Instagram Analytics Mcp

Abstract

1. Introduction

A Model Context Protocol (MCP) server for Instagram and Facebook analytics via the Meta Graph API.

2. Objective

A Model Context Protocol (MCP) server that provides access to Instagram analytics and insights using the Instagram Graph API. This server allows you to retrieve account-level insights, media insights, and profile information programmatically.

This project demonstrates how TypeScript can be applied to a real-world Digital Marketing & SEO problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Node.js (LTS) and npm / yarn / pnpm
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/BilalTariq01/instagram-analytics-mcp.git
cd instagram-analytics-mcp

1. Install and build
2. Get an access token from the Graph API Explorer with permissions:
3. Instagram: instagram_basic, instagram_manage_insights, pages_read_engagement
4. Facebook: read_insights, pages_read_engagement
5. Add to your MCP client (see configuration below)

git clone <repository-url>
   cd social-analytics-mcp
   npm install
   npm run build

import { InstagramClient, FacebookClient, createServer } from 'social-analytics-mcp';

// Use clients directly
const instagram = new InstagramClient({
  accessToken: 'your_token',
  accountId: 'optional_account_id',
});
const profile = await instagram.getUserProfile();

// Or create an MCP server programmatically
const server = createServer();

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Export reports to Google Sheets or PDF
• Schedule weekly automated reports
• Add competitor comparison

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which marketing or SEO problem does this tool solve?
2. Which data sources or APIs does it use (Search Console, Analytics, social platforms)?
3. Which metrics or KPIs does it report and how are they calculated?
4. How could the output help a business make decisions?
5. How would you schedule it to run automatically?

9. Source Code & License

This project is developed by BilalTariq01 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.