How to Use the FastSpring MCP in Pydantic AI

Type-safe commerce management for Pydantic AI projects.

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Connect FastSpring MCP to Pydantic AI

Create your Vinkius account to connect FastSpring 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 FastSpring with Pydantic AI

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Runtime-validated commerce actions

Every response from `get_subscription_details` is validated against your Pydantic models. You get instant failures if the API returns unexpected data formats. This keeps your agent logic clean. You stop worrying about malformed data corrupting your subscription state.

Atomic subscription updates

Call `update_subscription_plan` knowing that your Pydantic schemas enforce strict field requirements. You avoid silent failures during plan transitions. Use `cancel_subscription` with full type safety. The agent validates the structure of the request before sending it to the platform.

Typed catalog and account access

Access your entire product catalog via `list_catalog_products`. The tool returns structured data ready for immediate use in your typed agent workflows. Use `get_account_details` to hydrate your Pydantic objects. You get a reliable way to manage account properties across your entire application.

Setup guide

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

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

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

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Common questions about FastSpring MCP in Pydantic AI

It prevents hallucinated fields and silent corruption. Every bit of data returned by the server is checked against your defined models.

The MCP Server maps API responses to your Pydantic models at runtime. If the data doesn't match, the agent throws a clear validation error.

Yes. You define the schema, and the server ensures the agent only operates on data that matches those specific constraints.

Absolutely. Pydantic AI is model-agnostic, letting you run these tools against any model you choose.

The server enforces strict API permissions. Your Pydantic models act as the final layer of defense against unauthorized data exposure.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript

Mastra AI sdk-typescript

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