Baidu Qianfan MCP for AI. Connect Enterprise LLMs and Multimodal AI Services
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Baidu Qianfan connects your AI agent directly to enterprise LLM services for Chinese language applications. This MCP manages chat completions, generates vector embeddings for RAG systems, creates images from text prompts, and lets you monitor model usage—all through one standard connection.
What your AI can do
Get embeddings
Converts text into a numerical vector, which allows your agent to measure the semantic similarity between documents.
List train jobs
Checks the status and history of any active or completed model training jobs.
Chat completions
Sends a message and context history to a Baidu Qianfan model to get a natural language reply.
Start and continue conversations with powerful foundation models, maintaining context across multiple turns.
Convert any block of text into mathematical vector embeddings so your agent can perform deep semantic searches against private datasets.
Create original images using advanced diffusion models, simply by providing a descriptive text prompt.
Access and retrieve standardized prompt templates to ensure your model outputs are always consistent in tone and format.
Monitor token consumption and service status programmatically, giving you a clear picture of your operational costs.
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Baidu Qianfan with 6 Tools
Use these tools to control model endpoints, generate embeddings, list datasets, check models, monitor jobs, and create images.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Baidu Qianfan on VinkiusGet Embeddings
Converts text into a numerical vector, which allows your agent to measure the semantic similarity between documents.
List Train Jobs
Checks the status and history of any active or completed model training jobs.
Chat Completions
Sends a message and context history to a Baidu Qianfan model to get a natural...
List Datasets
Retrieves a list of all data sets you have uploaded and indexed within the platform.
List Models
Shows which specific AI model services are available for use right now.
Text To Image
Creates a unique image file based on a detailed text prompt you provide.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Baidu Qianfan integration is available immediately — no restart needed.
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 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ 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
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.
VINKIUS INFRASTRUCTURE
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Sandboxed per request
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No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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 connection provides 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Dealing with AI services used to mean jumping between dashboards.
Today, if you want your agent to chat with the model *and* search private documents, you have to jump through hoops. You're in one dashboard for chatting; then you leave it and go to another console just to upload data and run embeddings. Then maybe a third tool is needed just to check what models are even available.
With this MCP, all those actions are centralized. Your agent talks to the chat service, runs `get_embeddings` on your documents, and checks model status—all in one place. You just get the ability to run complex workflows without leaving your primary development environment.
The Power of Structured Model Operations
Before this MCP, managing a full AI stack meant manually tracking model versions and data schemas across multiple dashboards. If you needed to generate images based on text or wanted to compare two chat models side-by-side, it was a time-consuming mess of copy/pasting API keys.
Now, the agent handles all that plumbing. It knows how to manage prompt templates, run `text_to_image`, and track usage metrics automatically. You just get reliable, multi-faceted AI functionality built right into your workflow.
What your AI can actually do with this
You can run complex workflows without leaving your agent interface. Think of it like a central control panel for high-performance AI models: trigger conversations with advanced chat completions using persistent context; generate the vector embeddings needed to power custom search indexes; or create detailed images just by typing a prompt.
The platform also lets you manage everything from model access and usage tracking to retrieving standardized prompt templates. If your work involves integrating multiple types of large language models into an application, this is what you need. Vinkius makes connecting these specialized services simple, letting your agent talk to Baidu's full suite of AI tools without needing complex API wrapper code.
It means the power of enterprise-grade Chinese LLMs is available right where you’re working.
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Here's how it actually works
The bottom line is: You connect once at Vinkius, and that gives you control over multiple enterprise AI services.
First, subscribe to this MCP and log into the Baidu Qianfan Console. You'll need an API Key and Secret Key.
Next, you enable the specific models you want to use—like a certain version of Ernie Bot or a specialized embedding model.
Finally, plug those credentials into your AI client via Vinkius. Your agent can then access and run all the available tools.
Who is this actually for?
This is for ML Engineers and System Integrators who build applications around large language models. If your job involves connecting a commercial-grade LLM to an existing business data stack, this MCP handles the plumbing.
Building Retrieval Augmented Generation (RAG) pipelines that need to query private documents and generate summaries using external models.
Connecting disparate enterprise systems or legacy applications with a modern LLM API layer for consistent data flow.
Testing and comparing the performance of different model versions (e.g., Turbo vs Speed) to recommend the best fit for new features.
