How to Use the Mailsac MCP in Pydantic AI

Q: How do I perform a search in Mailsac with Pydantic AI?

Invoke searchaccountmessages within your agent toolset. The results will be validated against your Pydantic schemas, ensuring you only receive data that matches your expected format.

Q: Can I list reserved addresses in Pydantic AI?

Yes, use listreservedaddresses to see your currently active test emails. This helps you manage your quota and keep your test suite organized.

Q: How do I ensure data privacy for Mailsac emails?

We enforce privacy by allowing you to manually trigger deleteinboxmessage. Once your Pydantic AI agent confirms the email data is no longer needed, it deletes the message from the server storage.

Validate email workflows in Pydantic AI. Get runtime type checking for every Mailsac response.

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Connect Mailsac MCP to Pydantic AI

Create your Vinkius account to connect Mailsac 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 Mailsac with Pydantic AI

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Type-safe email parsing for Pydantic AI

Every message retrieved via `get_html_message_body` or `get_plain_text_body` is ready for your Pydantic schemas. If the API returns unexpected content, your agent catches the validation error immediately. This prevents your agent from acting on malformed email data. You define the structure, and the tool ensures the incoming message conforms to your requirements.

Strict inbox management in Pydantic AI

Use `list_inbox_messages` to fetch current mail and map it directly to your Python models. Your agent ensures that only valid, expected messages are processed during the testing phase. This reduces silent failures. You get a clear, typed response that defines exactly what arrived in your inbox.

Deterministic testing with Pydantic AI

Reserve specific email addresses with `reserve_new_address` to keep your test data predictable. Your Pydantic AI agent handles the reservation and release cycle programmatically. This gives you total control over the test environment. You can verify the entire email flow without worrying about stale data or overlapping test cases.

Setup guide

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

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

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

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Common questions about Mailsac MCP in Pydantic AI

Invoke `search_account_messages` within your agent toolset. The results will be validated against your Pydantic schemas, ensuring you only receive data that matches your expected format.

Since you are using Pydantic models for responses, any tool error or unexpected API output will raise a validation exception. Your agent can then handle this error gracefully.

Yes, use `list_reserved_addresses` to see your currently active test emails. This helps you manage your quota and keep your test suite organized.

We enforce privacy by allowing you to manually trigger `delete_inbox_message`. Once your Pydantic AI agent confirms the email data is no longer needed, it deletes the message from the server storage.

Your Pydantic model will fail validation if you expect data. This is intended behavior, as it forces your agent to address missing or empty emails before continuing the test.

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