How to Use the Disposable Email Blocker MCP in Pydantic AI

Q: How does Disposable Email Blocker work with Pydantic AI?

It works as a validated toolset. After running checkdisposable, the resulting boolean or string is strictly checked against your Pydantic schema, guaranteeing data correctness for downstream tasks.

Guaranteed clean inputs. Validate email addresses before they break your models with Pydantic AI.

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Connect Disposable Email Blocker MCP to Pydantic AI

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Setup Disposable Email Blocker with Pydantic AI

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Schema-Validated Email Checks

The `check_disposable` tool takes the raw email and runs it through a validation check against known temporary domains. Because this MCP response is validated by Pydantic, your agent knows *exactly* what to expect: a clean boolean result. No silent data corruption.

Robust Agent Failures

If the API unexpectedly returned malformed data after running `check_disposable`, the entire process fails loudly with a validation error. This prevents your agent from proceeding using bad assumptions or hallucinated fields, giving you immediate visibility into the failure point.

Type-Safe Lead Filtering

Use `check_disposable` to get a validated type (a boolean) that determines if an email is usable. Your agent can then use this strict, typed output in subsequent logic, ensuring the rest of your pipeline only deals with confirmed, high-quality data.

Setup guide

Set up Disposable Email Blocker 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": {
        "disposable-email-blocker-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Disposable Email Blocker transactions")
print(result.output)

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Common questions about Disposable Email Blocker MCP in Pydantic AI

It works as a validated toolset. After running `check_disposable`, the resulting boolean or string is strictly checked against your Pydantic schema, guaranteeing data correctness for downstream tasks.

The tool only processes the full email address and its associated domain. The validation output is what your agent consumes, not the raw content of the message.

Yes. Because the MCP response must conform to a strict schema, you're protected from unexpected or malformed outputs that could compromise your data handling.

You pass this toolset into your Agent constructor. This makes the `check_disposable` function available for your agent to call, and Pydantic ensures the output is always predictable.

The added validation layer adds overhead, but it's a necessary cost. You trade marginal latency for absolute certainty that your data structures remain correct.

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