How to Use the Guance Cloud / 观测云 MCP in Pydantic AI

Q: Can Pydantic AI handle Guance Cloud / 观测云 query results?

Yes. You define the expected output schema for querydata in your agent. If the raw DQL results don't fit your model, you get an immediate error.

Strictly validate your Guance Cloud / 观测云 data with Pydantic AI and MCP Server.

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Connect Guance Cloud / 观测云 MCP to Pydantic AI

Create your Vinkius account to connect Guance Cloud / 观测云 to Pydantic AI 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 Guance Cloud / 观测云 with Pydantic AI

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Validate Guance Cloud / 观测云 with Pydantic AI

Every response from `list_events` is parsed against your Pydantic models. If the API returns malformed data, the agent stops immediately, preventing silent failures in your observability logic. Use `get_event` to fetch specific details. Pydantic ensures the event object matches your expected structure, so you never process incomplete data.

Type-safe dashboard management in Pydantic AI

Retrieve your dashboard list using `list_dashboards` and let Pydantic handle the serialization. This guarantees your agent is working with valid IDs and names before it attempts to display metrics. Use `get_monitor` to verify monitor configurations. The strict validation catches any schema drift in the monitor settings before it affects your alerting logic.

Audit infrastructure keys via MCP Server

Call `list_access_keys` to get a list of your credentials. Pydantic models enforce the structure of these keys, making it easy to spot unauthorized or deprecated entries. Use `get_billing` to track costs. The tool provides usage stats that your agent validates against your budget constraints defined in your Pydantic schemas.

Setup guide

Set up Guance Cloud / 观测云 MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "guance-cloud-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Guance Cloud / 观测云 tools.",
)

result = await agent.run("List recent Guance Cloud / 观测云 transactions")
print(result.output)

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Common questions about Guance Cloud / 观测云 MCP in Pydantic AI

The MCPToolset automatically validates every tool output against your defined Pydantic models. If the API schema changes, your agent throws a validation error instead of crashing.

Yes, because it eliminates the risk of hallucinated fields. By enforcing strict data structures, you ensure that your observability workflows remain stable regardless of API updates.

Yes. You define the expected output schema for `query_data` in your agent. If the raw DQL results don't fit your model, you get an immediate error.

You install the server locally or remotely and point your MCPToolset to the endpoint. The connection is handled via standard HTTP or SSE transport.

The platform provides read-only access to your workspace logs and dashboard configurations. All interactions are ephemeral and validated at the runtime level by your Pydantic models.

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