How to Use the Termii MCP in Pydantic AI

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Connect Termii MCP to Pydantic AI

Create your Vinkius account to connect Termii 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 Termii with Pydantic AI

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Claude

Perplexity

Type-Safe Communication Status Checks

Before sending anything, run `check_balance` to get the account balance. Because your agent relies on defined schemas, the response data is guaranteed to be structured and predictable. This eliminates guesswork. You're not dealing with vague API responses; you know exactly what fields—like remaining credit or currency type—you'll get back.

Sending Messages via MCP Server

Whether it’s a rich chat message using `send_whatsapp` or a quick alert via `send_sms`, the tool requires specific JSON payloads. Pydantic ensures that any outgoing data adheres strictly to your defined messaging model. This strict validation is what keeps your agent from hallucinating fields, which can ruin an entire campaign.

Managing and Verifying User Access

Use the `send_otp` tool for initial outreach. The payload must conform to a defined structure—say, requiring a recipient phone number and a length parameter. Then, when you call `verify_otp`, Pydantic validates that the incoming code matches the expected type, guaranteeing the security of the verification step.

Setup guide

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

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

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

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Common questions about Termii MCP in Pydantic AI

The core benefit is validation. Every time your agent calls a tool like `send_whatsapp`, Pydantic validates the API's response against a strict schema, so you never get corrupted data.

This server handles user identities and communication messages. All inputs and outputs related to OTP codes or sender IDs must fit the strict schemas, providing maximum data integrity.

You absolutely can. Since every tool call is validated at runtime, you can chain operations—like `list_sender_ids` followed by `send_sms`—with confidence that the data structure is correct each time.

It standardizes the inputs. Whether you use WhatsApp or SMS, the underlying framework requires structured JSON payloads for sending messages, keeping your entire communication system predictable.

Yes. It manages user identities and communication messages. By forcing validation on these types of data, the MCP Server helps ensure that only correctly structured and expected information is passed between systems.

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