A [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) server that lets AI assistants — Claude, ChatGPT, Perplexity, Cursor, or any MCP
If you already use Meta Ads MCP, the meta ads mcp mcp server is the piece that lets your assistant work with it directly. A Model Context Protocol (MCP) server that lets AI assistants — Claude, ChatGPT, Perplexity, Cursor, or any MCP client — run your Meta Ads end to end: launch campaigns, upload creatives, update budgets.
The toolset is worth reading before you wire it up, because it tells you what the integration is really for:
Inputs — - access_token (optional): Meta API access token (will use cached token if not provided)user_id — Meta user ID or "me" for the current userlimit — Maximum number of accounts to return (default: 200)Returns — List of accessible ad accounts with their detailsaccount_id — Meta Ads account ID (format: act_XXXXXXXXX)status_filter — Filter by status (empty for all, or 'ACTIVE', 'PAUSED', etc.)campaign_id — Meta Ads campaign IDname — Campaign nameobjective — Campaign objective (ODAX, outcome-based). Must be one of:OUTCOME_AWARENESS — OUTCOME_TRAFFICOUTCOME_ENGAGEMENT — OUTCOME_LEADSOUTCOME_SALES — OUTCOME_APP_PROMOTIONBecause this one is hosted, setup is mostly authentication — you point your client at the endpoint and approve access. Nothing runs on your machine, so there is no runtime to keep patched.
Configuration is passed through the environment: PIPEBOARD_API_TOKEN, YOUR_PIPEBOARD_TOKEN. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
Plenty of developer tooling 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. Meta Ads MCP's toolset — Inputs, user_id, limit and 11 more — is a fair guide to whether it matches your workflow. It is maintained by pipeboard-co; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Inputs | - access_token (optional): Meta API access token (will use cached token if not provided) |
| user_id | Meta user ID or "me" for the current user |
| limit | Maximum number of accounts to return (default: 200) |
| Returns | List of accessible ad accounts with their details |
| account_id | Meta Ads account ID (format: act_XXXXXXXXX) |
| status_filter | Filter by status (empty for all, or 'ACTIVE', 'PAUSED', etc.) |
| campaign_id | Meta Ads campaign ID |
| name | Campaign name |
| objective | Campaign objective (ODAX, outcome-based). Must be one of: |
| OUTCOME_AWARENESS | OUTCOME_TRAFFIC |
| OUTCOME_ENGAGEMENT | OUTCOME_LEADS |
| OUTCOME_SALES | OUTCOME_APP_PROMOTION |
| status | Initial campaign status (default: PAUSED) |
| special_ad_categories | List of special ad categories if applicable |
{
"mcpServers": {
"meta-ads-remote": {
"url": "https://meta-ads.mcp.pipeboard.co/"
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| PIPEBOARD_API_TOKEN | Credential the server authenticates with. | Yes |
| YOUR_PIPEBOARD_TOKEN | Credential the server authenticates with. | Yes |
The simplest web tool that matters — fetch any URL and get model-ready markdown back.
Industrial-strength web extraction — render, scrape, crawl and search entire sites into clean markdown.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
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.
Put 6,000+ pre-built scrapers at your assistant's fingertips through one MCP endpoint.