Facebook MCP Server

Community

HagaiHen

Facebook MCP server for automating posts, comment moderation, insights, and sentiment filtering.

Publisher	HagaiHen
Repository	`facebook-mcp-server`
Language	Python
Forks	78
Stars	225
Available tools	0
Transport type	stdio
Categories	Communication
License	MIT
Links	GitHub

Connect tools to AI workflows
Facebook MCP Server exposes MCP capabilities that can be used by compatible AI clients and agents.
0 available tools
Browse the callable actions below, including names and descriptions when provided by the server.
Ready-to-copy setup
Use the installation snippets to configure this server in your preferred MCP client.
Open source signals
225 stars and 78 forks from the linked repository.

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.
`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

bash
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:

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

Once uv is installed, install the project dependencies:

bash
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

bash
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:

bash
"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 🎉

Installation

TypingMind

{
  "mcpServers": {
    "facebook-mcp-server": {
      "command": "uv",
      "args": [
        "run",
        "--directory",
        "/absolute/path/to/facebook-mcp-server",
        "facebook-mcp-server"
      ]
    }
  }
}

Use Facebook MCP Server MCP with multiple AI models

TypingMind connects MCP tools at the workspace level, so once Facebook MCP Server is connected, you can use it with different AI models in TypingMind instead of setting it up separately for each model. This MCP runs locally through the TypingMind MCP connector on your device.

Setup guide to use the local connector

Use this when the MCP server needs access to local files, apps, or private resources on your computer.

Open the MCP settings

In TypingMind, go to Settings, Advanced Settings, then Model Context Protocol and choose Setup Connector.

Open TypingMind in your browser.
Click the Settings icon.
Go to Advanced Settings.
Open the Model Context Protocol section.
Click Setup Connector and choose This Device.

TypingMind MCP connector setup screen with This Device selected

Run the connector command

Choose This Device, copy the command from TypingMind, and run it in Terminal. Keep the process running while you use MCP.

Copy the setup command shown by TypingMind.
Open Terminal on macOS or Windows Terminal on Windows.
Paste and run the command.
Approve the package install if Terminal asks you to proceed.
Keep the Terminal window running while using MCP tools.

Add Facebook MCP Server as a server

When the connector status is Ready, click Edit Servers and paste the MCP server configuration.

Wait until the connector status shows Ready.
Click Edit Servers.
Paste the Facebook MCP Server MCP server configuration.
Save the server list.
Refresh if you want to confirm the connector is still ready.

TypingMind MCP settings showing active server and Edit Servers button

Use it across models

Save the server list, open Plugins, enable the Facebook MCP Server MCP tools, then select any supported AI model in TypingMind and use the tools in chat or assign them to an AI agent.

Open the Plugins page in TypingMind.
Enable the Facebook MCP Server MCP tools.
Start a chat and choose the AI model you want to use.
Use the MCP tools in chat or assign them to an AI agent.
Switch to another AI model whenever needed without reconnecting MCP.

TypingMind chat using enabled MCP tools with a selected AI model

Frequently asked questions

What is the Facebook MCP Server MCP server used for?

Facebook MCP Server is an MCP server that lets compatible AI clients connect to external tools and context. In TypingMind, you can add this MCP server once and make its tools available in your AI workspace.

Can I use Facebook MCP Server MCP with multiple AI models in TypingMind?

Yes. TypingMind connects MCP tools at the workspace level, so you can use Facebook MCP Server with different AI models such as Claude, ChatGPT, Gemini, or other models you have configured in TypingMind without setting up the MCP server separately for each model.

Why use Facebook MCP Server MCP with TypingMind?

TypingMind is one of the best frontends for LLM chat because it brings multiple AI models, prompts, plugins, AI agents, API keys, and MCP tools into one workspace. With Facebook MCP Server connected, you can use its MCP tools across your preferred models while keeping your chat workflow organized in TypingMind.

How do I connect Facebook MCP Server MCP to TypingMind?

Facebook MCP Server runs through the TypingMind local MCP connector. This is best when the MCP server needs access to local files, desktop apps, command-line tools, or private resources on your computer.

What tools does Facebook MCP Server MCP provide in TypingMind?

Facebook MCP Server exposes MCP capabilities that can be enabled from the TypingMind Plugins page and used in chat or assigned to AI agents.

Do I need to share my API keys with TypingMind to use Facebook MCP Server MCP?

No. TypingMind is local-first and lets you keep your model providers, API keys, prompts, and MCP configuration under your control. If Facebook MCP Server requires authentication, add the required headers, OAuth settings, or local configuration for that MCP server when you create the connection.

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Facebook MCP Server

🤖 What Is This?

✅ Benefits

📦 Features

🚀 Setup & Installation

1. Clone the Repository

2. 🛠️ Installation

3. Set Up Environment

🧩 Using with Claude Desktop

✅ You’re Ready to Go!

🤝 Contributing

Installation

Use Facebook MCP Server MCP with multiple AI models

Setup guide to use the local connector

Open the MCP settings

Run the connector command

Add Facebook MCP Server as a server

Use it across models

Frequently asked questions

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