Where do I find my Offerslook API Key?

Log in to your Offerslook network dashboard and navigate to the API settings section to find your Network ID and API Key.

Can I see conversions in real-time?

Yes! Use the `list_recent_conversions` tool to retrieve a list of the latest successful goal completions in your network.

How do I generate a performance report?

The `get_performance_report` tool allows you to specify a start and end date to retrieve aggregated metrics for your network.

Offerslook MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Offerslook
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Offerslook 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 Offerslook "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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About Offerslook MCP Server

Connect your Offerslook network account to your AI agent and streamline your performance marketing operations and partner management through natural conversation.

Pydantic AI validates every Offerslook tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Offer Management — List all active and paused marketing offers and retrieve detailed payout and tracking configuration.
Partner Oversight — Access profiles and performance settings for all advertisers and affiliates in your network.
Campaign Tracking — View active marketing campaigns and monitor recent conversion history in real-time.
Performance Reporting — Generate aggregated reports for specific date ranges to track clicks, conversions, and revenue.
Category Discovery — Browse the organizational categories used to group and manage your offers.
Deep Inspection — Fetch complete metadata for specific offers, partners, or campaigns using their unique IDs.

The Offerslook MCP Server exposes 10 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 Offerslook to Pydantic AI via MCP

Follow these steps to integrate the Offerslook 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 10 tools from Offerslook with type-safe schemas

Why Use Pydantic AI with the Offerslook MCP Server

Pydantic AI provides unique advantages when paired with Offerslook 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 Offerslook integration code

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

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

Offerslook + Pydantic AI Use Cases

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

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

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

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

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

Offerslook MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Offerslook to Pydantic AI via MCP:

get_advertiser_details

Get specific advertiser details

get_affiliate_details

Get specific affiliate details

get_offer_details

Get specific offer info

get_performance_report

Generate performance report

list_active_campaigns

List marketing campaigns

list_advertisers

List all advertisers

list_affiliates

List all affiliates/publishers

list_offer_categories

List offer categories

list_offers

List all marketing offers

list_recent_conversions

List recent conversions

Example Prompts for Offerslook in Pydantic AI

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

"List all active offers in my network."

"What is the status of advertiser ID 'adv_98765'?"

"Show me the performance report for the first week of March."

Troubleshooting Offerslook MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Offerslook + Pydantic AI FAQ

Common questions about integrating Offerslook 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 Offerslook MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.