Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.
MCP Server: io.github.Gingiris-1031/analook
This MCP server provides competitor intelligence for AI agents, focused on SEO, traffic, social, Product Hunt, pricing, and AI insights. It exposes 8 tools to support analysis workflows where agent-driven research can reference multiple market signals for a given competitor.
🛠️ Key Features
Competitor intelligence for AI agents
Coverage includes SEO, traffic, social, Product Hunt, and pricing
Includes “AI insights” as part of the intelligence scope
8 available tools
🚀 Use Cases
Researching competitors using SEO and traffic signals
Tracking competitor presence on social channels and Product Hunt
Comparing competitor pricing and associated AI-driven insights
⚡ Developer Benefits
Tool-based integration (toolCount: 8) for agent workflows
Structured targeting of multiple intelligence categories (SEO, traffic, social, Product Hunt, pricing, AI insights)
⚠️ Limitations
Source material does not describe authentication, output formats, or specific tool names/inputs.
Captured live from the server via tools/list.
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
Poll an analysis job's status.
Args:
job_id: ID returned from analyze_competitor()
Returns:
{status: 'running'|'completed'|'failed', progress?: str, report_url?: str}
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.
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}
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}, ...]}
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'
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.
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'
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
Signal
What You Learn
🌐 Website History
When launched, how fast it grew, Wayback timeline
📈 Traffic & SEO
Monthly visits, top channels, keyword gaps
🐦 Twitter / X
Followers, engagement rate, content strategy
🚀 Product Hunt
Launch scores, positioning, community response
💡 AI Deep Dive
ICP, business model, growth flywheel, tactical recs
modules/ — analysis engines (traffic, social, Product Hunt, growth analysis, AI summary) plus the Supabase client, payment integrations, and the MCP server (mcp_app.py)
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.
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.
