Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"io-github-gingiris-analook": {
"command": "<see-readme>",
"args": []
}
}
}Are you the author?
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Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.
No automated test available for this server. Check the GitHub README for setup instructions.
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English | 中文 · 🤖 MCP Server · 📖 Docs
Analook is available as a Remote MCP server — use it from Claude Desktop, Cursor, or any MCP-compatible client. No install, no local process.
{
"mcpServers": {
"analook": {
"url": "https://www.analook.com/mcp",
"headers": { "Authorization": "Bearer <YOUR_ANALOOK_TOKEN>" }
}
}
}
5 tools: analyze_competitor, get_report_status, get_report, get_report_markdown, list_my_reports.
Full docs & token instructions → analook.com/docs/mcp
💡 出海增长咨询 · 1v1 Session 约课 $200 — Telegram @Iris_carrot
或访问 gingiris.com — Iris 的出海增长咨询,1:1 指导、开源项目运营、企业顾问服务
🌱 Philosophy
"做竞品分析不是为了抄,是为了找到别人没做好的地方。" — Competitor research isn't about copying — it's about finding the gaps they left open.
"数据会说话,但你得先知道问对问题。" — Data speaks, but only if you ask the right questions.
"30 秒看懂一个产品,30 天超越它。" — Understand a product in 30 seconds. Surpass it in 30 days.
💼 Work With Iris
1. Strategic Consultation (1v1)
Session Price Best For Quick Call (30 min) $150 USD Specific questions, quick diagnosis Deep Dive (60 min) $300 USD Full strategy review, detailed roadmap 2. Advisory Retainer
Plan Price Includes Monthly Retainer $1,500 USD/mo Up to 5 hours strategic consultation + milestone reviews 3. Playbooks & Templates
Package Price Contents Starter Pack $29 USD Core methodology + essential tools Flagship Bundle $199 USD Complete SOP, competitor research framework, templates 📩 Contact @Iris_carrot on Telegram — Crypto/USDT and Wire Transfer accepted
Analook is an open-source AI-powered competitor intelligence tool built by Iris, former cofounder & COO of [AFFiNE](https://github.com/toe