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 Pydantic AI 10 tools — connect in under 2 minutes

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

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Guance Cloud / 观测云 through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Guance Cloud / 观测云 "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Guance Cloud / 观测云?"
    )
    print(result.data)

asyncio.run(main())

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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 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.

Pydantic AI validates every Guance Cloud / 观测云 tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Guance Cloud / 观测云 MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 10 tools from Guance Cloud / 观测云 with type-safe schemas

Why Use Pydantic AI with the Guance Cloud / 观测云 MCP Server

Pydantic AI provides unique advantages when paired with Guance Cloud / 观测云 through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Guance Cloud / 观测云 integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Guance Cloud / 观测云 connection logic from agent behavior for testable, maintainable code

Guance Cloud / 观测云 + Pydantic AI Use Cases

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

Type-safe data pipelines: query Guance Cloud / 观测云 with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Guance Cloud / 观测云 tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Guance Cloud / 观测云 and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Guance Cloud / 观测云 responses and write comprehensive agent tests

Guance Cloud / 观测云 MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Guance Cloud / 观测云 to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Guance Cloud / 观测云 to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Guance Cloud / 观测云 + Pydantic AI FAQ

Common questions about integrating Guance Cloud / 观测云 MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Guance Cloud / 观测云 MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.