How to Use the Amazon Selling Partner MCP in Pydantic AI

Q: What's the setup for the Amazon Selling Partner server with Pydantic AI?

It's straightforward. Install the library with pip install "pydantic-ai-slimmcp". Then, instantiate the MCPToolset with your Vinkius server URL and pass it to your agent's toolsets list.

Q: How do I check on long-running reports?

First, you call createreport, which returns a reportId. You'll then need to poll the getreportstatus tool with that ID periodically. The status will update to DONE when the report is ready for download.

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Connect Amazon Selling Partner MCP to Pydantic AI

Create your Vinkius account to connect Amazon Selling Partner 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 Amazon Selling Partner with Pydantic AI

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Perplexity

Build Reliable Order Processors

When your agent calls `list_orders`, the JSON response is instantly parsed and validated against a Pydantic model. You get structured, predictable Python objects every time. No more writing defensive code to handle missing keys or weird data types. If the Amazon API ever returns an unexpected format, your code doesn't silently fail or process bad data. It raises a `ValidationError` immediately. This means you can build automated ordering workflows that you actually trust.

Safely Manage Your Product Catalog

Stop guessing what the API will return. With Pydantic AI, tools like `search_catalog` and `get_catalog_item` are strictly typed. The data your agent works with is guaranteed to match the schema you defined. This approach eliminates a whole class of bugs related to external data sources. You can focus on your business logic, not on parsing and cleaning unpredictable JSON from a third party. Your agent gets clean data, or it fails loudly.

A Type-Safe MCP Server for Pydantic AI

Think of this MCP Server as the data source and Pydantic AI as the quality control. The server provides a direct, authenticated connection to the Amazon Selling Partner API, exposing tools like `create_report` and `list_marketplaces`. Pydantic AI sits in your application and acts as the enforcer. It ensures that every piece of data coming from the server is correct and well-formed before your code ever sees it. It's the best way to build robust, maintainable agents.

Setup guide

Set up Amazon Selling Partner 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": {
        "amazon-selling-partner-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Amazon Selling Partner 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 Amazon Selling Partner. 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 Amazon Selling Partner MCP in Pydantic AI

Every response from this MCP server is automatically parsed and validated against a Pydantic model at runtime. If data from a tool like `list_orders` doesn't match the required schema, your agent will raise a `ValidationError` instead of processing it.

Yes. Pydantic AI is model-agnostic, so you're not locked into a specific provider. You can connect to this MCP server and drive your agent with a local model, a private endpoint, or a major commercial one.

It's straightforward. Install the library with `pip install "pydantic-ai-slim[mcp]"`. Then, instantiate the `MCPToolset` with your Vinkius server URL and pass it to your agent's `toolsets` list.

First, you call `create_report`, which returns a `reportId`. You'll then need to poll the `get_report_status` tool with that ID periodically. The status will update to `DONE` when the report is ready for download.

It's secure. The server only has access to specific data like product types and catalog item details, and only when your agent explicitly calls a tool. Vinkius operates on a zero-trust architecture, and your single auth token secures the entire connection.

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