How to Use the Togai (Usage Metering & Pricing) MCP in Pydantic AI

Q: What is the best way to list all customers for Togai (Usage Metering & Pricing) in Pydantic AI?

The listcustomers tool handles this. It provides a full, validated roster of every customer ID you need to check before running billing logic.

Q: Can I automate account updates for Togai (Usage Metering & Pricing) using Pydantic AI?

Yes, your agent calls the updateaccount tool. Because of Pydantic's validation, you know those updates are structured correctly and won't cause runtime errors.

Q: Does the MCP Server support bulk event ingestion for Togai (Usage Metering & Pricing) with Pydantic AI?

Yeah. ingestbatchevents lets your agent send large groups of usage records, which is designed to work perfectly within a typed Python environment.

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Connect Togai (Usage Metering & Pricing) MCP to Pydantic AI

Create your Vinkius account to connect Togai (Usage Metering & Pricing) 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 Togai (Usage Metering & Pricing) with Pydantic AI

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Manage Billing Cycles with the MCP Server

Need to know what's owed? You can check current billing status by calling `list_invoices`. This gives you a clear view of all pending and past bills. Agents also track usage history using `get_metrics`, letting your agent pinpoint exactly where consumption happened. Agents maintain full visibility over the financial side of things. They can list available rate cards with `list_rate_cards` or check an account's current balance using `get_wallet_balance`. This keeps billing logic predictable for Pydantic AI.

Process Usage Events via Type-Safe Tools

When something happens, you need to record it. The agent calls `ingest_event` when a single event occurs, requiring specific details like the schema name and timestamp. For bulk processing, use `ingest_batch_events` to handle large volumes of usage data efficiently. This process keeps your billing accurate because every response is validated. You'll want to ensure incoming events include account ID and dimensions so you can correctly attribute costs.

Maintain Customer Records with Pydantic AI

Your agent needs to manage who's paying. Use `list_accounts` to see all existing accounts, or call `create_account` for a new user. Updates are handled by the `update_customer` tool when details change. The system also lets you confirm feature access using `check_entitlement`. If the account needs more funds, your agent can quickly call `grant_credit`.

Setup guide

Set up Togai (Usage Metering & Pricing) 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": {
        "togai-usage-metering-pricing-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Togai (Usage Metering & Pricing) transactions")
print(result.output)

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Common questions about Togai (Usage Metering & Pricing) MCP in Pydantic AI

You use the MCP Server's tools. To track consumption, you call `get_metrics` or ingest raw data using `ingest_event`. The type-safe nature of Pydantic ensures that any returned metrics are correct.

The `list_customers` tool handles this. It provides a full, validated roster of every customer ID you need to check before running billing logic.

Yes, your agent calls the `update_account` tool. Because of Pydantic's validation, you know those updates are structured correctly and won't cause runtime errors.

Yeah. `ingest_batch_events` lets your agent send large groups of usage records, which is designed to work perfectly within a typed Python environment.

It touches account and customer billing data. Specifically, it handles usage events, rate cards, wallet balances, and invoice records.

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