What Changes When You Connect
Control model access: Easily check which services are available using the list_models tool. This stops you from accidentally calling an endpoint that's deprecated or offline.
Power RAG systems: Generate accurate vector embeddings for any text using get_embeddings, letting your agent search private knowledge bases, not just its training data.
Consistent outputs: Manage and retrieve centralized prompt templates so all model calls—whether it’s writing code or generating a report—use the same high-quality structure.
Multi-modal capability: Beyond text, you can trigger advanced image generation tasks using text_to_image to create visual assets directly from your agent's workflow.
Deep operational visibility: Keep track of costs and usage by monitoring token consumption. This helps you budget and optimize your calls with the service status check.
See it in action
Building a Customer Support Chatbot
The agent needs to answer questions based on hundreds of internal manuals. First, it uses get_embeddings on the manual text chunks and indexes them. Then, when a user asks a question, the agent runs a conversation using chat_completions, referencing those indexed documents for accurate answers.
Creating Marketing Assets at Scale
A product manager needs 20 unique headers for an ad campaign. Instead of manually prompting an image generator, the agent loops through a list of concepts and executes text_to_image twenty times, collecting all assets in one go.
Comparing Model Performance
A development team wants to know if the new 'Speed' model is better than the old 'Turbo' model for summarization. They use list_models to confirm both are active, then run parallel tests using chat_completions on the exact same data set.
Auditing AI Costs
The finance team needs a report showing how much token usage was consumed last month. They programmatically check the model service status and consumption metrics to ensure billing is accurate before submitting the budget proposal.
The honest tradeoffs
Hardcoding API keys
A junior dev writes code that manually inserts the secret key into a function call, making it impossible to update when credentials change.
Don't handle secrets directly. Use this MCP; Vinkius manages the connection and authentication flow for you, keeping your API keys secure and abstracted away from your core logic.
Treating all LLMs equally
Calling chat_completions without checking if the model is still supported or if it requires a specific endpoint name.
Always check available services first. Use list_models to confirm your target model (like Ernie-4.0) is active before you send any message via chat_completions.
Manual embedding pipelines
Trying to use a general web search tool instead of generating vector embeddings for specific, proprietary documents.
For internal knowledge only, you must generate vectors. Use get_embeddings on your document text before building any RAG pipeline so the agent searches exactly what you own.
When It Fits, When It Doesn't
Use this MCP if your core challenge is integrating multiple types of enterprise AI capabilities—chatting, image generation, and deep semantic search—into a single workflow. You need this when working with specialized or regional LLMs that require dedicated connection management.
Don't use it if you just need a simple, off-the-shelf text summarization tool for public data; then, a simpler, general API might suffice. Conversely, if your entire product revolves around building a complex knowledge graph over proprietary documents and generating accompanying visuals, this is the right choice because of its structured access to get_embeddings and text_to_image. It's built for depth and enterprise complexity.
Questions you might have
How do I use the chat_completions tool with my proprietary data? +
You first run get_embeddings on your documents to convert them into vectors. Then, when you call chat_completions, the agent uses those vectors as context for the model so it answers based on your private data.
Can I generate images using text_to_image within my workflow? +
Yes. You can trigger image generation tasks directly through this MCP, which is useful when you need to create visual content that matches the context of a conversation or article.
What if I want to check what models are available for chat_completions? +
You simply use list_models. This tool shows all the specific AI services you have access to, letting you choose the best one for your current task.
How do I know if a model is ready for production use? +
Use list_train_jobs to check the status of any model training. This confirms that the model has finished its job and is stable enough for real-world integration.
When I call `get_embeddings`, what credentials do I need for my AI agent? +
You must provide a valid API Key and Secret Key. These are generated in the Baidu Qianfan Console. Your agent uses these keys to authenticate your requests, ensuring secure access to the embedding service.
If I run `chat_completions`, what happens if my calls hit a rate limit? +
The API response will include an explicit error code detailing the rate violation. Your client should implement exponential backoff and retry logic to gracefully manage temporary service restrictions.
How do I use `list_datasets` to verify my data sources before RAG? +
The tool lists available datasets, providing IDs and names. To actually ingest or query the content, you'll need a separate workflow step that passes those listed identifiers to your retrieval pipeline.
How does `list_train_jobs` help me monitor custom model development? +
It fetches metadata about active and completed training runs. This lets you check the current status, review job IDs, and verify when a customized foundation model is ready for deployment.
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.
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