Can I automatically list all buckets in my Qiniu account?

Yes! Use the `list_buckets` tool. Your agent will retrieve a complete list of all Kodo storage buckets currently configured in your account.

How do I refresh the CDN cache for a specific URL?

Use the `refresh_cdn` tool with a list of target URLs. This will trigger the Qiniu Fusion CDN to purge the old cached versions and fetch the latest assets.

Can I retrieve metadata for a specific file?

Yes! Use the `get_file_stat` tool with the bucket name and file key. Your agent will return the file size, hash, and MIME type.

Qiniu Cloud MCP Server for CrewAI 11 tools — connect in under 2 minutes

Name: Qiniu Cloud
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 11 Tools Framework

Connect your CrewAI agents to Qiniu Cloud through the Vinkius — pass the Edge URL in the `mcps` parameter and every Qiniu Cloud tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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

from crewai import Agent, Task, Crew

agent = Agent(
    role="Qiniu Cloud Specialist",
    goal="Help users interact with Qiniu Cloud effectively",
    backstory=(
        "You are an expert at leveraging Qiniu Cloud tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Qiniu Cloud "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 11 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

When paired with CrewAI, Qiniu Cloud becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Qiniu Cloud tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Qiniu Cloud MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 11 tools from Qiniu Cloud

Why Use CrewAI with the Qiniu Cloud MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Qiniu Cloud through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Qiniu Cloud + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Qiniu Cloud MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Qiniu Cloud for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Qiniu Cloud, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Qiniu Cloud tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Qiniu Cloud against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Qiniu Cloud MCP Tools for CrewAI (11)

These 11 tools become available when you connect Qiniu Cloud to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Qiniu Cloud to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Qiniu Cloud + CrewAI FAQ

Common questions about integrating Qiniu Cloud MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.