How to Use the Baidu Qianfan MCP in LlamaIndex

Q: Can LlamaIndex filter which Baidu Qianfan tools are active?

Yes. Use the allowedtools filter when configuring the MCP client. This restricts your LlamaIndex agent to specific operations, like only allowing embeddings while blocking image generation.

Index Baidu Qianfan model data into your LlamaIndex vector stores for semantic search and RAG applications.

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Connect Baidu Qianfan MCP to LlamaIndex

Create your Vinkius account to connect Baidu Qianfan to LlamaIndex 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 LlamaIndex

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Index Baidu Qianfan Model Services

LlamaIndex thrives on structured knowledge. Instead of just pulling a list of available AI models, you can call `list_models` and push the results directly into a vector store. Now your application can semantically search for the right Baidu endpoint based on user queries. The same applies to your training runs. Run `list_train_jobs` and index the output. Your RAG setup can now answer questions about past fine-tuning performance by querying actual historical metadata rather than guessing.

Generate Embeddings via MCP Server

Vector search requires high-quality embeddings. You can configure your LlamaIndex pipeline to route text chunks through the `get_embeddings` tool. The MCP standard ensures the data formats correctly before hitting Baidu's endpoint. This setup keeps your architecture clean. You manage the Baidu connection in Vinkius, while LlamaIndex focuses entirely on chunking documents and retrieving the nearest neighbor vectors.

Grounded Chat Completions

Combine your vector store with Baidu's generation capabilities. LlamaIndex retrieves relevant context from your documents and feeds it into the `chat_completions` tool. You get answers backed by your actual data. Fallback tools provide extra flexibility. If a user asks for a visual representation of the indexed data, your LlamaIndex agent can trigger `text_to_image` to generate a graphic based on the synthesized text context.

Setup guide

Set up Baidu Qianfan MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Baidu Qianfan MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Baidu Qianfan tools.",
)
response = await agent.run("List recent Baidu Qianfan data")

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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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Common questions about Baidu Qianfan MCP in LlamaIndex

Install llama-index-tools-mcp via pip. Setup BasicMCPClient with your Vinkius URL, wrap it in McpToolSpec, and await the tool list conversion before passing it to FunctionAgent.

Yes. Use the allowed_tools filter when configuring the MCP client. This restricts your LlamaIndex agent to specific operations, like only allowing embeddings while blocking image generation.

Your setup chunks documents and retrieves context, then passes that context to Baidu via the chat completions tool. The server handles the API translation automatically.

No. Vinkius manages the remote connection. Your local LlamaIndex environment just needs the adapter to communicate with the server.

The server processes your prompt text and embedding strings. Vinkius routes this text through a zero-trust V8 isolate that destroys itself immediately after Baidu returns the response.

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