AEO Copilot MCP Server

AEO Copilot MCP Server

Enables AI assistants to monitor and analyze a brand's visibility across ChatGPT, Claude, Perplexity, and Google AI Overviews, providing insights, recommendations, and competitive analysis without switching tabs.

Category
访问服务器

README

AEO Copilot MCP Server

Connect AEO Copilot to Claude or any MCP-compatible assistant. Ask your AI about your brand's visibility in ChatGPT, Claude, Perplexity, and Google AI Overviews without switching tabs.

What is AEO Copilot?

AI search engines are eating into traditional search traffic. When someone asks ChatGPT "What's the best tool for X?", your brand either shows up or it doesn't — and unlike Google, there's no ranking page to check.

AEO Copilot runs prompts against ChatGPT, Claude, Perplexity, and Google AI Overviews and records whether your brand got mentioned, where it ranked, how it was described, and which competitors showed up instead. This MCP server puts that data inside your AI assistant.

Tools

Tool Description
list_brands List all brands on your account
list_topics List topics for a brand (e.g. "Product Comparisons", "Pricing Questions")
get_results Per-prompt results across ChatGPT, Claude, Perplexity, and Google AI Overviews: mention status, position, sentiment, sources, competitors, and the full answer text. Set engine to read just one LLM's answers (e.g. Claude)
get_insights Aggregated analytics: visibility score, sentiment counts, competitive share, weekly trends, top topics
get_recommendations Prioritised action items based on prompt results and a technical audit of your site
create_brand Create a new brand (subject to your plan's brand limit)
create_topic Create a topic cluster for a brand
add_prompts Bulk-add prompts to a brand under a topic (subject to your plan's monthly prompt limit)
run_brand_prompts Run all prompts for a brand (or one topic) across every enabled LLM
scan_brand Run the technical audit on the brand's website and return the full scan result
create_index Create an industry index (brand-agnostic — every cited entity is captured)
list_indexes List all industry indexes on your account
add_index_topic Add a topic cluster to an index
add_index_prompts Bulk-add prompts to a topic in an index
run_index_prompts Run all index prompts across all 4 LLMs and store full per-LLM results
get_index_results Raw per-prompt results for an index across all LLMs
get_index_share_of_voice Ranked entity list by citation frequency + concentration score (top-1 share, HHI)
get_index_sources Domains ranked by citation frequency across every LLM response
get_index_whitespace Prompts/topics where no entity is consistently cited — opportunity gaps

Setup

1. Get your API key

  1. Log in to aeo-copilot.com
  2. Go to Settings → API
  3. Click Create API key
  4. Copy the key — it starts with aeo_

Keep it somewhere safe; you won't be able to see the full key again after closing the dialog.

2. Add to Claude Code

claude mcp add aeo-copilot -e AEO_COPILOT_API_KEY=aeo_your_key_here -- npx aeo-copilot-mcp

3. Add to Claude Desktop

Edit claude_desktop_config.json:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%\Claude\claude_desktop_config.json
{
  "mcpServers": {
    "aeo-copilot": {
      "command": "npx",
      "args": ["aeo-copilot-mcp"],
      "env": {
        "AEO_COPILOT_API_KEY": "aeo_your_key_here"
      }
    }
  }
}

Restart Claude Desktop after saving.

What you can ask

  • "What's my brand's AI visibility score?"
  • "Show me exactly what Claude said about my brand"
  • "Which prompts mention my competitors but not me?"
  • "Show me my visibility trend for the last 30 days"
  • "What topics are performing best in AI search?"
  • "What should I fix to improve my AI visibility?"
  • "How does my brand compare to [competitor] in AI-generated answers?"
  • "What's the sentiment breakdown for my brand mentions?"

API reference

list_brands

GET /api/v1/brands

Returns all brands on your account: name, website, industry, products, competitors.

list_topics

GET /api/v1/brands/:id/topics

Topics group related prompts. Each topic has a name, description, target pages, and keywords.

get_results

GET /api/v1/brands/:id/results

Per-prompt results. Each entry includes mention status, position, sentiment, sources cited, and competitors — broken down by AI engine. Every engine block also carries response: the full answer text that engine returned for the prompt.

