mcp server
Analook — Competitor Intelligence
Competitor intelligence for AI agents — SEO, traffic, social, Product Hunt, pricing, AI insights.
Description as published by the maintainer. Source
- version 1.0.0
- slowing
slowing — Most recent push to the repository was 2026-05-27.
What this server can do
8 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
analyze_competitor(url, lang, product_name)- 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 Required: url.
browse_public_reports(category)- 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'
get_growth_audit(job_id)- 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. Required: job_id.
get_report(job_id)- 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. Required: job_id.
get_report_markdown(job_id)- 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} Required: job_id.
get_report_status(job_id)- Poll an analysis job's status. Args: job_id: ID returned from analyze_competitor() Returns: {status: 'running'|'completed'|'failed', progress?: str, report_url?: str} Required: job_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}, ...]}
run_growth_audit(url, lang, product_name)- 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' Required: url.
Last successful function declaration observed on . Source: https://www.analook.com/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://www.analook.com/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 0 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-05-27 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-04-22 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-04-22 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub | |
| mcp tools declared | 8 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | www.analook.com | |
| mcp endpoint status | ok | The server listed 8 functions when asked. | as of probe | www.analook.com |
Where to get it
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These share tags the maintainers applied themselves, such as competitive-intelligence, saas. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-agents, claude, competitive-intelligence, competitor-analysis, cursor, mcp, mcp-server, model-context-protocol, saas.
This record as data
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