How do I find my Mailshake API Key?

Log in to Mailshake, navigate to 'Extensions' in the main menu, and select 'API' to generate or copy your personal API key.

What is the difference between a Lead and a Recipient?

A Recipient is anyone added to a campaign. A Lead is a recipient who has met specific engagement criteria (like replying) and is tracked in the 'Lead Catcher'.

Can I add multiple prospects at once?

Yes, the `add_prospects_to_campaign` tool accepts a JSON body where you can provide a list of multiple recipients to be added in a single request.

Mailshake MCP Server for Pydantic AI 9 tools — connect in under 2 minutes

Name: Mailshake
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 9 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mailshake through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Mailshake "
            "(9 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Mailshake?"
    )
    print(result.data)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Mailshake MCP Server

Connect your Mailshake account to any AI agent to automate your cold outreach and sales engagement workflows. This MCP server enables your agent to manage campaigns, promote prospects to lead status, and track email interactions directly from natural language interfaces.

Pydantic AI validates every Mailshake tool response against typed schemas, catching data inconsistencies at build time. Connect 9 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Campaign Management — List all outreach campaigns, retrieve detailed sequences, and pause or unpause sending
Lead Tracking — Monitor qualified leads, retrieve interaction histories, and update prospect statuses
Prospect Ingestion — Programmatically add new recipients to existing campaigns to keep your pipeline full
Message Insights — List sent and received messages and retrieve full content for automated sentiment analysis or follow-up planning
Audience Oversight — List all recipients in a campaign and monitor their individual engagement stages (Sent, Opened, Replied)

The Mailshake MCP Server exposes 9 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Mailshake to Pydantic AI via MCP

Follow these steps to integrate the Mailshake MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 9 tools from Mailshake with type-safe schemas

Why Use Pydantic AI with the Mailshake MCP Server

Pydantic AI provides unique advantages when paired with Mailshake through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Mailshake integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Mailshake connection logic from agent behavior for testable, maintainable code

Mailshake + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Mailshake MCP Server delivers measurable value.

Type-safe data pipelines: query Mailshake with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Mailshake tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Mailshake and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Mailshake responses and write comprehensive agent tests

Mailshake MCP Tools for Pydantic AI (9)

These 9 tools become available when you connect Mailshake to Pydantic AI via MCP:

add_prospects_to_campaign

Requires a JSON body with recipient details. Add new prospects to an existing campaign

get_campaign_details

Get details for a specific campaign

get_lead_history

Get history for a specific lead

get_message_content

Get content for a specific email message

list_campaign_leads

List qualified leads

list_campaign_recipients

List all recipients in a campaign

list_outreach_campaigns

List all outreach campaigns

list_outreach_messages

List sent and received messages

pause_outreach_campaign

Pause a running campaign

Example Prompts for Mailshake in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Mailshake immediately.

"List all my active outreach campaigns in Mailshake."

"Show recent leads for the 'Partnership' campaign."

"Pause the campaign with ID '12345'."

Troubleshooting Mailshake MCP Server with Pydantic AI

Common issues when connecting Mailshake to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Mailshake + Pydantic AI FAQ

Common questions about integrating Mailshake MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Mailshake MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.