Cold Mail Agent MCP

Cold Mail Agent MCP

Provides context-aware cold outreach guidance for AI clients via a single tool, cold_email_guidance, helping draft personalized and effective emails.

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README

Cold Mail Agent MCP

<p align="center"> <img src="docs/images/mcp-icon.png" alt="Cold Mail Agent MCP envelope icon" width="112" /> </p>

<p align="center"> <strong>Context-aware cold outreach guidance for any MCP-compatible AI client.</strong> </p>

<p align="center"> <a href="#connect">Connect</a> · <a href="#how-it-works">How it works</a> · <a href="#develop">Develop</a> · <a href="docs/protocol.md">Protocol</a> </p>

Cold Mail Agent MCP gives an AI client a focused cold-outreach playbook: a writing engine, proven style patterns, voice options, use-case guidance, and a required humanizer pass. It is a public, read-only Cloudflare Worker that implements the Model Context Protocol (MCP) over Streamable HTTP.

<p align="center"> <img src="docs/images/mcp-workflow.png" alt="Minimalist diagram showing an AI client calling the Cold Mail Agent MCP endpoint, which loads public writing-playbook reference cards." width="900" /> </p>

What it does

The server exposes one tool, cold_email_guidance. Your MCP client calls it with the outreach task it is working on. The tool returns the Cold Mail Agent playbook and its public references so the client can draft, revise, and humanize a genuinely specific email.

The MCP server does not send email, access inboxes, store contacts, scrape recipients, or invent personal details. The connected AI client remains responsible for writing the final response and using any other tools.

Tool Input Returns
cold_email_guidance request string, 1–2,000 characters The request plus the complete public writing playbook

How it works

The examples below are fictional illustrations. They show the difference between generic cold outreach and an AI client that first loads the MCP's guidance. They are not claims about a specific model's output.

Without focused guidance

<p align="center"> <img src="docs/images/generic-outreach.png" alt="Illustrative minimalist client mockup representing a generic cold email with vague, ungrounded writing." width="760" /> </p>

A generic assistant often starts with vague praise, spends too long on the sender, and asks for a meeting before earning the recipient's attention.

With Cold Mail Agent MCP

<p align="center"> <img src="docs/images/mcp-tool-call.png" alt="Illustrative minimalist developer interface representing a successful call to cold_email_guidance." width="760" /> </p>

The client loads the playbook before it drafts. That guidance emphasizes a true personalization hook, a compact proof point, a low-friction ask, and a final humanizer pass.

<p align="center"> <img src="docs/images/mcp-outreach.png" alt="Illustrative minimalist AI writing interface representing a concise, specific cold outreach draft after MCP guidance." width="760" /> </p>

The resulting email is still the client's work, but it is calibrated against a clearer system: do the work first, keep the writing simple, and never fake a fact or personalization.

Connect

Use this public endpoint in an MCP client that supports Streamable HTTP:

https://cold-mail-agent-mcp.cold-mail-agent.workers.dev/mcp

The server does not require authentication. Your client must support MCP over HTTP and send standard JSON-RPC requests to /mcp.

{
  "mcpServers": {
    "cold-mail-agent": {
      "url": "https://cold-mail-agent-mcp.cold-mail-agent.workers.dev/mcp"
    }
  }
}

Client configuration formats vary. Use your client's documentation to add a remote MCP server, then use cold_email_guidance before drafting or revising an email to someone you do not already know.

Develop

You need Node.js 22 or later and a Cloudflare account for deployment. Install dependencies, start a local Worker, and connect your client to the local URL.

npm install
npm run dev

The local endpoint is http://localhost:8788/mcp.

Run the repository checks and a Worker bundle dry run before opening a pull request:

npm run check

For deployment steps and production verification, read the development guide.

Privacy and public content

This repository is public because the deployed Worker reads the approved playbook files from content/ at runtime. The public material includes only writing guidance and examples.

Do not add lead lists, contact data, private notes, API keys, client prompts, or credentials to this repository. Read the security policy before reporting a vulnerability or proposing a data-handling change.

Documentation

The documentation describes the server contract and operating model:

License

This project is available under the MIT License.

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