Analook — Competitor Intelligence logo

Analook — Competitor Intelligence

Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.

Install Analook — Competitor Intelligence

Pick your client, then add it in one click or paste the config.

Streamable HTTP (Remote)
Server URL
https://www.analook.com/mcp

Remote servers are added in the app, not the config file: Customize → Connectors → Add custom connector, then paste this URL.

Overview

Tools8

What Analook — Competitor Intelligence lets your agent do. Expand a tool for its parameters.

Verified live~4.2k tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked 1d ago

Recent health check succeeded.

29/29 checks healthy over the last 91d

What it can do

From the 8 tools this server returned when we ran it.

Makes network requests · 7Read-only tools · 8

Observed 2026-10-17 in an isolated sandbox. A description of reach, not a security audit.

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars111Stargazers on the source repository.
Last commit7d agoMost recent push to the default branch.
Tools exposed8Callable tools this server registers over MCP.

From the project README

Analook Logo

Analook — 竞品情报分析工具

30 秒看透任何竞品 · AI-powered competitor intelligence for indie hackers & growth teams

License: MIT Website Skills on ClawHub Deployed on Railway

English | 中文 · 🤖 MCP Server · 📖 Docs

🤖 Remote MCP Server

Analook is available as a Remote MCP server — use it from Claude Desktop, Cursor, or any MCP-compatible client. No install, no local process.

config.json
{
  "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.tools — Iris 的出海增长咨询,1:1 指导、开源项目运营、企业顾问服务

Table of Contents


🌱 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)

SessionPriceBest For
Quick Call (30 min)$150 USDSpecific questions, quick diagnosis
Deep Dive (60 min)$300 USDFull strategy review, detailed roadmap

2. Advisory Retainer

PlanPriceIncludes
Monthly Retainer$1,500 USD/moUp to 5 hours strategic consultation + milestone reviews

3. Playbooks & Templates

PackagePriceContents
Starter Pack$29 USDCore methodology + essential tools
Flagship Bundle$199 USDComplete SOP, competitor research framework, templates

📩 Contact @Iris_carrot on Telegram — Crypto/USDT and Wire Transfer accepted


What is Analook?

Analook is an open-source AI-powered competitor intelligence tool built by Iris, former cofounder & COO of AFFiNE (60k+ stars).

Enter any product URL. In ~30 seconds, Analook runs a 7-module parallel analysis pipeline and returns a structured deep-dive report — growth strategy, traffic signals, social footprint, ProductHunt history, AI insights, and more.

Built for:

  • Indie hackers validating a market before building
  • Growth teams benchmarking against competitors
  • Investors doing quick pre-DD intelligence
  • Founders preparing a launch in a crowded niche

Analysis Results Include

SignalWhat You Learn
🌐 Website HistoryWhen launched, how fast it grew, Wayback timeline
📈 Traffic & SEOMonthly visits, top channels, keyword gaps
🐦 Twitter / XFollowers, engagement rate, content strategy
🚀 Product HuntLaunch scores, positioning, community response
💡 AI Deep DiveICP, business model, growth flywheel, tactical recs
📡 PropagationPeak traffic events, viral moments, channel breakdown
🧠 Growth StrategyEarly-stage strategy reconstruction from public signals

What's Inside

This repo is the production backend powering analook.com:

  • app.py — FastAPI orchestrator: parallel analysis pipeline, job state, streaming, credit gating
  • modules/ — analysis engines (traffic, social, Product Hunt, growth analysis, AI summary) plus the Supabase client, payment integrations, and the MCP server (mcp_app.py)
  • migrations/ — Supabase SQL migrations (auth/credits/promo codes/attribution)
  • scripts/ — operational tooling: user metrics, attribution reports, EDM campaigns
  • static/ — the web UI served at analook.com
  • tests/ + Dockerfile — regression suite and container build

Stack: FastAPI + Supabase (auth, credits, report storage), deployed via Docker.


🔗 Live Demo

analook.com — Free to use. No login required.

Try with: lovable.dev · linear.app · notion.so · cursor.com


📦 Analysis Modules

ModuleFileDescription
Website Historymodules/website_history.pyWayback Machine CDX API, first-seen date, snapshot timeline
Traffic & SEOmodules/traffic_analysis.pyDataForSEO — monthly visits, channels, top pages, keywords
Social Mediamodules/social.pyApify Twitter scraper — followers, engagement, recent content
Product Huntmodules/producthunt.pyPH GraphQL API — launches, scores, upvotes, reviews
OSS Growth Attributionmodules/oss_growth_attribution.pyGitHub star stages, public channel evidence, representative content links, confidence scoring
Growth Analysismodules/growth_analysis.pyTraffic peak detection, event correlation, growth stage
AI Summarymodules/ai_summary.py7-section structured analysis via TeamoRouter + DeepSeek fallback
Report Builderapp.pyFastAPI orchestrator — parallel pipeline, job state, streaming

🧠 AI Insights Depth

Analook's AI module goes beyond surface-level summaries. Each report includes:

  1. 产品定位与 ICP — Target user profiles, market positioning, who actually pays
  2. 商业模式拆解 — Pricing model, conversion hypothesis, revenue estimate range
  3. 增长密码 — 4–6 data-backed growth strategies with evidence
  4. 增长飞轮 — Product-specific flywheel reconstruction
  5. 内容与传播策略 — Channel mix, content types, launch propagation model
  6. 给后来者的战术建议 — 5 actionable recommendations you can execute this week
  7. 风险与机会 — Red flags and market gaps, with data citations

Powered by TeamoRouter (primary) and DeepSeek (fallback), with max 4,000 token output per report.


