Analook — Competitor Intelligence

io.github.Gingiris-1031Published by GitHub account Gingiris-1031v1.0.2Updated Oct 24, 2026

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

VerifiedStreamable HTTPWeb executableWeb Search & ScrapingData & AnalyticsBusiness & Commerce

Overview

AI-generated overview

Remote MCP server that lets an assistant run AI competitor analyses on any product URL and fetch structured or Markdown intelligence reports.

What you can do
  • Submit a competitor analysis job for a product URL, drawing on SEO, traffic, social, Product Hunt, GitHub and Wayback data
  • Poll job status and retrieve the finished report as structured JSON or human-readable Markdown
  • List your recent reports and browse the public report gallery without spending credits
  • Run a Growth Audit that produces an executive summary, a diagnosis report and a 30-day action plan, then fetch the three reports
When to use it
Useful for indie hackers validating a market, growth teams benchmarking competitors, investors doing quick pre-diligence, or founders preparing a launch in a crowded niche who want competitor intelligence inside the assistant.
Requirements
A remote MCP endpoint; no local install or process. The README's configuration sends an Authorization Bearer token, and listing your own reports requires authentication. Analysis jobs consume credits.
Before you install
The README's client configuration passes an Analook token in an Authorization header, so keep that token private. Analyses consume credits, and browsing public reports is described as the free alternative. Submitted product URLs and analysis data are processed by the hosted service and its third-party data providers.

Installation

In SourceWeft

  1. Open Analook — Competitor Intelligence in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/io.github.Gingiris-1031%2Fanalook/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "analook-competitor-intelligence": {
      "type": "http",
      "url": "https://www.analook.com/mcp/"
    }
  }
}

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.

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.gitcd Competitor-analysis-toolpip 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？

Analook 是一个开源 AI 竞品情报工具，由 AFFiNE（60k+ stars）联创 & 前 COO Iris 构建。

输入任意产品网址，30 秒内生成一份结构化竞品深度报告——包含增长策略、流量信号、社交足迹、ProductHunt 历史、AI 洞察等全套数据。

适合谁用？

  • 独立开发者 — 开始动手前快速验证市场
  • 增长团队 — 对标竞品，找到差距
  • 投资人 — Pre-DD 快速情报收集
  • 创始人 — 在红海市场找到进攻角度

核心分析模块

模块数据来源输出内容
🌐 网站历史Wayback Machine上线时间、成长速度、快照时间线
📈 流量 & SEODataForSEO月访问量、流量渠道、关键词矩阵
🐦 Twitter/XApify粉丝数、互动率、内容策略
🚀 Product HuntPH GraphQL发布记录、评分、社区反馈
⭐ 开源增长归因GitHub API + 公开内容检索Star 阶段、渠道证据、关键内容原链、证据置信度
💡 AI 深度分析TeamoRouterICP 画像、商业模式、增长飞轮、战术建议
📡 传播分析综合流量峰值事件、渠道来源、传播节点
🧠 早期增长策略综合从公开信号反推产品早期增长路径

快速部署

bash
git clone https://github.com/Gingiris-1031/Competitor-analysis-tool.gitcd Competitor-analysis-toolpip install -r requirements.txt# 配置 .env 文件后：uvicorn app:app --reload --port 8000

Railway 一键部署： Fork 本 repo → Railway 连接 → 添加环境变量 → 自动部署 ✅

关于作者

Iris（生姜iris），AFFiNE 联创 & 前 COO，现运营 Gingiris 开源出海增长咨询。

License

MIT — free to use, modify, and redistribute.

Source: README.md at commit 9bf577d

Tools

8

analyze_competitor

Submit a competitor analysis job. Analyzes a competitor's website across 15+ data sources (SEO, traffic, social, Product Hunt, GitHub, Wayback Machine history, AI-generated insights, etc.) and returns a job_id. Use get_report_status(job_id) to poll and get_report(job_id) to retrieve results when status='completed'. Typical analysis takes 2-5 minutes. Requires authentication (deducts 1 credit from your Analook balance). Args: url: Competitor website URL (e.g. 'https://linear.app' or 'lovable.dev') product_name: Optional product name override (defaults to domain) lang: Report language, 'en' (default) or 'zh' for Chinese output Returns: {job_id: str, status: 'started', poll_url: str} on success {error: str, hint?: str} on auth/validation failure

Parametersurlrequiredlangcontextrequiredllm_modelrequiredproduct_nameconversation_id

get_report_status

Poll an analysis job's status. Args: job_id: ID returned from analyze_competitor() Returns: {status: 'running'|'completed'|'failed', progress?: str, report_url?: str}

Parametersjob_idrequiredcontextrequiredllm_modelrequiredconversation_id

get_report

Fetch the full competitor analysis report as structured JSON. Reports contain: website snapshot, Wayback Machine history, SEO/traffic data (DataForSEO), social media presence, Product Hunt launches, GitHub stats, pricing, funding, AI-generated business insights, growth playbooks, and more. Args: job_id: ID from analyze_competitor(); status must be 'completed' Returns: The full report dict (nested structure), or {error} if not found / not ready.

Parametersjob_idrequiredcontextrequiredllm_modelrequiredconversation_id

get_report_markdown

Fetch the competitor analysis report as human-readable Markdown. Suitable for piping into agents that prefer text over structured JSON, or for direct display to end users. Args: job_id: ID from analyze_competitor(); status must be 'completed' Returns: {markdown: str} or {error: str}

Parametersjob_idrequiredcontextrequiredllm_modelrequiredconversation_id

list_my_reports

List your recent competitor analysis reports (up to 50). Requires authentication. Returns a lightweight list (id, url, product_name, created_at, status) — use get_report(job_id) to fetch the full report for any of them. Returns: {reports: [{id, url, product_name, created_at, status}, ...]}

Parameterscontextrequiredllm_modelrequiredconversation_id

run_growth_audit

Run a full Growth Audit — three linked strategic reports for a product. Unlike analyze_competitor (a single 15-signal intelligence snapshot), a Growth Audit produces an Executive Summary + a Diagnosis Report + a 30-day Action Plan, grounded in real channel/tactic playbooks. Best for 'how do I grow THIS product' rather than 'what is this competitor doing'. Takes ~4-6 minutes. Requires authentication and deducts 10 credits. Poll with get_growth_audit(job_id) until status='completed'. Args: url: Product website URL to audit product_name: Optional product name override (defaults to domain) lang: Report language, 'en' (default) or 'zh'

Parametersurlrequiredlangcontextrequiredllm_modelrequiredproduct_nameconversation_id

get_growth_audit

Fetch a Growth Audit's three reports (Executive Summary, Diagnosis, Action Plan) as Markdown. Args: job_id: ID from run_growth_audit() (starts with 'ga-') Returns: {status, reports: {executive_summary, diagnosis_report, action_plan}} while running, only {status, progress} is returned.

Parametersjob_idrequiredcontextrequiredllm_modelrequiredconversation_id

browse_public_reports

Browse Analook's public competitor-intelligence report gallery. Returns recently published public reports (product name, domain, category, and a link). No authentication or credits required — a fast way to discover existing analyses before spending a credit on a fresh one. Args: category: Optional filter, e.g. 'AI / Agents', 'Dev Tools', 'Crypto / Web3', 'Marketing / SEO', 'SaaS / Other'

Parameterscontextrequiredcategoryllm_modelrequiredconversation_id

Version history

1
  1. v1.0.2LatestSep 16, 2026

Score

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  • Reached in the last 30 daysRequired25 pts
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