Pangram MCP Editorial Tools

An MCP (Model Context Protocol) server that integrates Pangram's AI attribution APIs into professional writing workflows.

Designed for editorial review, transparency, and quality assurance when AI-assisted tools are used in journalism, research, and enterprise content creation.

Use Cases

• Newsrooms & Publishers — Audit AI attribution before publication

• Academic Writers — Verify transparency requirements for submissions

• Enterprise Content Teams — QA workflows for AI-assisted documentation

• Legal & Compliance — Attribution audits for authored materials

Related MCP server: gptzero-mcp

Features

Quick Start

1. Install

git clone https://github.com/nicholasgriffintn/pangram-mcp-editorial-tools.git
cd pangram-mcp-editorial-tools
npm install
npm run build

2. Get Your Pangram API Key

1. Go to pangram.com

2. Log in to your dashboard

3. Click API in the header

4. Copy your API key

3. Configure Claude Desktop

Add to your Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "pangram-editorial": {
      "command": "node",
      "args": ["/absolute/path/to/pangram-mcp-editorial-tools/dist/index.js"],
      "env": {
        "PANGRAM_API_KEY": "your-api-key-here"
      }
    }
  }
}

4. Restart Claude Desktop

The Pangram editorial tools will now be available in all your conversations.

Usage Examples

Once connected, use naturally in Claude:

"Run an attribution audit on this article before I submit it"

"Quick transparency check on this draft"

"Analyze the authorship segments in my report"

Tools Reference

pangram_attribution_audit

Comprehensive attribution analysis providing:

• Overall authorship assessment

• Segment-by-segment attribution breakdown

• Confidence metrics per section

• Transparency report suitable for editorial review

Parameters:

• text (required): Content to analyze (minimum 50 words)

• response_format (optional): "markdown" (default) or "json"

pangram_quick_snapshot

Fast attribution check for iterative editorial workflows:

• Summary authorship indicator

• Attribution percentage

• Quick review status

Parameters:

• text (required): Content to analyze (minimum 50 words)

Requirements

• Node.js 18+

• Pangram API key (pangram.com)

• Claude Desktop or any MCP-compatible client

API Note

Pangram's API is priced separately from their web dashboard subscription. See pangram.com/solutions/api for details.

Development

npm install
npm run build
npm run dev  # watch mode

About

This project addresses the growing need for attribution transparency in professional writing workflows. As AI-assisted authorship becomes standard practice in journalism, research, and enterprise content, tools that provide clear attribution analysis support responsible disclosure and editorial integrity.

License

MIT

Contributing

Contributions welcome. Please ensure any additions maintain the project's focus on editorial transparency and professional quality assurance.