How to Use the Flystack MCP in Pydantic AI

Validate your cluster metrics and deployment configurations at runtime with Pydantic AI and Flystack.

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Connect Flystack MCP to Pydantic AI

Create your Vinkius account to connect Flystack 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 Flystack with Pydantic AI

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Strict runtime schema validation

Stop letting your agents parse loose JSON; when you run `get_cluster_metrics`, this integration ensures the returned CPU and memory data matches your Pydantic models exactly. If the Flystack API returns unexpected null values or modified keys, the execution fails immediately. You get type safety instead of silent runtime bugs or hallucinated metrics.

Verified deployment auditing with Pydantic AI

Audit your workloads safely by calling `list_flystack_deployments` to get active services, then querying `get_deployment_details` for specific configurations. Every field is checked against your defined schemas. This MCP Server setup ensures your diagnostic agents only make decisions based on clean, structured infrastructure data.

Pre-flight connection checks

Before running a complex audit, use `check_api_status` to verify the connection is active. It returns a structured status that your agent validates before executing resource-heavy tasks. This step prevents your agent from running in circles when an external service is down. It also helps you map your overall infrastructure footprint by listing active environments via `list_flystack_clusters` safely.

Setup guide

Set up Flystack 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": {
        "flystack-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Flystack transactions")
print(result.output)

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Common questions about Flystack MCP in Pydantic AI

Install pydantic-ai-slim with the mcp extra. Use the MCPToolset class to connect to your Vinkius HTTP endpoint, and pass it directly into the toolsets list when instantiating your Agent.

Yes. Every tool response from Flystack is validated against strict Pydantic models at runtime. If the performance metrics don't match the expected schema, a validation error is raised.

It supports both Streamable HTTP and SSE transports. The MCPServerHTTP class is deprecated, so you should use the unified MCPToolset helper for your connections.

Yes. Pydantic AI is model-agnostic. You can run your diagnostic agents using local models or other cloud providers while keeping the exact same Flystack tool schemas.

Only infrastructure configurations and real-time performance metrics are accessed. No application databases or user PII are accessed. Data is processed in isolated, single-tenant V8 sandboxes and never persisted.

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