How to Use the No2Bounce MCP in Pydantic AI

Q: How do I initialize the No2Bounce MCP Server with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your Vinkius HTTP endpoint, and pass it in the toolsets array when building your agent.

Type-safe email validation for Pydantic AI using the No2Bounce MCP Server.

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Setup No2Bounce with Pydantic AI

Ask AI about this MCP

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Claude

Perplexity

Strictly typed bulk validation

The `validate_bulk` tool accepts an array of strings and returns a tracking ID, and Pydantic AI guarantees the agent never passes malformed data. If the model tries to send an object instead of a list of emails, Pydantic catches it before the request hits the network. Strict validation prevents silent failures. You know exactly what goes into the MCP Server, and the agent gets a loud validation error if it hallucinates the payload structure.

Predictable status responses

Using the `get_validation_status` tool, your agent retrieves the final bounce and deliverability metrics for your list. Pydantic AI checks the incoming JSON against your schemas, so every field matches your expectations. If the API changes or the agent misinterprets the response, the framework throws an error. You never end up with corrupted data slipping into your production database.

Swap models, keep the tools

You can run both the `validate_bulk` and `get_validation_status` tools with OpenAI today and switch to a local model tomorrow. Pydantic AI abstracts the LLM, but the tool execution remains identical. You just pass the MCPToolset to your agent. The framework handles the underlying transports, so your email cleaning pipeline stays intact regardless of which model writes the code.

Setup guide

Set up No2Bounce 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": {
        "no2bounce-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to No2Bounce tools.",
)

result = await agent.run("List recent No2Bounce transactions")
print(result.output)

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Common questions about No2Bounce MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Create an MCPToolset pointing to your Vinkius HTTP endpoint, and pass it in the toolsets array when building your agent.

Yes. The agent receives the tracking ID from the first tool and knows it must pass that exact string to the status tool. Pydantic guarantees the ID format is correct.

Pydantic AI will throw a validation error. Your agent fails loudly instead of silently processing bad data, protecting your downstream systems from corrupted email lists.

No. That class is deprecated in Pydantic AI. You must use the unified MCPToolset approach to connect to the MCP Server.

The tool only processes the raw email addresses you provide. The Vinkius infrastructure runs the validation inside a strict V8 Isolate Sandbox, checking the domains and instantly wiping the memory. Nothing persists.

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