This project is a **Model Context Protocol (MCP)** server for automating and managing interactions on a **Facebook Page** using the Facebook Graph
This project is a Model Context Protocol (MCP) server for automating and managing interactions on a Facebook Page using the Facebook Graph API. It provides tools to create posts, moderate comments, fetch post insights, and filter. The just facebook mcp mcp server wraps that behind the Model Context Protocol, so an assistant can use it rather than through you.
You will need 2 environment variables: FACEBOOK_ACCESS_TOKEN, FACEBOOK_PAGE_ID. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Installation goes through your MCP client rather than a global install: point it at You on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Plenty of AI and media services servers cover similar ground. The differences that matter in practice are scope of access and how much setup stands between you and a working tool call. It is maintained by Livia-Zaharia; worth a glance at recent repository activity before you build anything load-bearing on it.
SyncDev reviews every entry in this directory against the project's own documentation before publishing, and revisits them as servers change.
{
"mcpServers": {
"just_facebook_mcp": {
"command": "uvx",
"args": [
"just_facebook_mcp"
],
"env": {
"FACEBOOK_ACCESS_TOKEN": "<put_your_FB_token_here>",
"FACEBOOK_PAGE_ID": "<put_your_page_id_here>"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| FACEBOOK_ACCESS_TOKEN | Credential the server authenticates with. | Yes |
| FACEBOOK_PAGE_ID | Configuration value read at startup. | Optional |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.