How to Use the LinkedIn Ads MCP in Pydantic AI

Q: How do I initialize the LinkedIn Ads MCP Server in Pydantic AI?

Use the unified MCPToolset pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your Pydantic AI Agent instance to make tools like listadcampaigns instantly available to your model.

Build type-safe, validated LinkedIn Ads workflows using Pydantic AI to eliminate runtime API errors.

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Connect LinkedIn Ads MCP to Pydantic AI

Create your Vinkius account to connect LinkedIn Ads 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 LinkedIn Ads with Pydantic AI

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Validate campaign structures in Pydantic AI

Stop letting malformed API responses break your production code by validating every campaign list returned by `list_ad_campaigns` against strict schemas. If the LinkedIn API returns unexpected null values or modified fields, the framework catches it immediately. You get type safety across your entire campaign management pipeline.

Type-safe analytics processing

Pulling metrics with `get_ad_analytics` gives you structured data that Pydantic AI validates instantly. The agent uses `list_campaign_groups` to organize your spend. Because the tool outputs are strictly typed, your agent can calculate ROAS confidently without silent type coercion bugs.

Audit creatives and conversion rules safely

Your agent calls `list_ad_creatives` and `list_conversion_rules` to verify that every active ad has a matching tracking rule in place. Pydantic AI guarantees that the returned tracking configurations conform to your expected models. If a conversion rule is misconfigured, your agent flags it without crashing your entire monitoring loop.

Setup guide

Set up LinkedIn Ads 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": {
        "linkedin-ads-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent LinkedIn Ads transactions")
print(result.output)

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Common questions about LinkedIn Ads MCP in Pydantic AI

The framework wraps the MCP tools in type-safe signatures. When your agent calls `list_ad_accounts`, Pydantic AI validates the incoming JSON against its internal models at runtime, raising a clear error if anything is malformed.

Yes. Pydantic AI is model-agnostic. You can connect this server to your agent and run it with Anthropic, Gemini, or local models while maintaining strict type validation on tools like `get_ad_analytics`.

Use the unified `MCPToolset` pointing to your Vinkius HTTP endpoint. Pass this toolset directly into your Pydantic AI `Agent` instance to make tools like `list_ad_campaigns` instantly available to your model.

If a schema change occurs, Pydantic AI will fail loudly during runtime validation instead of silently processing corrupt data. This lets you catch and fix integration issues before they affect your live budget calculations.

Vinkius manages your API tokens on its secure, zero-trust infrastructure. Your Pydantic AI agent only needs a single endpoint token to connect, ensuring your raw credentials, ad creatives, and conversion configurations are never exposed in your Python environment or codebase.

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