Facebook MCP Server

This project is a MCP server for automating and managing interactions on a Facebook Page using the Facebook Graph API. It exposes tools to create posts, moderate comments, fetch post insights, and filter negative feedback — ready to plug into Claude, or other LLM-based agents.

🤖 What Is This?

This MCP provides a suite of AI-callable tools that connect directly to a Facebook Page, abstracting common API operations as LLM-friendly functions.

✅ Benefits

Empowers social media managers to automate moderation and analytics.
Seamlessly integrates with Claude Desktop or any Agent client.
Enables fine-grained control over Facebook content from natural language.

📦 Features

Tool	Description
`post_to_facebook`	Create a new Facebook post with a message.
`reply_to_comment`	Reply to a specific comment on a post.
`get_page_posts`	Retrieve recent posts from the Page.
`get_post_comments`	Fetch comments on a given post.
`delete_post`	Delete a specific post by ID.
`delete_comment`	Delete a specific comment by ID.
`hide_comment`	Hide a comment from public view.
`unhide_comment`	Unhide a previously hidden comment.
`delete_comment_from_post`	Alias for deleting a comment from a specific post.
`filter_negative_comments`	Filter out comments with negative sentiment keywords.
`get_number_of_comments`	Count the number of comments on a post.
`get_number_of_likes`	Count the number of likes on a post.
`get_post_impressions`	Get total impressions on a post.
`get_post_impressions_unique`	Get number of unique users who saw the post.
`get_post_impressions_paid`	Get number of paid impressions on the post.
`get_post_impressions_organic`	Get number of organic impressions on the post.
`get_post_engaged_users`	Get number of users who engaged with the post.
`get_post_clicks`	Get number of clicks on the post.
`get_post_reactions_like_total`	Get total number of 'Like' reactions.
`get_post_top_commenters`	Get the top commenters on a post.
`post_image_to_facebook`	Post an image with a caption to the Facebook page.
`post_video_to_facebook`	Post a local video file path or publicly accessible video URL with a description.
`send_dm_to_user`	Send a direct message to a user.
`update_post`	Updates an existing post's message.
`schedule_post`	Schedule a post for future publication.
`get_page_fan_count`	Retrieve the total number of Page fans.
`get_post_share_count`	Get the number of shares on a post.
`get_post_reactions_breakdown`	Get all reaction counts for a post in one call.
`bulk_delete_comments`	Delete multiple comments by ID.
`bulk_hide_comments`	Hide multiple comments by ID.
`bulk_unhide_comments`	Unhide multiple comments by ID.
`get_comment_replies`	Get all replies to a specific comment.
`get_post_permalink`	Get the permalink URL of a post.
`get_scheduled_posts`	List all scheduled (unpublished) posts on the Page.
`get_page_info`	Get extended Page details (name, about, category, website).

🚀 Setup & Installation

1. Clone the Repository

git clone https://github.com/your-org/facebook-mcp-server.git
cd facebook-mcp-server

2. 🛠️ Installation

Install dependencies using uv, a fast Python package manager: If uv is not already installed, run:

curl -Ls https://astral.sh/uv/install.sh | bash

Once uv is installed, install the project dependencies:

uv pip install -r requirements.txt

3. Set Up Environment

Create a .env file in the root directory and add your Facebook Page credentials. You can obtain these from https://developers.facebook.com/tools/explorer

FACEBOOK_ACCESS_TOKEN=your_facebook_page_access_token
FACEBOOK_PAGE_ID=your_page_id

🧩 Using with Claude Desktop

To set up the FacebookMCP in Clade:

Open Clade.
Go to Settings → Developer → Edit Config.
In the config file that opens, add the following entry:

"FacebookMCP": {
  "command": "uv",
  "args": [
    "run",
    "--with",
    "mcp[cli]",
    "--with",
    "requests",
    "mcp",
    "run",
    "/path/to/facebook-mcp-server/server.py"
  ]
}

✅ You’re Ready to Go!

That’s it — your Facebook MCP server is now fully configured and ready to power Claude Desktop. You can now post, moderate, and measure engagement all through natural language prompts!

🤝 Contributing

Contributions, issues, and feature requests are welcome!
Feel free to fork the repo and submit a pull request.

Create a branch: git checkout -b feature/YourFeature
Commit your changes: git commit -m 'feat: add new feature'
Push to the branch: git push origin feature/YourFeature
Open a pull request 🎉