How to Use the Commission Factory MCP in Pydantic AI

Build type-safe affiliate automation using Pydantic AI to validate Commission Factory data at runtime.

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Connect Commission Factory MCP to Pydantic AI

Create your Vinkius account to connect Commission Factory 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 Commission Factory with Pydantic AI

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Validate Affiliate Transactions with Pydantic AI and MCP

The `get_transaction_details` tool retrieves specific transaction records and validates the response against strict Pydantic schemas. This ensures your agent never processes corrupt transaction IDs or malformed commission amounts. If the API returns unexpected data formats, the framework raises a validation error immediately. This prevents silent failures in your accounting pipeline and keeps your ledger accurate.

Audit Joined Merchant Programs Safely

The `list_affiliate_merchants` tool pulls your active merchant partnerships and parses them into typed Python objects. Your agent analyzes these partnerships to ensure your affiliate links point to active, high-paying programs. Because every field is typed, your agent can safely filter programs by category or approval status. You write clean Python code that handles the output without worrying about missing dictionary keys.

Track Campaign Performance Metrics

The `get_performance_summary` tool fetches aggregated sales and commission metrics for your active campaigns. Pydantic AI ensures the returned floats and integers match your expected schemas before running any financial reports. You can build automated dashboards that rely on these typed metrics. The agent flags performance drops without risk of processing hallucinated numbers.

Setup guide

Set up Commission Factory 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": {
        "commission-factory-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Commission Factory MCP in Pydantic AI

Use the MCPToolset class pointing to your Vinkius HTTP URL. Pass this toolset directly into the toolsets argument of your Pydantic AI Agent to expose all eight affiliate marketing tools.

The framework automatically wraps the MCP tool definitions in Pydantic models. When tools like get_transaction_details execute, the response is parsed and validated against these models before reaching your agent.

Yes, Pydantic AI is model-agnostic. You can run local models via Ollama or llama.cpp and use this server to fetch your merchant performance metrics securely.

The framework's validation layer checks the field definition. If a field like commission rate is null but marked as required, Pydantic AI raises a validation error to prevent downstream math errors.

Your API tokens are managed entirely on Vinkius's secure infrastructure. The Python framework only interacts with the sandboxed MCP endpoint, keeping your sensitive merchant credentials hidden from the LLM provider.

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