How do I find my NumVerify Access Key?

Log in to your [**NumVerify dashboard**](https://numverify.com/dashboard), and you will find your Access Key on the main page. Copy and paste it below.

Does it support international numbers?

Yes. NumVerify covers over 232 countries and provides detailed validation and metadata for global phone numbers.

Can the agent identify if a number is mobile or landline?

Yes. The `get_phone_line_type` tool retrieves the line type (e.g., 'mobile', 'landline') allowing you to audit the nature of your contact data.

NumVerify MCP Server for Pydantic AI 4 tools — connect in under 2 minutes

Name: NumVerify
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 4 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect NumVerify through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to NumVerify "
            "(4 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in NumVerify?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About NumVerify MCP Server

Empower your AI agent to orchestrate your entire phone validation and identity verification workflow with NumVerify, the global API for phone number intelligence. By connecting NumVerify to your agent, you transform complex validation tasks into a natural conversation. Your agent can instantly verify if a number is valid, audit carrier information, and retrieve geographic location data without you ever touching a manual lookup tool. Whether you are cleaning lead lists or verifying user identity, your agent acts as a real-time communications analyst, ensuring your phone data is always verified and accurate.

Pydantic AI validates every NumVerify tool response against typed schemas, catching data inconsistencies at build time. Connect 4 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Phone Auditing — Verify if any international phone number is valid and retrieve detailed metadata, including country and dial codes.
Carrier Oversight — Identify the current carrier for a phone number to maintain a clear view of network distribution.
Location Discovery — Retrieve the geographic location (city/region) associated with a phone number instantly.
Line-type Intelligence — Identify if a number is a mobile, landline, or VoIP line to optimize your communication strategy.
Metadata Integrity — Retrieve official country names and formatting details to maintain strict organizational control over your contact data.

The NumVerify MCP Server exposes 4 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect NumVerify to Pydantic AI via MCP

Follow these steps to integrate the NumVerify MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 4 tools from NumVerify with type-safe schemas

Why Use Pydantic AI with the NumVerify MCP Server

Pydantic AI provides unique advantages when paired with NumVerify through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your NumVerify integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your NumVerify connection logic from agent behavior for testable, maintainable code

NumVerify + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the NumVerify MCP Server delivers measurable value.

Type-safe data pipelines: query NumVerify with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple NumVerify tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query NumVerify and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock NumVerify responses and write comprehensive agent tests

NumVerify MCP Tools for Pydantic AI (4)

These 4 tools become available when you connect NumVerify to Pydantic AI via MCP:

get_phone_carrier

Get carrier information for a phone number

get_phone_line_type

Identify if a phone number is mobile, landline, or other

get_phone_location

Get geographic location details for a phone number

validate_phone

Verify if a phone number is valid and retrieve metadata

Example Prompts for NumVerify in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with NumVerify immediately.

"Validate the phone number +14158586273 using NumVerify."

"Identify the carrier for +442071838750."

"Check if +5511999999999 is a mobile line."

Troubleshooting NumVerify MCP Server with Pydantic AI

Common issues when connecting NumVerify to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

NumVerify + Pydantic AI FAQ

Common questions about integrating NumVerify MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your NumVerify MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.