Optional filters:

Parameter Type Description
topicId string Filter by topic
engine string Return only one engine's answers: chatgpt, claude, perplexity, or googleAio. Each result is flattened to that engine's block (including its response text) and prompts where it wasn't run are omitted. Omit to get every engine
from ISO date Start date, e.g. 2025-01-01
to ISO date End date
limit number Max results (default 100, max 500)

The API has no server-side engine filter — it always returns all four engine blocks. The MCP server applies the engine filter client-side so you get a focused, smaller payload when you only want one LLM (e.g. exactly what Claude said).

get_insights

GET /api/v1/brands/:id/insights

Aggregated analytics:

  • Visibility score: percentage of prompts where your brand was mentioned
  • Sentiment: positive, neutral, and negative counts
  • Competitive share: your mentions vs. competitor mentions
  • Visibility trend: week-by-week breakdown
  • Top topics: which groups are driving the most visibility
  • Competitor breakdown: how often each competitor appears

get_recommendations

GET /api/v1/brands/:id/recommendations

Prioritised recommendations (high / medium / low) across visibility, content, and technical categories.

create_brand

POST /api/v1/brands

Create a new brand. Body: name (required), website, industry, products[], competitors[]. Returns 402 if you've hit your plan's brand limit.

create_topic

POST /api/v1/brands/:id/topics

Create a topic cluster. Body: name (required), description, pages[], keywords[].

add_prompts

POST /api/v1/brands/:id/prompts

Bulk-add prompts under a topic. Body: topicId (required), prompts[] (required, array of strings). Returns 402 if adding these prompts would exceed your plan's monthly prompt limit.

run_brand_prompts

POST /api/v1/brands/:id/run

Run all prompts across every enabled LLM. Optional query param topicId to scope to a single topic. Returns the count of prompts run.

scan_brand

POST /api/v1/brands/:id/scan

Run the technical audit on the brand's website. Returns the full scan result (schema markup, sitemap, llms.txt, etc.) — same data the dashboard's technical scan view shows.

Industry indexes

Indexes are brand-agnostic: instead of tracking how your brand is mentioned, an index tracks every entity cited across an industry's prompts. Useful for category mapping, competitive whitespace, and seeing which sources the LLMs lean on.

create_index

POST /api/v1/indexes

Create an industry index. Body: name (required), industry (required), description.

list_indexes

GET /api/v1/indexes

List all indexes on your account.

add_index_topic

POST /api/v1/indexes/:id/topics

Add a topic cluster. Body: name (required), description.

add_index_prompts

POST /api/v1/indexes/:id/prompts

Bulk-add prompts under a topic. Body: topicId (required), prompts[] (required, array of strings).

run_index_prompts

POST /api/v1/indexes/:id/run

Run every prompt across all 4 LLMs (ChatGPT, Claude, Perplexity, Google AI Overviews). All extracted entities are stored as competitors — no brand filter.

get_index_results

GET /api/v1/indexes/:id/results

Raw per-prompt results across all LLMs. Same shape as /brands/:id/results minus the brand-mention fields.

get_index_share_of_voice

GET /api/v1/indexes/:id/share-of-voice

Ranked entity list by citation frequency, plus a concentration score:

  • top1Share: the % of mentions held by the most-cited entity
  • hhi: an HHI-style index (sum of squared shares × 10,000) showing how concentrated mentions are. Higher = more dominated by a few entities.

get_index_sources

GET /api/v1/indexes/:id/sources

Domains ranked by citation frequency across every LLM response in the index.

get_index_whitespace

GET /api/v1/indexes/:id/whitespace

Prompts and topics where no entity is consistently cited — i.e. fewer than 1 consistent entity appears across at least 50% of runs. These are the gaps where a brand can establish authority before the category solidifies.

Development

git clone https://github.com/sofianbettayeb/aeo-copilot-mcp
cd aeo-copilot-mcp
npm install
AEO_COPILOT_API_KEY=aeo_your_key npm run dev

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