anysite-market-research

name anysite-market-research description Conduct comprehensive market research using Y Combinator data, SEC filings, social media insights, and web scraping via anysite MCP server. Analyze tech markets, research startup ecosystems, study public companies, identify market opportunities, and understand competitive dynamics. Supports startup discovery, industry analysis, public company research, and social sentiment analysis. Use when users need to analyze market opportunities, research industries, evaluate startups, study public companies, or gather market intelligence for strategic planning and investment decisions. anysite Market Research Comprehensive market research using Y Combinator, SEC, social media, and web data through anysite MCP. Analyze tech markets, research startups, and study competitive landscapes. Overview Research startup ecosystems via Y Combinator data Analyze public companies through SEC filings Gather market intelligence from social platforms Study industry trends across communities Identify market opportunities through data analysis Coverage : 70% - Excellent for tech/startup markets; pivoted from local business to tech focus Supported Platforms ✅ Y Combinator : Startup research, batch analysis, founder discovery, funding data ✅ SEC : Public company filings, financial data, disclosures ✅ Reddit : Market sentiment, community insights, product discussions ✅ LinkedIn : Industry trends, company intelligence, professional discussions ✅ Twitter/X : Market pulse, news, influencer opinions ✅ Web Scraping : Company websites, industry reports, market data v2 MCP Tool Interface All data fetching uses the universal execute() meta-tool. Always call discover(source, category) first if you need to verify endpoint names or parameters. Core workflow : execute(source, category, endpoint, params) -- fetch data (returns first page + cache_key ) get_page(cache_key, offset, limit) -- paginate through remaining results query_cache(cache_key, conditions, sort_by, aggregate, group_by) -- filter/sort/aggregate cached data without new API calls export_data(cache_key, format) -- export to CSV, JSON, or JSONL for deliverables Error handling : check response for llm_hint field -- it contains actionable guidance when calls fail or return partial data. Quick Start Step 1: Define Research Scope Choose focus: Startup ecosystem: execute("yc", "search", "search", {"query": ...}) Public companies: execute("sec", "search", "search", {"query": ...}) Industry sentiment: execute("reddit", "search", "search", {"query": ...}) , execute("twitter", "search", "search_users", {"query": ...}) Company intelligence: execute("linkedin", "search", "search_companies", {...}) Step 2: Gather Data Execute searches: # Startup research execute("yc", "search", "search", {"query": "fintech", "batch": "W24,S23"}) # Public company research execute("sec", "search", "search", {"query": "tech company"}) # Market sentiment execute("reddit", "search", "search", {"query": "fintech trends"}) → use get_page(cache_key, offset, limit) to collect up to 100 results Step 3: Analyze Results Use query_cache() to slice data without re-fetching: # Count startups by category query_cache(cache_key, aggregate={"field": "category", "function": "count"}) # Filter high-engagement posts query_cache(cache_key, conditions=[{"field": "score", "operator": ">", "value": 50}], sort_by={"field": "score", "order": "desc"}) Extract insights: Market size indicators Competitive landscape Technology trends Consumer sentiment Funding patterns Step 4: Synthesize Findings Use export_data(cache_key, "csv") or export_data(cache_key, "json") to deliver: Market opportunity assessment Competitive analysis Trend identification Strategic recommendations Common Workflows Workflow 1: Startup Ecosystem Analysis Scenario : Analyze fintech startup landscape Steps : Find Startups execute("yc", "search", "search", { "query": "fintech", "batch": "W24,S23,W23,S22" }) → use get_page(cache_key, offset, limit) to paginate through all results Categorize by Focus For each startup: execute("yc", "company", "get", {"slug": company_slug}) Group by: - Payments - Lending - Investment/Trading - Banking - Insurance - B2B fintech tools Or use query_cache to group: query_cache(cache_key, group_by="category") Analyze Patterns Identify: - Hot subcategories (most startups) - Team size distribution - Geographic concentration - Common tech stacks (from job postings) Use query_cache for aggregation: query_cache(cache_key, aggregate={"field": "team_size", "function": "avg"}) Research Traction For promising startups: execute("linkedin", "search", "search_companies", {"keywords": startup_name}) → Check employee growth execute("twitter", "search", "search_users", {"query": startup_name}) → Check social presence and buzz execute("webparser", "parse", "parse", {"url": startup_website}) → Check positioning and features Identify White Spaces Compare: - Overcrowded categories - Underserved segments - Emerging opportunities - Geographic gaps Expected Output : 50-100 startup landscape map Category distribution Funding trends Market gaps identified Competitive intensity by segment Use export_data(cache_key, "csv") to deliver the startup list as a spreadsheet. Workflow 2: Public Company Competitive Analysis Scenario : Research public competitors in cloud infrastructure Steps : Find Companies execute("sec", "search", "search", { "query": "cloud" }) → use get_page(cache_key, offset, limit) to collect up to 50 results Get Financial Data For each company: execute("sec", "document", "get", {"url": document_url}) Extract: - Revenue and growth - Operating margins - R&D spending - Geographic breakdown - Risk factors mentioned Analyze Strategy From 10-K filings: - Business model - Target markets - Competitive advantages - Growth initiatives - Challenges and risks Track Changes Compare year-over-year: - Revenue growth trends - Market focus shifts - New initiatives - Risk factor changes Supplement with Social Intel execute("linkedin", "search", "search_companies", {"keywords": company_name}) → Employee count, hiring patterns execute("linkedin", "company", "get", {"company": company_urn}) → Company details and strategic messaging execute("reddit", "search", "search", {"query": company_name}) → Customer sentiment Use query_cache to filter sentiment: query_cache(cache_key, conditions=[{"field": "text", "operator": "contains", "value": "review"}]) Expected Output : Competitive landscape map Financial benchmarks Strategic positioning Growth trajectories Market opportunities Use export_data(cache_key, "json") for structured competitive data. Workflow 3: Industry Trend Analysis Scenario : Understand AI/ML market evolution Steps : YC Startup Trends execute("yc", "search", "search", { "query": "AI OR machine learning OR artificial intelligence" }) → use get_page(cache_key, offset, limit) to collect up to 200 results Group by batch to see: - Trend over time - Focus area shifts - Team size changes query_cache(cache_key, group_by="batch", aggregate={"field": "id", "function": "count"}) Public Market Signals execute("sec", "search", "search", { "query": "artificial intelligence" }) → use get_page(cache_key, offset, limit) to collect up to 50 results Check 10-K mentions of: - "AI" or "machine learning" frequency - AI-related investments - AI revenue segments Community Sentiment execute("reddit", "search", "search", { "query": "AI trends 2026" }) → use get_page(cache_key, offset, limit) to collect up to 100 results Analyze for: - Excitement vs. concern - Adoption barriers - Use case validation - Technology maturity query_cache(cache_key, sort_by={"field": "score", "order": "desc"}) Professional Discussion execute("linkedin", "post", "search_posts", { "keywords": "artificial intelligence" }) Check: - Industry adoption - Job market signals - Skill requirements - Thought leader opinions Web Intelligence For key AI companies: execute("webparser", "parse", "parse", {"url": website + "/blog"}) → Technology updates, product launches Expected Output : Market evolution timeline Technology adoption curves Sentiment analysis Opportunity identification Risk assessment Use export_data(cache_key, "csv") for trend data tables. MCP Tools Reference (v2) Data Fetching execute(source, category, endpoint, params) -- Universal data fetcher; always returns cache_key Pagination get_page(cache_key, offset, limit) -- Load additional pages from a previous execute() Analysis query_cache(cache_key, conditions, sort_by, aggregate, group_by) -- Filter, sort, and aggregate cached data Export export_data(cache_key, format) -- Export to CSV, JSON, or JSONL; returns download URL Y Combinator Research execute("yc", "search", "search", {"query": ...}) -- Find startups by industry, batch, filters execute("yc", "company", "get", {"slug": ...}) -- Get detailed company profile SEC Research execute("sec", "search", "search", {"query": ...}) -- Find public companies and filings execute("sec", "document", "get", {"url": ...}) -- Get full document content Social Intelligence execute("reddit", "search", "search", {"query": ...}) -- Community insights and sentiment execute("twitter", "search", "search_users", {"query": ...}) -- Real-time market pulse execute("linkedin", "post", "search_posts", {"keywords": ...}) -- Professional trends Company Intelligence execute("linkedin", "search", "search_companies", {"keywords": ...}) -- Find companies execute("linkedin", "company", "get", {"company": ...}) -- Company details execute("webparser", "parse", "parse", {"url": ...}) -- Extract website data Market Discovery Use discover(source, category) to explore available endpoints for any source execute("webparser", "parse", "parse", {"url": ...}) -- Scrape any URL for market data Note : Crunchbase endpoints are disabled in v2. Use LinkedIn company search and Y Combinator data as alternatives for company research. Market Analysis Frameworks TAM/SAM/SOM Analysis : Total Addressable Market (TAM):