How to Use the Mailsoftly MCP in Pydantic AI

Q: How do I configure Mailsoftly with Pydantic AI?

Install pydantic-ai-slimmcp. Create an MCPToolset pointing to your Vinkius HTTP URL. Pass this toolset directly to your Agent constructor to connect the Mailsoftly MCP server.

Q: Does Pydantic AI catch API errors from Mailsoftly?

It catches everything. If listsubscribercollections returns an unexpected JSON structure, Pydantic throws a validation error immediately. Your agent will not proceed with broken data.

Q: How does the agent handle contact list IDs?

The agent first runs listsubscribercollections to fetch valid IDs. Pydantic validates these IDs, ensuring the agent uses exact matches when calling addcontacttolist.

Type-safe email automation with Pydantic AI. Validate every contact and campaign payload via this MCP Server before execution.

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Connect Mailsoftly MCP to Pydantic AI

Create your Vinkius account to connect Mailsoftly 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 Mailsoftly with Pydantic AI

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Perplexity

Strict schema validation for Mailsoftly MCP Server

Bad data corrupts contact lists fast. When you use Pydantic AI, every response from the REST API gets validated against strict Pydantic models at runtime. If the `get_campaign_details` tool returns a missing field or a malformed date string, the framework fails loudly. You get a clear validation error instead of silent corruption in your database. This matters when you are automating email operations. You know exactly what the API returned, and your agent only acts on structurally perfect data.

Type-safe contact creation and list management

Adding users to the wrong list causes immediate bounce rate spikes. Your agent uses `create_base_contact` to inject the email, then immediately follows up with `add_contact_to_list`. Pydantic AI ensures the contact list ID matches the exact required integer or string type before the request even fires. You stop worrying about LLM hallucinations inserting garbage data into your CRM. If the agent tries to pass a malformed email address, the framework catches the type mismatch and halts the operation.

Reliable campaign tracking across any LLM

Because Pydantic AI is model-agnostic, you can swap between Anthropic, OpenAI, or local models without rewriting your integration. Your agent pulls engagement metrics using `get_campaign_click_data` and `list_sent_history`. The framework parses those metrics into predictable Python objects. You can build reliable reporting dashboards that trust the underlying data structure, knowing Pydantic already stripped out any unexpected text the model tried to append.

Setup guide

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

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

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

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Common questions about Mailsoftly MCP in Pydantic AI

Install pydantic-ai-slim[mcp]. Create an MCPToolset pointing to your Vinkius HTTP URL. Pass this toolset directly to your Agent constructor to connect the Mailsoftly MCP server.

It catches everything. If `list_subscriber_collections` returns an unexpected JSON structure, Pydantic throws a validation error immediately. Your agent will not proceed with broken data.

Yes. The framework is completely model-agnostic. As long as the model supports tool calling, it can run an MCP server to execute `send_campaign_to_all` or read campaign metrics.

The agent first runs `list_subscriber_collections` to fetch valid IDs. Pydantic validates these IDs, ensuring the agent uses exact matches when calling `add_contact_to_list`.

We handle the auth layer entirely. You use one Vinkius endpoint token. Your click rates, email lists, and campaign histories process through an ephemeral sandbox that destroys itself after the run. We store absolutely nothing.

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