Provides integration with Instagram's Graph API, enabling AI agents to manage content, analyze post and account performance, research competitors, handle comments, and manage publishing workflows.
Provides integration with Meta's APIs, using Meta App credentials and the Meta Graph API to access Instagram Business accounts and related data.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Instagram MCP Server What were my best performing Reels last month?"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A production-ready Model Context Protocol (MCP) server for interacting with Instagram/Meta APIs. Enables AI agents to manage content, analyze performance, research competitors, and handle publishing workflows through structured MCP tools.
Account Management: Profile retrieval, account insights, media listing
Content Analytics: Post, Reel, and Story insights with engagement metrics
Content Generation: Reel ideas, captions, hashtags, content calendars
Competitor Research: Profile analysis, content comparison, competitor reports
Comment Management: Classification, moderation, auto-reply workflows
Publishing Workflow: Container creation, media publishing, status tracking
Lead Scoring: Business lead analysis with scoring tiers
Audit Logging: Full request/response logging with execution metrics
Related MCP server: Instagram Control MCP Server
Python 3.10+
Meta Developer App with Instagram Graph API access
Instagram Business Account connected to your Meta App
git clone <repository-url>
cd instagram-mcp
pip install -r requirements.txtcp .env.example .envEdit .env with your credentials:
META_APP_ID=your_meta_app_id
META_APP_SECRET=your_meta_app_secret
INSTAGRAM_ACCESS_TOKEN=your_instagram_access_token
INSTAGRAM_BUSINESS_ACCOUNT_ID=your_instagram_business_account_idpython server.pydocker-compose up --buildAdd to your MCP client configuration (e.g., Claude Desktop, Cursor):
{
"mcpServers": {
"instagram": {
"command": "python",
"args": ["server.py"],
"cwd": "/path/to/instagram-mcp"
}
}
}Tool | Description |
| Get profile information |
| Get account-level analytics |
| List media posts |
| Get detailed post info |
Tool | Description |
| Post performance metrics |
| Reel performance metrics |
| Story performance metrics |
| Top-performing content |
| Period-over-period comparison |
Tool | Description |
| Analyze post structure |
| Generate Reel concepts |
| Generate captions with hashtags |
| Generate relevant hashtags |
| Create multi-day calendars |
| Identify content patterns |
Tool | Description |
| Analyze competitor profile |
| Compare multiple accounts |
| Find top competitor content |
| Full competitor report |
Tool | Description |
| Get and classify comments |
| Reply to a comment |
| Moderate with classification |
Tool | Description |
| Create publish container |
| Publish to Instagram |
| Check publish status |
Tool | Description |
| Score and analyze leads |
"What were my best performing Reels last month?"
→ instagram_get_reel_insights (multiple calls) → sort → analyze → top results
"Generate a 30-day content calendar for an AI automation agency"
→ instagram_generate_content_calendar
"Analyze my competitor @examplebrand"
→ instagram_analyze_competitor → instagram_find_competitor_content
"What comments need moderation on my latest post?"
→ instagram_get_comments → classify → filter
"Compare my engagement with @competitor1 and @competitor2"
→ instagram_compare_accountsThe server implements an internal decision pipeline:
User Request - Natural language input
Intent Detection - Determine required operation
Permission Check - Validate API access
Tool Selection - Map to appropriate MCP tool
Instagram API - Execute authorized API call
Data Validation - Validate response data
Analytics/Reasoning - Process and analyze results
Structured Response - Return formatted JSON
Comments are automatically classified into:
Category | Description |
| Praise, compliments, positive feedback |
| Complaints, criticism, negative sentiment |
| Questions, inquiries |
| Purchase intent, business inquiries |
| Promotional spam, scam content |
| Support requests, issue reports |
| Unclassified comments |
Score Range | Tier |
81-100 | Very High |
61-80 | High |
31-60 | Medium |
0-30 | Low |
Scoring factors: follower count, posting frequency, business category, identified needs, website presence.
All tools return structured error responses:
{
"success": false,
"error": {
"code": "RATE_LIMIT",
"message": "Instagram API rate limit reached.",
"retryable": true
}
}Error codes: UNAUTHORIZED, FORBIDDEN, NOT_FOUND, RATE_LIMIT, SERVER_ERROR, NETWORK_ERROR, INVALID_MEDIA, PERMISSION_MISSING
instagram-mcp/
├── server.py # Main MCP server with tool definitions
├── config.py # Configuration management
├── requirements.txt # Python dependencies
├── .env.example # Environment template
├── Dockerfile # Container configuration
├── docker-compose.yml # Docker Compose setup
├── mcp/
│ ├── tools/ # MCP tool implementations
│ │ ├── account.py # Account tools
│ │ ├── analytics.py # Analytics tools
│ │ ├── content.py # Content generation tools
│ │ ├── comments.py # Comment management tools
│ │ ├── publishing.py # Publishing workflow tools
│ │ └── competitors.py # Competitor research tools
│ └── schemas/ # Pydantic data models
│ ├── account.py
│ ├── content.py
│ └── analytics.py
├── services/ # Business logic layer
│ ├── instagram_api.py # Instagram Graph API client
│ ├── analytics.py # Analytics calculations
│ ├── content_ai.py # Content generation engine
│ └── lead_scoring.py # Lead analysis and scoring
├── database/ # Data persistence
│ ├── models.py # SQLAlchemy models
│ └── repository.py # Database operations
└── tests/ # Unit tests
├── test_account.py
├── test_analytics.py
├── test_publishing.py
└── test_errors.pypytest tests/ -vOAuth 2.0 authentication via Meta Graph API
Access tokens stored in environment variables
No credential scraping or session extraction
Permission validation before operations
Rate-limit handling with retry logic
Audit logging for all tool executions
Never exposes tokens or secrets in responses
MIT
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