Can I see the flights associated with a campaign?

Yes, use the `list_flights` tool to get a full list of flights, which includes their parent campaign IDs and targeting metadata.

How do I check the status of my ad inventory sites?

The `list_sites` and `list_zones` tools provide real-time visibility into your inventory structure and the status of each placement.

Is it possible to list all uploaded creatives?

Absolutely. Use the `list_creatives` tool to retrieve a comprehensive list of all ad creatives configured in your Kevel account.

Kevel MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: Kevel
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

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

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

asyncio.run(main())

Fully ManagedVinkius Servers

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IAMAccess control

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DLPData protection

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Kevel MCP Server

Connect your Kevel (formerly Adzerk) account to any AI agent to streamline your ad serving operations. This MCP server allows your agent to manage advertisers, campaigns, flights, and inventory sites directly through natural language.

Pydantic AI validates every Kevel tool response against typed schemas, catching data inconsistencies at build time. Connect 11 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

Campaign Management — List and retrieve detailed configurations for campaigns and flights
Advertiser Oversight — Query and manage advertising entities and their metadata
Inventory Control — List and inspect sites, zones, and channels to manage your ad placements
Creative Audit — Access a comprehensive list of ad creatives and individual ad instances
Format Exploration — List supported ad types and sizes to ensure correct technical implementations

The Kevel MCP Server exposes 11 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 Kevel to Pydantic AI via MCP

Follow these steps to integrate the Kevel 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 11 tools from Kevel with type-safe schemas

Why Use Pydantic AI with the Kevel MCP Server

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

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

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

Kevel + Pydantic AI Use Cases

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

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

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

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

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

Kevel MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Kevel to Pydantic AI via MCP:

get_advertiser

Get details for a specific advertiser

get_campaign

Get details for a specific campaign

list_ad_types

g., banner, native). List available ad types

list_ads

List all ads

list_advertisers

List all advertisers in Kevel

list_campaigns

List all campaigns

list_channels

List all channels

list_creatives

) uploaded to the account. List all creatives

list_flights

List all flights

list_sites

List all sites

list_zones

List all zones

Example Prompts for Kevel in Pydantic AI

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

"Show me all active campaigns in Kevel."

"List all ad zones for the site with ID 12345."

"What ad types are supported in my Kevel account?"

Troubleshooting Kevel MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Kevel + Pydantic AI FAQ

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