MCP Server
Analook — Competitor Intelligence
io.github.Gingiris/analook
What this MCP does
Analyzes competitors across SEO, traffic, social media, Product Hunt, GitHub, pricing, funding, and related business signals, and produces intelligence reports and growth audits.
Capabilities
Tools
8
tools
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
Input schema
{'type': 'object', 'title': 'analyze_competitorArguments', 'required': ['url', 'context', 'llm_model'], 'properties': {'url': {'type': 'string', 'title': 'Url'}, 'lang': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lang', 'default': None}, 'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'product_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Product Name', 'default': None}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
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 â 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'
Input schema
{'type': 'object', 'title': 'browse_public_reportsArguments', 'required': ['context', 'llm_model'], 'properties': {'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'category': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Category', 'default': None}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
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.
Input schema
{'type': 'object', 'title': 'get_growth_auditArguments', 'required': ['job_id', 'context', 'llm_model'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id'}, 'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
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.
Input schema
{'type': 'object', 'title': 'get_reportArguments', 'required': ['job_id', 'context', 'llm_model'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id'}, 'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
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}
Input schema
{'type': 'object', 'title': 'get_report_markdownArguments', 'required': ['job_id', 'context', 'llm_model'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id'}, 'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
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}
Input schema
{'type': 'object', 'title': 'get_report_statusArguments', 'required': ['job_id', 'context', 'llm_model'], 'properties': {'job_id': {'type': 'string', 'title': 'Job Id'}, 'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
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) â use get_report(job_id) to fetch
the full report for any of them.
Returns:
{reports: [{id, url, product_name, created_at, status}, ...]}
Input schema
{'type': 'object', 'title': 'list_my_reportsArguments', 'required': ['context', 'llm_model'], 'properties': {'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
run_growth_audit
Run a full Growth Audit â 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'
Input schema
{'type': 'object', 'title': 'run_growth_auditArguments', 'required': ['url', 'context', 'llm_model'], 'properties': {'url': {'type': 'string', 'title': 'Url'}, 'lang': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Lang', 'default': None}, 'context': {'type': 'string', 'description': 'Explain in 15-25 words, in third person, why this tool is called and how it supports the user\'s goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer\'s recent orders to investigate a billing issue and help support determine the appropriate resolution."'}, 'llm_model': {'type': 'string', 'description': 'The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" â\x80\x94 never guess.'}, 'product_name': {'anyOf': [{'type': 'string'}, {'type': 'null'}], 'title': 'Product Name', 'default': None}, 'conversation_id': {'type': 'string', 'description': "Echo the conversation_id from the server's previous response. The server provides it on the first call â\x80\x94 never invent one, and do not issue parallel tool calls until you have it."}}}
History
Recent tool changes
Changed
browse_public_reports
Sept. 19, 2026, 2:41 a.m.
Changed
get_growth_audit
Sept. 19, 2026, 2:41 a.m.
Changed
run_growth_audit
Sept. 19, 2026, 2:41 a.m.
Changed
list_my_reports
Sept. 19, 2026, 2:41 a.m.
Changed
get_report_markdown
Sept. 19, 2026, 2:41 a.m.
Changed
get_report
Sept. 19, 2026, 2:41 a.m.
Changed
get_report_status
Sept. 19, 2026, 2:41 a.m.
Changed
analyze_competitor
Sept. 19, 2026, 2:41 a.m.
Added
browse_public_reports
Sept. 17, 2026, 12:42 p.m.
Added
get_growth_audit
Sept. 17, 2026, 12:42 p.m.
Added
run_growth_audit
Sept. 17, 2026, 12:42 p.m.
Added
list_my_reports
Sept. 17, 2026, 12:42 p.m.
Added
get_report_markdown
Sept. 17, 2026, 12:42 p.m.
Added
get_report
Sept. 17, 2026, 12:42 p.m.
Added
get_report_status
Sept. 17, 2026, 12:42 p.m.
Added
analyze_competitor
Sept. 17, 2026, 12:42 p.m.
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