Declawed: A Configurable, Promptable AI Mail Assisty Kitty

A local Model Context Protocol (MCP) server and LLM integration platform. Built to connect to LLM APIs - be itClaude Desktop, others... or locally hosted models. Executes your prompts to manage your mail, while you... watch Fellini films, solve climate change, sip Mai Tais, or.... whatever.

Ssssimple. Siamsese, if you please.... Eats the blue-plate-crustacean for breakfast.

Infinitely mod-able. Dead simple. Privacy centric.

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Scope

Current implementation contemplates: Claude Desktop + Custom, Local Model Context Protocol Server + Commercial SMTP Server (MX'config’d properly) + your DNS-config’d XXX.YYY

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1. Install: Node.js

Node.js v16 or higher must be installed.

node --version
npm --version

If not installed, download from nodejs.org.

2. Initialize the Project

mkdir assistant
cd assistant
npm init -y

3. Install Dependencies

npm install @modelcontextprotocol/sdk zod@3 googleapis
npm install -D @types/node typescript

Create the source directory and entry file:

mkdir src
touch src/index.ts

4. Configure the Project

4a. Update package.json

Set the module type, binary entry, and build scripts:

{
  "type": "module",
  "bin": {
    "assistant": "./build/index.js"
  },
  "scripts": {
    "build": "tsc && chmod 755 build/index.js",
    "auth": "npm run build && node build/auth.js"
  },
  "files": ["build"]
}

4b. Create tsconfig.json in the project root

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "Node16",
    "moduleResolution": "Node16",
    "outDir": "./build",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true,
    "skipLibCheck": true,
    "forceConsistentCasingInFileNames": true
  },
  "include": ["src/**/*"],
  "exclude": ["node_modules"]
}

5. Write the Server Code

The server source lives in src/index.ts. It registers three tools and one prompt with the MCP server:

• fetch_new_emails -- fetches unread Gmail messages
• delete_emails -- trashes messages by ID
• append_to_summary -- logs classified emails to summary.json
• review_emails (prompt) -- feeds Claude the classification instructions

The auth helper lives in src/auth.ts, used only for the one-time OAuth setup.

6. Set Up Google Cloud Credentials

6a. Create a Google Cloud Project

1. Go to Google Cloud Console
2. Sign in with the Google account that owns the target Gmail
3. Click the project dropdown (top-left) and select New Project
4. Name it (e.g., assistant-mcp) and click Create
5. Select the new project from the dropdown

6b. Enable the Gmail API

1. Go to APIs & Services > Library (direct link)
2. Search for Gmail API
3. Click it, then click Enable

6c. Configure the OAuth Consent Screen

1. Go to Google Auth Platform > Branding (or APIs & Services > OAuth consent screen)
2. Set user type to External, click Create
3. Fill in app name, support email, and developer contact email
4. Save and continue

6d. Add the Gmail Scope

1. Go to Google Auth Platform > Data Access (or the Scopes page)
2. Click Add or remove scopes
3. Add: https://www.googleapis.com/auth/gmail.modify
4. Save

6e. Add Yourself as a Test User

1. Go to Google Auth Platform > Audience
2. Add your Gmail address as a test user

6f. Create OAuth Client Credentials

1. Go to Google Auth Platform > Clients (or APIs & Services > Credentials)
2. Click Create Client (or + Create Credentials > OAuth client ID)
3. Application type: Desktop app
4. Name it anything (e.g., Assistant MCP Desktop)
5. Click Create
6. Download the JSON file
7. Rename it to credentials.json
8. Move it to the project root: /Users/kjannette/assistant/credentials.json

7. Authorize Your Gmail Account

Build the project and run the auth script:

npm run auth

This will:

1. Print a URL -- open it in your browser
2. Sign in with your Google account and click Allow
3. You'll land on a "localhost refused to connect" page (this is normal)
4. Copy the entire URL from the browser address bar
5. Paste it into the terminal prompt
6. The script extracts the auth code and saves token.json

You only need to do this once. The token auto-refreshes.

8. Build the Server

npm run build

This compiles src/*.ts into build/*.js.

9. Write the Classification Prompt

Create a file called classify-emails.txt in the project root. This file contains the plain-text instructions that tell Claude how to classify your emails.

Tips for writing the prompt:

• Use clear, explicit category definitions with example language for each
• Handle ambiguous cases (e.g., "If an email both acknowledges receipt AND requests action, classify it as B")
• Define the exact actions to take for each category (delete, summarize, etc.)
• Specify what fields to include in summaries
• Keep it in plain text -- no JSON or special formatting needed
• The file is loaded at runtime, so you can edit it without rebuilding the server

File location: Must be at the project root as classify-emails.txt.

10. Configure Claude Desktop

Edit the Claude Desktop config file:

code ~/Library/Application\ Support/Claude/claude_desktop_config.json

Add the assistant server to the mcpServers object:

{
  "mcpServers": {
    "assistant": {
      "command": "/ABSOLUTE/PATH/TO/node",
      "args": [
        "/Users/kjannette/assistant/build/index.js"
      ]
    }
  }
}

Replace /ABSOLUTE/PATH/TO/node with the output of which node.

11. Restart Claude Desktop

Fully quit Claude Desktop (Cmd+Q, not just close the window) and reopen it. The assistant server should now appear under Connectors in the chat input.

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Usage Guide

Prompt Loader

The server reads classify-emails.txt from the project root at runtime. To change classification behavior, edit that file directly -- no rebuild required. The updated instructions take effect on the next tool call.

MCP Prompt: review_emails

A registered MCP prompt available in Claude Desktop's Connectors menu. When invoked, it feeds Claude the full contents of classify-emails.txt as a user message, giving Claude all the classification criteria before it calls any tools. This is the recommended way to trigger the workflow -- it ensures Claude has the complete instructions every time.

fetch_new_emails Enrichment

Every time fetch_new_emails is called, the classification instructions from classify-emails.txt are appended to the response alongside the email data. This means Claude always sees the rules with the data, even if the review_emails prompt was not explicitly invoked. Belt and suspenders.

Running the Workflow

1. Open Claude Desktop
2. Type: "Review my inbox" (or invoke the review_emails prompt from Connectors)
3. Claude will:
   • Call fetch_new_emails to retrieve unread messages
   • Classify each email as A, B, C, or D using the prompt instructions
   • Call delete_emails for categories A and C
   • Call append_to_summary for categories B and D
4. Results are displayed in the chat and saved to summary.json

Key Commands

Command	Purpose
npm run build	Recompile after editing src/index.ts
npm run auth	Re-authorize Gmail (only if token.json deleted/expired)
Cmd+Q Claude Desktop, reopen	Pick up server changes after a rebuild

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Project Structure

assistant/
├── src/
│   ├── index.ts          # MCP server source (tools + prompt)
│   └── auth.ts           # One-time OAuth setup script
├── build/
│   ├── index.js          # Compiled server (Claude Desktop runs this)
│   └── auth.js           # Compiled auth script
├── classify-emails.txt   # Classification prompt (plain text, edit anytime)
├── credentials.json      # Google OAuth client credentials (from Cloud Console)
├── token.json            # Gmail access/refresh token (auto-generated)
├── summary.json          # Output file where B/D emails are logged
├── package.json          # Project config and scripts
├── tsconfig.json         # TypeScript compiler config
└── README.md             # This file