How to Use the UPYUN Developer Platform MCP in Pydantic AI

Q: How does Pydantic AI validate file listing from the UPYUN Developer Platform?

When listdirectory runs, the raw list of files and folders is immediately validated against your defined schemas. If a path or name doesn't fit the expected type, the agent halts with a clear error message.

Q: If I need usage metrics, how does Pydantic AI handle that via the UPYUN Developer Platform?

The getserviceusage tool returns data that is automatically validated by Pydantic. This means you are guaranteed to receive numeric values for counts and consumption figures.

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Connect UPYUN Developer Platform MCP to Pydantic AI

Create your Vinkius account to connect UPYUN Developer Platform 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 UPYUN Developer Platform with Pydantic AI

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Structured File Discovery via MCP Server

`list_directory` reliably returns a directory structure, and your agent validates this list against a strict Pydantic schema. If the tool output deviates, the agent fails with an explicit validation error. This means you never have to worry about unexpected or malformed asset lists corrupting your program's logic.

Write and Manage Assets using UPYUN Developer Platform

When `create_text_file` runs, the agent handles the file upload. More importantly, it processes the resulting file metadata through Pydantic validation, guaranteeing that any subsequent calls treat the new asset correctly. Using `delete_file` is also type-checked; you can't accidentally reference a non-existent path without getting an immediate error.

Validate Resource Metadata with UPYUN Developer Platform

`get_file_info` retrieves metadata, and Pydantic validation forces that data into expected Python types. This prevents silent corruption where a field might return unexpected text instead of an integer count. Similarly, `get_service_usage` ensures the usage metrics are parsed as numbers, keeping your agent's calculations accurate.

Setup guide

Set up UPYUN Developer Platform 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": {
        "upyun-developer-platform-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent UPYUN Developer Platform transactions")
print(result.output)

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Common questions about UPYUN Developer Platform MCP in Pydantic AI

When `list_directory` runs, the raw list of files and folders is immediately validated against your defined schemas. If a path or name doesn't fit the expected type, the agent halts with a clear error message.

Pydantic ensures that any data derived from or related to the new file—like its name, size, or location—is correctly typed and structured for your agent's subsequent steps. You get type safety on output.

The `get_service_usage` tool returns data that is automatically validated by Pydantic. This means you are guaranteed to receive numeric values for counts and consumption figures.

Yes. By enforcing runtime validation on all inputs and outputs, Pydantic drastically reduces the chance of hallucinated fields or unexpected data types entering your production code.

The server touches file metadata and usage metrics. These are treated as structured records, ensuring that dates, sizes, and counts are always parsed into predictable Python types.

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