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:

1. execute(source, category, endpoint, params) -- fetch data (returns first page + cache_key)
2. get_page(cache_key, offset, limit) -- paginate through remaining results
3. query_cache(cache_key, conditions, sort_by, aggregate, group_by) -- filter/sort/aggregate cached data without new API calls
4. 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:

1. 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

2. 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")

3. 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"})

4. 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

5. 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:

1. Find Companies

execute("sec", "search", "search", {
  "query": "cloud"
})
→ use get_page(cache_key, offset, limit) to collect up to 50 results

2. 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

3. Analyze Strategy

From 10-K filings:
- Business model
- Target markets
- Competitive advantages
- Growth initiatives
- Challenges and risks

4. Track Changes

Compare year-over-year:
- Revenue growth trends
- Market focus shifts
- New initiatives
- Risk factor changes

5. 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:

1. 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"})

2. 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

3. 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"})

4. Professional Discussion

execute("linkedin", "post", "search_posts", {
  "keywords": "artificial intelligence"
})

Check:
- Industry adoption
- Job market signals
- Skill requirements
- Thought leader opinions

5. 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):
- Count YC companies in category x avg market size
- SEC filing market size mentions
- Industry reports via execute("webparser", "parse", "parse", {"url": report_url})

Serviceable Addressable Market (SAM):
- Filter by geography, segment using query_cache()
- LinkedIn company search by ICP
- YC companies by batch/stage

Serviceable Obtainable Market (SOM):
- Realistic capture based on competition
- Competitive analysis via LinkedIn/social
- Market share indicators

Porter's Five Forces:

Using anysite v2 data:

1. Competitive Rivalry:
   - YC startups in space
   - LinkedIn company counts
   - Social mention volume

2. Threat of New Entrants:
   - Recent YC batches
   - Funding announcements
   - Talent movement (LinkedIn)

3. Supplier Power:
   - Technology dependencies
   - Integration partners

4. Buyer Power:
   - Customer reviews (Reddit)
   - Pricing transparency
   - Switching costs mentioned

5. Threat of Substitutes:
   - Alternative solutions
   - Adjacent markets

Output Formats

Chat Summary:

• Key market insights
• Competitive landscape summary
• Opportunity identification
• Strategic recommendations

CSV Export (via export_data(cache_key, "csv")):

• Company list with metrics
• Market segmentation data
• Trend indicators

JSON Export (via export_data(cache_key, "json")):

• Complete research data
• Time-series analysis
• Cross-platform correlations

Reference Documentation

• RESEARCH_METHODS.md - Market research methodologies, analysis frameworks, and data synthesis techniques

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