How to Use the Apex & Division Stats by TRN MCP in Pydantic AI

Q: Can I use Apex & Division Stats by TRN for Division 2 stats in Pydantic AI?

Yes, you use getdivision2matchhistory and getdivision2pvpstats to feed data into your models. The agent validates these stats against your defined schemas.

Type-safe gaming stats for Pydantic AI using the Apex & Division Stats by TRN MCP Server.

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Connect Apex & Division Stats by TRN MCP to Pydantic AI

Create your Vinkius account to connect Apex & Division Stats by TRN 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 Apex & Division Stats by TRN with Pydantic AI

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Enforce schema validation with Pydantic AI

Every response from `get_apex_player_profile` is checked against Pydantic models. If the Tracker Network returns malformed data, your agent stops immediately. This prevents your agent from hallucinating stats or crashing on bad inputs. You get a clean, validated object every time a tool runs.

Retrieve match history in Pydantic AI

The `get_apex_match_history` tool returns recent session logs. Your Pydantic AI agent parses these into structured objects that match your internal game logic. This typed approach ensures your agent always knows which fields are available. You avoid runtime errors by defining exactly what a match looks like.

Fetch game metadata with Pydantic AI

Use `get_apex_game_metadata` to keep your agent updated on current game states. It populates your agent's knowledge base with reference data. Your agent then uses this to interpret player stats correctly. Everything remains type-safe, from the initial query to the final summary.

Setup guide

Set up Apex & Division Stats by TRN 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": {
        "apex-division-stats-by-trn-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Apex & Division Stats by TRN tools.",
)

result = await agent.run("List recent Apex & Division Stats by TRN transactions")
print(result.output)

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Common questions about Apex & Division Stats by TRN MCP in Pydantic AI

Pydantic AI provides runtime validation for all tool responses. If the API structure changes, your agent throws a clear error instead of processing bad data.

Yes, you use `get_division2_match_history` and `get_division2_pvp_stats` to feed data into your models. The agent validates these stats against your defined schemas.

Pydantic AI rejects the response if it doesn't fit your model. This forces your agent to handle the error gracefully rather than injecting junk data.

You install the slim package, define your MCPToolset with the server URL, and pass it to your Agent. It works immediately with your existing models.

Your player information is never stored by the Vinkius platform. The server acts as a stateless gateway, passing encrypted traffic directly to your Pydantic AI agent.

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