How do I find my Guance Cloud API Key?

Log in to your Guance Cloud workspace, navigate to [Management] → [API Key Management], and generate a new key. Use the provided value as your DF-API-KEY.

Data Query Language (DQL) is the query syntax used by Guance Cloud to retrieve metrics, logs, and other observability data. You can use the `query_data` tool to execute these statements.

Can I check my data usage via the agent?

Yes. Use the `get_billing` tool to retrieve current data usage and billing statistics for your workspace, helping you manage costs and resource allocation.

Guance Cloud / 观测云 MCP Server for CrewAI 10 tools — connect in under 2 minutes

Name: Guance Cloud / 观测云
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

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

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Guance Cloud / 观测云 Specialist",
    goal="Help users interact with Guance Cloud / 观测云 effectively",
    backstory=(
        "You are an expert at leveraging Guance Cloud / 观测云 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 Guance Cloud / 观测云 "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

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

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* 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 Guance Cloud / 观测云 MCP Server

Empower your AI agent to orchestrate your entire observability stack with Guance Cloud (观测云), the leading next-generation monitoring platform. By connecting Guance Cloud to your agent, you transform complex system monitoring, log analysis, and incident response into a natural conversation. Your agent can instantly list your monitors, retrieve detailed dashboard configurations, browse system events, and even execute Data Query Language (DQL) statements without you ever needing to navigate the Guance console. Whether you are troubleshooting a production outage or auditing resource usage, your agent acts as a real-time site reliability assistant, keeping your infrastructure data accurate and your systems healthy.

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

What you can do

Workspace Orchestration — Retrieve detailed metadata and status information for your Guance Cloud workspace.
Monitoring Control — List and retrieve detailed configurations for all system monitors and alert rules.
Event Auditing — Browse real-time observability events, including alerts, errors, and system changes.
Data Querying — Execute powerful DQL statements to retrieve specific metrics and logging data via natural language.
Operations Insights — Monitor billing usage and manage API access keys for your organizational infrastructure.

The Guance Cloud / 观测云 MCP Server exposes 10 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 Guance Cloud / 观测云 to CrewAI via MCP

Follow these steps to integrate the Guance Cloud / 观测云 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 10 tools from Guance Cloud / 观测云

Why Use CrewAI with the Guance Cloud / 观测云 MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Guance Cloud / 观测云 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 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

Guance Cloud / 观测云 + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Guance Cloud / 观测云 MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Guance Cloud / 观测云 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 Guance Cloud / 观测云, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Guance Cloud / 观测云 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 Guance Cloud / 观测云 against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Guance Cloud / 观测云 MCP Tools for CrewAI (10)

These 10 tools become available when you connect Guance Cloud / 观测云 to CrewAI via MCP:

get_billing

Get billing usage

get_event

Get event details

get_monitor

Get monitor details

get_workspace

Get workspace information

list_access_keys

List workspace access keys

list_dashboards

List all dashboards

list_events

) from the workspace. List observability events

list_log_sources

List log data sources

list_monitors

List all monitors

query_data

Query Guance data (DQL)

Example Prompts for Guance Cloud / 观测云 in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Guance Cloud / 观测云 immediately.

"List all active monitors in Guance Cloud."

"Show me recent events from the last hour."

"Query average CPU usage using DQL."

Troubleshooting Guance Cloud / 观测云 MCP Server with CrewAI

Common issues when connecting Guance Cloud / 观测云 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

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

Guance Cloud / 观测云 + CrewAI FAQ

Common questions about integrating Guance Cloud / 观测云 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.