Qiniu Cloud MCP Server for LlamaIndex 11 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Qiniu Cloud as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Qiniu Cloud. "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Qiniu Cloud?"
)
print(response)
asyncio.run(main())
* 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 Qiniu Cloud MCP Server
Connect your AI agents to Qiniu Cloud (七牛云), the leading enterprise cloud storage and content delivery network in China. This MCP provides 10 tools to manage the full lifecycle of your cloud assets, from bucket orchestration and file manipulation to CDN cache refreshment and global traffic monitoring.
LlamaIndex agents combine Qiniu Cloud tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Storage Orchestration — List buckets and manage file lifecycles, including deletions and bulk operations
- File Management — Retrieve granular metadata for stored assets and generate download URLs programmatically
- CDN Optimization — Refresh cache and prefetch content to ensure high-performance delivery across the network
- Usage Analytics — Monitor bandwidth consumption and storage quotas directly through natural conversation
The Qiniu Cloud MCP Server exposes 11 tools through the Vinkius. Connect it to LlamaIndex 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 Qiniu Cloud to LlamaIndex via MCP
Follow these steps to integrate the Qiniu Cloud MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
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 11 tools from Qiniu Cloud
Why Use LlamaIndex with the Qiniu Cloud MCP Server
LlamaIndex provides unique advantages when paired with Qiniu Cloud through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Qiniu Cloud tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Qiniu Cloud tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Qiniu Cloud, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Qiniu Cloud tools were called, what data was returned, and how it influenced the final answer
Qiniu Cloud + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Qiniu Cloud MCP Server delivers measurable value.
Hybrid search: combine Qiniu Cloud real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Qiniu Cloud to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Qiniu Cloud for fresh data
Analytical workflows: chain Qiniu Cloud queries with LlamaIndex's data connectors to build multi-source analytical reports
Qiniu Cloud MCP Tools for LlamaIndex (11)
These 11 tools become available when you connect Qiniu Cloud to LlamaIndex via MCP:
delete_file
Delete a file from a bucket
get_account_info
Retrieve Qiniu account profile
get_bucket_domains
Get domains associated with a specific bucket
get_cdn_bandwidth
Get CDN bandwidth statistics
get_file_stat
Get metadata for a specific file
get_pfop_status
Check the status of a persistent processing task
get_sms_stats
Get SMS sending statistics
list_buckets
List all storage buckets in your Qiniu account
list_files
List files within a bucket
persistent_file_op
Trigger persistent file processing (transcoding, etc.)
refresh_cdn_urls
Refresh CDN cache for specific URLs
Example Prompts for Qiniu Cloud in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Qiniu Cloud immediately.
"List all storage buckets in my Qiniu account."
"Get the file status for 'logo.png' in bucket 'media-assets'."
"Refresh the CDN cache for 'https://cdn.example.com/styles.css'."
Troubleshooting Qiniu Cloud MCP Server with LlamaIndex
Common issues when connecting Qiniu Cloud to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpQiniu Cloud + LlamaIndex FAQ
Common questions about integrating Qiniu Cloud MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Qiniu Cloud with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Qiniu Cloud to LlamaIndex
Get your token, paste the configuration, and start using 11 tools in under 2 minutes. No API key management needed.