How do I find my Missive Access Token?

Go to Missive Settings > API and generate a new Bearer token.

Do I need a specific Missive plan to use the API?

Yes, API access typically requires a Productive or Business plan.

Can I perform actions like closing a conversation?

Yes, use the 'create_post' tool and set the 'close' parameter to true.

Missive MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Missive
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Missive 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 Missive "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Missive?"
    )
    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 Missive MCP Server

Connect Missive to your AI agent and manage your team's communication efficiently. Access conversations, messages, and contacts through natural conversation to stay organized and responsive.

Pydantic AI validates every Missive tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Conversation Management — List and view conversations from any mailbox (inbox, assigned, closed).
Collaboration — Post internal comments or trigger actions like closing and assigning conversations.
Message Access — Read all messages and comments within a specific conversation thread.
Contact Organization — Search for and create contacts in your shared or private contact books.
Draft & Send — Create email drafts and deliver them directly from your AI agent.

The Missive MCP Server exposes 10 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 Missive to Pydantic AI via MCP

Follow these steps to integrate the Missive 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 10 tools from Missive with type-safe schemas

Why Use Pydantic AI with the Missive MCP Server

Pydantic AI provides unique advantages when paired with Missive 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 Missive integration code

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

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

Missive + Pydantic AI Use Cases

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

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

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

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

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

Missive MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Missive to Pydantic AI via MCP:

create_contact

Create a new contact

create_draft

Create an email draft

create_post

Can also trigger actions like closing, assigning, or labeling. Create a post (comment or action) in a conversation

get_conversation

Get details for a specific conversation

get_me

Get current Missive user details

list_contacts

List Missive contacts

list_conversations

A mailbox filter is required (e.g., "inbox", "all", "assigned", "closed"). List conversations from a specific mailbox

list_labels

List Missive labels

list_messages

List messages in a conversation

send_draft

Send a prepared draft

Example Prompts for Missive in Pydantic AI

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

"List my recent conversations in the inbox."

"Add a comment 'Working on this now' to conversation id 123."

"Find contact info for 'Jane Smith'."

Troubleshooting Missive MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Missive + Pydantic AI FAQ

Common questions about integrating Missive 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 Missive MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.