Fastmail MCP Server

Fastmail MCP Server

Enables reading and searching Fastmail inbox emails, including listing inbox emails, querying by keyword, and retrieving email content via JMAP.

Category
访问服务器

README

Fastmail MCP Server

2026-04-23: Fastmail now has an official MCP server!

A basic MCP server that provides access to a Fastmail inbox, built with FastMCP and jmapc.

Prerequisites

  • Python 3.12+. The project has been developed with Python 3.12.
  • Environment variables
    • BEARER_TOKEN: A static token required to authorize HTTP requests to the server.
    • LOG_LEVEL (optional): Python logging level for server output. Defaults to INFO.

Along with the bearer token, a Fastmail API token must also be provided by MCP clients. See Fastmail's API documentation for instructions on creating a token (Settings -> Privacy & Security -> Connected apps & API tokens).

Installation

Clone the repo, then install the dependencies in a virtual environment:

git clone https://github.com/jeffjjohnston/fastmail-mcp-server.git
cd fastmail-mcp-server
python3.12 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

Tools

The server implements the following tools:

  • list_inbox_emails: Lists the emails in the Inbox (id, sender, subject, and date). Accepts an offset for pagination.
  • query_emails_by_keyword: Searches for a keyword in the subject or body of emails while ignoring junk and deleted messages. Returns the total matches and a page of results, with an optional offset parameter.
  • get_email_content: Retrieves the content of an email given an id. HTML content is converted to text using BeautifulSoup4.

Running the server

Export a bearer token and start the server:

export BEARER_TOKEN="my-secret-token"
export LOG_LEVEL="DEBUG"  # optional
python server.py

By default the server listens on http://127.0.0.1:8000/mcp/.

Testing

Run the test suite with pytest:

pytest

Example client usage: FastMCP client

You can interact with the server using the fastmcp client. The example below calls the list_inbox_emails tool:

import asyncio
from fastmcp.client.transports import StreamableHttpTransport
from fastmcp import Client

BEARER_TOKEN = "my-secret-token"
FASTMAIL_API_TOKEN = "<FASTMAIL_API_TOKEN>"

async def main():
    transport = StreamableHttpTransport(
        "http://127.0.0.1:8000/mcp/",
        headers={"fastmail-api-token": FASTMAIL_API_TOKEN},
        auth=f"Bearer {BEARER_TOKEN}",
    )
    client = Client(transport)
    async with client:
        result = await client.call_tool("list_inbox_emails")
        print(result)

asyncio.run(main())

Replace <FASTMAIL_API_TOKEN> with your personal Fastmail API token.

Example client usage: OpenAI

Your server needs to be accessible from the Internet to use it with OpenAI's Remote MCP capabilities. A quick way to enable this for testing is to use Cloudflare's cloudflared tool to build a tunnel.

# on Mac a with homebrew, use `brew install cloudflared`
cloudflared tunnel --url http://127.0.0.1:8000

You will get back an HTTPS URL endpoint and can use it as the MCP server in an OpenAI API request (with /mcp/ appended):

from openai import OpenAI

BEARER_TOKEN = "my-secret-token"
FASTMAIL_API_TOKEN = "<FASTMAIL_API_TOKEN>"

# an OPENAI_API_KEY environment variable is required
client = OpenAI()

resp = client.responses.create(
    model="gpt-4o-mini",
    tools=[
        {
            "type": "mcp",
            "server_label": "Email",
            "server_url": "https://random-words-generated-here.trycloudflare.com/mcp/",
            "require_approval": "never",
            "headers": {
                "Authorization": f"Bearer {BEARER_TOKEN}",
                "fastmail-api-token": FASTMAIL_API_TOKEN,
            },
        },
    ],
    input="Summarize the newest message in my inbox.",
    instructions="Respond without formatting.",
)

print(resp.output_text)

Fastmail Chat

A small web app, Fastmail Chat, provides a chat interface built on OpenAI's remote MCP support and this MCP server.

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选