How to Use the Lemon Squeezy MCP in Pydantic AI

Connect Lemon Squeezy to your Pydantic AI agents with this type-safe MCP Server.

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Connect Lemon Squeezy MCP to Pydantic AI

Create your Vinkius account to connect Lemon Squeezy 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 Lemon Squeezy with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Guarantee strict schema validation for order lookups

Integrating `get_order` and `list_orders` with your type-safe agent workflows is simple. Pydantic AI validates every incoming JSON payload from your store against strict Python models before your agent can use the data. If the API returns an unexpected format, the agent raises a validation error immediately. This prevents your model from making decisions based on corrupted pricing or missing customer fields.

Pull validated subscription records safely

Your agent uses `get_subscription` and `list_subscriptions` to check user accounts without risking silent runtime failures. Because the framework is model-agnostic, you can swap your underlying LLM while maintaining absolute data integrity. You connect using the unified toolset class, which handles the underlying HTTP communications. This approach replaces older, deprecated connection methods for a cleaner codebase.

Inspect store setups and product catalogs

The MCP Server exposes `list_products` and `list_variants` so your agent can inspect your live inventory. It pulls active SKUs and matches them against user requests, ensuring your agent only recommends valid purchase options. Using these tools guarantees your agent works with real-time data instead of relying on outdated training weights. It reads the exact pricing structure defined in your dashboard.

Setup guide

Set up Lemon Squeezy 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": {
        "lemon-squeezy-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Lemon Squeezy. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Lemon Squeezy MCP in Pydantic AI

Use the unified MCPToolset class with your HTTP endpoint URL. Pass this toolset into the toolsets parameter list when creating your Pydantic AI agent.

The framework raises a loud validation error at runtime. This prevents your agent from hallucinating or processing incorrect order details.

Yes, the framework is completely model-agnostic. You can run local models or commercial APIs alongside this toolset to query your product catalog.

Yes, you should use the unified toolset class for all new integrations. It provides a more stable connection layer for your Python agents.

All customer names and transaction details are processed within this MCP Server in an isolated V8 sandbox. Authentication is handled via a single endpoint token, meaning your master API keys are never exposed.

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