How to Use the Authkey MCP in Pydantic AI

Build type-safe messaging workflows in Pydantic AI that validate every Authkey SMS, email, and OTP response at runtime.

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Connect Authkey MCP to Pydantic AI

Create your Vinkius account to connect Authkey 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 Authkey with Pydantic AI

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Claude

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Type-safe OTP verification with Pydantic AI

This MCP Server provides type-safe access to `send_otp` and `verify_otp` so your agent can execute user verification with absolute runtime certainty. When your agent calls these tools within Pydantic AI, every returned field is validated against strict Pydantic models. If the API returns an unexpected payload structure, the framework raises a validation error immediately rather than letting corrupt data slip into your database. It's a clean way to prevent silent failures in production. Your agent can confidently process verification results, knowing that the data returned from `get_sms_status` matches your exact schema expectations.

Model-agnostic SMS dispatching using an MCP Server

The `send_sms` and `send_bulk_sms` tools let you dispatch text messages uniformly across any LLM backend you choose. Pydantic AI allows you to swap your underlying LLM without rewriting your communication tools. You can run your agent on Claude, GPT-4, or a local model, and this MCP Server exposes the communication gateway uniformly. You can monitor the history of these dispatches safely. The agent calls `list_sms_history` to verify delivery, and Pydantic AI validates the list of messages against your internal data models before passing them to your application logic.

Real-time balance and status checking

Your agent can monitor API spend in real time by querying the `check_balance` and `check_authkey_status` tools. With Pydantic AI, you can build a supervisor agent that constantly checks these endpoints. Because the response is validated against strict types, your code can safely trigger automated alerts or top-up scripts when balances run low. The same safety applies to monitoring delivery performance. If `get_email_status` or `get_voice_status` returns an error state, the agent parses the structured error object safely, preventing application crashes.

Setup guide

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

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Authkey. 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 Authkey MCP in Pydantic AI

Use the MCPToolset class with your Vinkius HTTP endpoint URL. Pass the toolset object in the toolsets list when initializing your Agent.

The framework raises a validation error immediately. This prevents your agent from making decisions based on malformed or hallucinated API data.

Yes, the MCP toolset supports both Streamable HTTP and SSE transports, allowing you to connect to your externally hosted Vinkius server easily.

Absolutely. The framework is completely model-agnostic, meaning you can drive tools like `send_email` using local models or commercial APIs.

Communication payloads containing phone numbers and message content are processed inside secure, ephemeral V8 isolates. No personal data is ever stored or logged by Vinkius.

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