How to Use the Treinta App MCP in Pydantic AI

Q: How do I check debt profiles using Treinta App and Pydantic AI?

You use the getclient tool. Since your agent uses Pydantic, you know that when it receives a client's debt profile, every field—like outstanding balance or account ID—will conform to your specified data types.

Q: Can I list all sales using Treinta App and Pydantic AI?

Yes. You can run listsales to get consolidated data, which is then passed through a Pydantic validator. This guarantees the resulting list of sales records has the exact structure your agent expects.

Q: What is the best way to record new inventory using Treinta App and Pydantic AI?

Use createproduct to add a new item, or use updateproductstock when stock changes. In either case, the resulting data structure is validated by Pydantic before your agent proceeds.

Type-safe e-commerce agents for Pydantic AI that validate every financial and inventory action.

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Connect Treinta App MCP to Pydantic AI

Create your Vinkius account to connect Treinta App 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 Treinta App with Pydantic AI

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Manage Client Data with Treinta App

The agent can fetch a client's debt profile using `get_client`, guaranteeing the response matches your defined Pydantic schema. If the API sends bad data, your system fails loudly, protecting against silent errors. Need to track who you sell to? Use `list_clients` to get a validated list of all registered customer records. This ensures reliable input for any subsequent business logic.

Update Inventory Stock via MCP Server

When physical stock changes, the agent runs `update_product_stock`. Because every response is type-validated by Pydantic, you're guaranteed that the stock count field is a proper integer. To add new merchandise or check existing items, use `create_product` and `get_product`, respectively. This maintains data integrity across your entire product catalog.

Audit Transactions with Pydantic AI

Every financial change is controlled. The agent uses `create_transaction` to log sales, or it can correct mistakes using `delete_transaction`. You'll always know exactly what happened and why. For reporting, your agent pulls overall performance data via `get_business_stats`, or simply lists all recorded book flow movements with `list_transactions`.

Setup guide

Set up Treinta App 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": {
        "treinta-app-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Treinta App MCP in Pydantic AI

You use the `get_client` tool. Since your agent uses Pydantic, you know that when it receives a client's debt profile, every field—like outstanding balance or account ID—will conform to your specified data types.

This server touches client debt profiles and macro P&L operations. The type-safe nature of the MCP Server means that when handling these financial metrics, you get guaranteed structure, eliminating risks from unexpected or corrupted fields.

Yes. You can run `list_sales` to get consolidated data, which is then passed through a Pydantic validator. This guarantees the resulting list of sales records has the exact structure your agent expects.

Use `create_product` to add a new item, or use `update_product_stock` when stock changes. In either case, the resulting data structure is validated by Pydantic before your agent proceeds.

The core value here is type safety. The MCP Server exposes tools, but Pydantic ensures that every single response—whether it's a list of clients or a single transaction record—is validated against your schema at runtime.

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