How to Use the AppsFlyer (Pull API) MCP in Pydantic AI

Q: How do I register the AppsFlyer (Pull API) toolset in Pydantic AI?

Use the MCP Server setup via the MCPToolset class pointing to your Vinkius HTTP server URL. Pass this toolset instance inside the toolsets list when initializing your Pydantic AI agent.

Fetch type-safe AppsFlyer marketing attribution reports validated at runtime with Pydantic AI.

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Connect AppsFlyer (Pull API) MCP to Pydantic AI

Create your Vinkius account to connect AppsFlyer (Pull API) 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 AppsFlyer (Pull API) with Pydantic AI

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Perplexity

Validate raw marketing data with Pydantic AI.

When parsing raw attribution files, a single missing column can break your entire data pipeline. Your agent uses `get_installs_report` to fetch non-organic installs, and the framework validates the response schema before your code even touches it. If the API structure changes or returns unexpected null values, the framework raises a validation error immediately. This prevents silent data corruption and keeps your internal database clean and reliable.

Verify connections safely using this MCP Server.

Before kicking off a massive reporting run, your agent can execute `get_account_check` to verify your credentials are valid. This MCP Server returns a clean connection status that your agent validates against a strict Pydantic schema. This step prevents your agent from wasting LLM tokens on broken API calls. If the check fails, the execution stops right there, saving you money and protecting your system from infinite retry loops.

Parse in-app event data with absolute correctness.

Tracking user actions requires absolute precision. Your agent can call `get_in_app_events_report` to pull raw event logs and convert them into strongly typed Python objects. You can also pull aggregate performance views using `get_daily_report` or `get_partners_report`. Because everything is typed, you can confidently write logic that routes this data to your internal dashboards without worrying about runtime type errors.

Setup guide

Set up AppsFlyer (Pull API) 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": {
        "appsflyer-pull-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent AppsFlyer (Pull API) transactions")
print(result.output)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by AppsFlyer. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about AppsFlyer (Pull API) MCP in Pydantic AI

Use the MCP Server setup via the `MCPToolset` class pointing to your Vinkius HTTP server URL. Pass this toolset instance inside the `toolsets` list when initializing your Pydantic AI agent.

The framework will fail loudly with a validation error. This ensures your agent never processes malformed attribution data, protecting your downstream databases from corrupt inputs.

It supports both Streamable HTTP and SSE transports. Since Vinkius hosts the server externally, you just need to point your `MCPToolset` to the provided HTTP endpoint.

Yes, Pydantic AI is model-agnostic. You can connect this server to local models, Anthropic, or OpenAI, and the MCP Server runtime validation will function exactly the same way.

All raw install logs, geographic summaries, and event data are validated in memory on your local runtime. The Vinkius sandbox acts as a secure, ephemeral bridge, meaning your private attribution metrics are never stored or exposed.

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