How to Use the Mailshake MCP in Pydantic AI

Q: How do I configure the Mailshake MCP Server in Pydantic AI?

Install the slim package with MCP support. Initialize MCPToolset with your HTTP endpoint URL, then pass it to your Agent's toolsets array.

Type-safe Mailshake MCP Server execution for Pydantic AI agents.

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Connect Mailshake MCP to Pydantic AI

Create your Vinkius account to connect Mailshake 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 Mailshake with Pydantic AI

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Claude

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Type-Safe Mailshake MCP Server Actions

The `list_outreach_campaigns` and `get_campaign_details` tools return exact sequence metrics to your Python application. Pydantic AI intercepts these responses and validates them against strict data models at runtime. If you need to halt outreach, the agent calls `pause_outreach_campaign`. Because the framework enforces strict typing, the agent cannot pass malformed campaign IDs or hallucinated parameters to the execution endpoint.

Add and Verify Campaign Recipients

The `add_prospects_to_campaign` tool expects a highly structured JSON body containing contact details. Your Pydantic AI agent guarantees the schema matches exactly before the network request ever leaves your server. After insertion, the agent runs `list_campaign_recipients` to confirm the additions. If the API returns unexpected nested objects, the framework fails loudly with a validation error instead of silently passing bad data downstream.

Extract Lead History and Email Copy

The `get_lead_history` and `list_campaign_leads` tools surface engagement events for your active contacts. Your agent parses these events into typed Python objects for further processing. To audit the actual copy sent, the agent triggers `list_outreach_messages` followed by `get_message_content`. You get guaranteed string outputs that your model-agnostic setup can pass to OpenAI, Claude, or local LLMs.

Setup guide

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

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

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

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Common questions about Mailshake MCP in Pydantic AI

Install the slim package with MCP support. Initialize `MCPToolset` with your HTTP endpoint URL, then pass it to your Agent's `toolsets` array.

Standard requests leave you vulnerable to silent API changes. Pydantic AI validates every response from tools like `get_campaign_details`, failing loudly if the data structure shifts.

No. The server provides tools to read data and manage prospects, but it does not expose an endpoint for sending one-off emails. It manages existing campaigns.

The tool execution will return an error status. Your agent code must catch this exception and implement backoff logic before retrying the tool call.

The server touches personally identifiable information like prospect names, email addresses, and the raw text of sent messages. Vinkius runs the server in an ephemeral sandbox, ensuring no PII persists on the host machine after the tool executes.

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