⚡ Quick Start

bash
git clone https://github.com/Gingiris-1031/Competitor-analysis-tool.git
cd Competitor-analysis-tool
pip install -r requirements.txt

Create a .env file:

env
TEAMOROUTER_API_KEY=sk-teamo-...
DEEPSEEK_API_KEY=sk-...
DATAFORSEO_B64=base64(email:password)
PRODUCTHUNT_TOKEN=...
APIFY_API_TOKEN=apify_api_...

Run locally:

bash
uvicorn app:app --reload --port 8000

Visit http://localhost:8000


🚀 Deploy Your Own

Production source-of-truth

The production source is the main branch of Gingiris-1031/Competitor-analysis-tool. Before any production deploy, run:

bash
python3 scripts/verify_deploy_source.py

The preflight fails if the checkout is behind origin/main, is on the wrong branch or remote, or is missing tracked SEO/GEO assets such as static/llms.txt, static/robots.txt, and static/sitemap.xml. This prevents an older local clone from silently removing live assets during deployment.

The current production Fly app is declared in fly.toml as competitor-analysis-tool. Run the preflight from the same checkout immediately before fly deploy.

Self-hosting

One-click deploy on Railway:

Deploy on Railway

Steps:

  1. Fork this repo
  2. Create a new Railway project → connect your fork
  3. Add the environment variables above in Railway → Settings → Variables
  4. Railway auto-deploys on every push to main

The app is Dockerized and uses Nixpacks on Railway (Python 3.13).


🔌 API Integrations

ServicePurposeNotes
TeamoRouterPrimary LLMRoutes to best available model
DeepSeekFallback LLMCost-efficient backup
DataForSEOTraffic & SEO dataMonthly visits, channels, keywords
ApifyTwitter/X scrapingapidojo/twitter-profile-scraper actor
ProductHuntLaunch historyPH GraphQL API
Wayback MachineWebsite historyCDX API + timemap

⭐ Star This Repo

If Analook saved you hours of manual research, a ⭐ helps others discover it!


About the Author

Iris (生姜iris) — Former cofounder & COO of AFFiNE (60k+ GitHub stars). Now running Gingiris — an open-source go-to-market and global expansion consulting practice.

Related Playbooks (now on ClawHub):

  • @gingiris on ClawHub — GTM strategy, B2B SaaS PLG/SLG growth, open source launch marketing, and other AI-agent skills

中文版

Analook 是什么?

Reviews

No reviews yet — be the first to share how it worked for you.

Frequently Asked Questions about Analook — Competitor Intelligence

Analook — Competitor Intelligence is an MCP server that gives AI agents access to competitor intelligence workflows through a remote endpoint. It supports analyses covering SEO, traffic, social presence, Product Hunt history, pricing, and AI insights, then provides tools to check report status, retrieve reports, return Markdown, and list your reports.

Connect using the remote MCP endpoint https://www.analook.com/mcp and an Authorization header containing your Analook bearer token. It works with Claude Desktop, Cursor, and other MCP-compatible clients without installing or running a local process.

The server provides five tools: analyze_competitor, get_report_status, get_report, get_report_markdown, and list_my_reports. These tools let an MCP client start competitor analysis, monitor reports, retrieve results as structured data or Markdown, and list the user’s reports.

Analook analyzes competitor signals including website history, traffic and SEO, social presence, Product Hunt history, pricing, and AI insights. You provide a product URL, and the service runs a parallel analysis pipeline that returns a structured report.

Related MCP servers

Add a badge to your READMEShow it’s listed — live SVG, Markdown or HTML.

AllMCPs Directory Badge

Full Badge Customizer

Showcase your server listing on GitHub or your project documentation. Embed this dynamic SVG badge to highlight official listing status and live engagement.

Badge Style:
Live Dynamic SVG PreviewAnalook — Competitor Intelligence AllMCPs Directory Badge
Markdown (GitHub README)
[![AllMCPs](https://allmcps.com/api/badge/analook-competitor-intelligence?style=directory)](https://allmcps.com/mcp/analook-competitor-intelligence)
HTML Embed
<a href="https://allmcps.com/mcp/analook-competitor-intelligence"><img src="https://allmcps.com/api/badge/analook-competitor-intelligence?style=directory" alt="Analook — Competitor Intelligence on AllMCPs" /></a>