Where do I find my Adrecord API Token?

Log in to your account (Adrecord or Adtraction), navigate to **Account** > **Settings**. You can generate or retrieve your API Token there. It will be used in the `X-Token` header for all requests.

Can I see my pending commissions?

Yes! Use the `list_transactions` tool to retrieve all tracked sales and leads, including their current status (Pending, Approved, Denied) and commission amounts.

Does this server support product feeds?

Yes, you can use the `get_product_feed` tool to retrieve structured product data from advertisers you are partnered with.

Adrecord MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: Adrecord
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Adrecord through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Adrecord "
            "(5 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Adrecord?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Adrecord MCP Server

Connect your Adrecord (powered by Adtraction) affiliate account to your AI agent to unlock professional performance marketing management. From auditing advertiser programs to tracking real-time sales and managing your media channels, your agent handles your affiliate ecosystem through natural conversation.

Pydantic AI validates every Adrecord tool response against typed schemas, catching data inconsistencies at build time. Connect 5 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Program Management — List and retrieve details for available advertiser programs, including commission rates and rules
Transaction Auditing — Retrieve detailed logs of sales and leads to monitor your marketing performance and ROI
Channel Oversight — List and manage your registered media channels (websites, social media) across the network
Product Feed Access — Retrieve and audit product data from advertisers to power your affiliate content
Performance Insights — Quickly identify top-performing programs and track your pending earnings directly from chat

The Adrecord MCP Server exposes 5 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Adrecord to Pydantic AI via MCP

Follow these steps to integrate the Adrecord MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 5 tools from Adrecord with type-safe schemas

Why Use Pydantic AI with the Adrecord MCP Server

Pydantic AI provides unique advantages when paired with Adrecord through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Adrecord integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Adrecord connection logic from agent behavior for testable, maintainable code

Adrecord + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Adrecord MCP Server delivers measurable value.

Type-safe data pipelines: query Adrecord with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Adrecord tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Adrecord and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Adrecord responses and write comprehensive agent tests

Adrecord MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect Adrecord to Pydantic AI via MCP:

get_product_feed

Fetch advertiser product data

get_program_details

Get program metadata

list_channels

List media channels

list_programs

List advertiser programs

list_transactions

Filterable by status. List sales and leads

Example Prompts for Adrecord in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Adrecord immediately.

"List all active advertiser programs I am partnered with."

"Show me all transactions from the last 30 days."

"Retrieve the product feed for program ID 5678."

Troubleshooting Adrecord MCP Server with Pydantic AI

Common issues when connecting Adrecord to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Adrecord + Pydantic AI FAQ

Common questions about integrating Adrecord MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Adrecord MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.