adpilot-mcp

adpilot-mcp

Enables AI agents to perform marketing operations like campaign management, content creation, and outreach, with a tiered approval model ensuring human oversight for consequential actions.

Category
访问服务器

README

adpilot-mcp

The MCP (Model Context Protocol) server from AdPilot, a production system where AI agents run marketing operations, ad campaigns, content, outreach and SEO, across a set of projects, with a human in the loop for anything that matters.

This repo is the agent-facing tool surface: the MCP server and its tool definitions. The data-access layer it calls (Postgres, and the Google Ads, Meta, GA4 and Search Console integration clients) is part of the private AdPilot app and is not included here. What is published is the part worth reading: how the tools are designed, and how the approval model keeps autonomous agents safe on real accounts.

Why it exists

Agents are good at drafting a campaign, writing a post, or preparing outreach. They are not something I want spending real ad budget or emailing real people without a check. AdPilot lets agents propose actions through these tools, and a tiered approval gate decides what happens next.

The approval model

Every action-taking tool routes through queue_approval with a tier:

  • Tier 1 (auto): low-risk, runs immediately (for example, pulling a performance report).
  • Tier 2 (approval): anything with consequences waits for a human sign-off in Discord before it executes (publishing content, sending outreach, changing spend).
  • Tier 3 (blocked): never allowed from an agent.

An agent can do the whole job end to end, but the blast radius is bounded. Read-only tools return data directly. Write tools return a queued item, not a completed action.

Tools

Read:

  • list_projects - projects and their integration status
  • get_campaign_summary - campaigns and spend for a project
  • get_performance - GA4 sessions and Search Console keywords
  • get_spend_ledger - spend history for a project
  • get_pending_approvals / get_pending_content / get_pending_outreach - the approval queues

Write (queued behind approval):

  • queue_approval - queue an action at a given tier
  • queue_content - queue a social post across one or more platforms
  • queue_outreach - queue an outreach message
  • create_blog_post / update_blog_post / publish_blog_post - blog workflow
  • upload_asset - register a generated image, video or audio asset

Plus a project-intelligence tool group.

How it is wired

Standard MCP: a Server over StdioServerTransport, a ListToolsRequest handler that returns the catalogue above, and a CallToolRequest handler that dispatches to the tool implementations in mcp/tools/. Built on @modelcontextprotocol/sdk.

Note

This is excerpted from a live private system, for reference. It is not a runnable package on its own, the data and integration layers are omitted deliberately. It is here to show the tool design and the approval model, not to be installed.

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选