
When It Fits, When It Doesn't
Use this if you need to build a full, multi-step AI pipeline that touches data storage, model endpoints, and content creation. The get_embeddings tool is the core enabler for any data retrieval system, so if you're building RAG, you need this. You don't use this if you just need a simple, one-off API call for text generation; for that, a dedicated single-function wrapper might suffice. However, if you need to check the status of the underlying infrastructure (models or datasets), the list_models and list_datasets tools are non-negotiable additions.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Baidu Qianfan. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Managing AI data and models shouldn't feel like logging into five different dashboards.
Today, managing a full LLM stack means bouncing between the model console, the data upload dashboard, and the API key management page. You copy dataset IDs, switch tabs to check model endpoints, and then paste them into a separate notebook just to run a chat test. It’s slow, and you'll lose context.
With the Baidu Qianfan MCP Server, your agent handles the entire sequence. You list the datasets with `list_datasets`, then generate the embeddings with `get_embeddings`, and immediately feed that data into `chat_completions`. It's all one flow, zero context switching.
Baidu Qianfan MCP Server: Orchestrate model services and data.
Previously, if you wanted to generate an image, you had to switch to a separate diffusion API and manage its keys and parameters. If you wanted to chat, you needed a different setup entirely. These tasks were siloed.
Now, your agent handles both. You use `text_to_image` to create a visual asset, and then you can use the text description of that asset in `chat_completions` to write a supporting article. The tools talk to each other. That's the difference.
Common Questions About Baidu Qianfan MCP
How do I use the `get_embeddings` tool with Baidu Qianfan? +
You pass the text you want to embed directly to get_embeddings. The tool returns a vector (a list of numbers) that you can then use to query your vector database for similar documents.
Is `chat_completions` the only way to talk to Ernie Bot? +
No. While chat_completions is the primary method, you should check list_models first. This confirms the exact endpoint names and available versions of the Ernie Bot you can access.
Can I use `list_datasets` to check my data sources? +
Yes. Running list_datasets gives you a clean list of all the datasets uploaded to your Qianfan account. This is the first step before you can process any data with get_embeddings.
What's the purpose of `list_train_jobs`? +
This tool lets you monitor the status of model training jobs. You check this if you need to know if a model is currently being updated or trained in the background.
How does `text_to_image` work? +
You provide a text prompt, and the text_to_image tool runs Baidu's diffusion models to generate and return a visual file based on that description.
How do I use `list_models` to see which Baidu AI models are available? +
The list_models tool shows all active model services you have access to. This is helpful for confirming if a specific model, like Ernie 4.0, is enabled and ready to use for chat or generation tasks.
What's the difference between `chat_completions` and `get_embeddings`? +
They perform different tasks. Use chat_completions when you want conversational dialogue or text generation. Use get_embeddings when you need to convert raw text into numerical vectors for search or RAG systems.
How do I manage my credentials or API keys for this server? +
Credentials are managed directly through the Baidu Qianfan Console. You'll need to obtain your API Key and Secret Key there and input them into the server connection fields.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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