How to Use the AppFollow MCP in Pydantic AI

Strictly type-safe AppFollow integrations for your Pydantic AI agent.

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Connect AppFollow MCP to Pydantic AI

Create your Vinkius account to connect AppFollow 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 AppFollow with Pydantic AI

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Type-safe AppFollow data validation

Use `list_reviews` to pull feedback that hits your Pydantic models directly. If the data format is unexpected, the agent halts immediately to prevent errors. `get_ratings` returns star distributions that our system validates at runtime. You get clean data objects every single time you query.

Pydantic AI server performance monitoring

Call `get_rankings` to monitor store positions with full type safety. Your agent logic relies on structured outputs, which makes building reliable workflows much easier. `get_reviews_summary` provides the core metrics your agent needs to assess app health. It ensures every response matches your expected schema.

Manage app info and history

Query `get_app_info` to get current metadata for your apps. The strict typing ensures your agent never misinterprets an app name or store ID. `get_ratings_history` provides the historical data required for trend analysis. It integrates perfectly with your existing Pydantic-based agent structures.

Setup guide

Set up AppFollow 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": {
        "appfollow-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about AppFollow MCP in Pydantic AI

Add the MCPToolset to your agent's configuration. You just point it at the Vinkius HTTP endpoint, and your agent treats every AppFollow tool as a native function.

It does. Because we use the standard protocol, Pydantic AI checks every response against your models at runtime. You won't have to deal with silent data corruption.

The server is model-agnostic. Whether you use Anthropic or local models, Pydantic AI ensures the data from AppFollow is always formatted correctly for your agent.

Yes. The integration supports SSE and HTTP transports, which fit naturally into the async loop of your Pydantic AI project.

We keep your AppFollow API keys locked behind our Vinkius endpoint. Your Pydantic AI agent never sees your raw credentials, only the specific data requested through our secure MCP server.

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