Enterprise DNA
M MCP Servers Developer low

Nolas-Shadow/agent1st-ads-mcp

by Various

Facebook and Tic Toc MCP

N

MCP

Nolas-Shadow/agent1st-ads-mcp

Added 1 June 2026

#agent1st #agent1st-ads #mcp #mcp-server #meta-ads #model-context-protocol #tiktok-ads

Overview

A JavaScript MCP server that provides access to Facebook and TikTok advertising APIs. It enables AI agents to manage ad campaigns, retrieve performance data, and automate ad operations through the Model Context Protocol.

Best for

Best for
Developers building AI agents that need to interact with Facebook and TikTok advertising APIs

Use cases

  • Automate ad campaign creation and management across Facebook and TikTok
  • Pull real-time ad performance metrics into AI agent workflows
  • Integrate ad platform data with other MCP-compatible tools

Notes

A JavaScript MCP server that provides access to Facebook and TikTok advertising APIs. It enables AI agents to manage ad campaigns, retrieve performance data, and automate ad operations through the Model Context Protocol.

2 stars on GitHub. Last updated 2026-03-23. Licensed MIT.

Use cases

  • Automate ad campaign creation and management across Facebook and TikTok
  • Pull real-time ad performance metrics into AI agent workflows
  • Integrate ad platform data with other MCP-compatible tools

Pros

  • Direct integration with two major ad platforms via a single MCP server
  • Lightweight JavaScript implementation easy to extend or modify
  • Open source with permissive license for custom deployments

Cons

  • Very early stage with only 2 GitHub stars and minimal community adoption
  • Limited documentation and no clear usage examples beyond the basic description
  • Requires separate API credentials and setup for each ad platform

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Direct integration with two major ad platforms via a single MCP server
  • Lightweight JavaScript implementation easy to extend or modify
  • Open source with permissive license for custom deployments

Cons

  • Very early stage with only 2 GitHub stars and minimal community adoption
  • Limited documentation and no clear usage examples beyond the basic description
  • Requires separate API credentials and setup for each ad platform