Web Research

Search the web, read pages, and synthesize findings using Tavily (search/research) and Firecrawl (scrape/crawl).

Tool Routing

Pick the right tool for the job:

When Tavily vs Firecrawl

Default to Tavily for searching and quick reads. It's faster and returns results in-context.

Escalate to Firecrawl when:

• You need full page content as clean markdown (not just snippets)
• The page is large and results should go to a file
• Content requires browser interaction (clicks, scrolls, form fills)
• You need to crawl multiple pages from one site
• Tavily extraction fails on a protected or complex page

Use both together for research workflows:

1. Tavily search to discover relevant URLs
2. Firecrawl scrape to deep-read the best sources

Workflow

Standard Research Flow

1. SEARCH    → Tavily search to discover sources and get initial snippets
2. READ      → Firecrawl scrape (or Tavily extract) the most relevant pages
3. FOLLOW-UP → Search again with refined queries based on what you learned
4. SYNTHESIZE → Summarize findings with source attribution

Quick Question (simple factual lookup)

tavily_search("query", search_depth="basic", max_results=5)
→ Answer from snippets, cite sources

Topic Deep-Dive (broad or complex question)

tavily_research(input="detailed research question", model="auto")
→ Returns comprehensive synthesis from multiple sources

Use model="mini" for narrow questions with few subtopics, model="pro" for broad questions.

Competitive / Site Analysis

firecrawl map "https://competitor.com" --search "pricing"  → find relevant pages
firecrawl scrape "https://competitor.com/pricing"          → extract content
tavily_search("competitor name vs alternatives")           → market context

Reading a Specific URL

If the user gives you a URL:

• Short page / quick read: tavily_extract with the URL
• Long page / need full content: firecrawl scrape "<url>" -o .firecrawl/page.md
• Page behind interactions: firecrawl browser "open <url>" → snapshot → scrape

Tavily Search Tips

# Basic search
tavily_search(query="your question", max_results=5)

# Recent results only
tavily_search(query="topic", time_range="week")

# Specific date range
tavily_search(query="topic", start_date="2025-01-01", end_date="2025-02-01")

# Search specific sites
tavily_search(query="topic", include_domains=["example.com", "docs.example.com"])

# Exclude sites
tavily_search(query="topic", exclude_domains=["reddit.com", "pinterest.com"])

# Thorough search (slower but better)
tavily_search(query="topic", search_depth="advanced", max_results=10)

# Get full page content with results (alternative to scraping)
tavily_search(query="topic", include_raw_content=true)

Firecrawl Output

Write large results to .firecrawl/ with descriptive filenames:

firecrawl scrape "<url>" -o .firecrawl/competitor-pricing.md
firecrawl search "query" --scrape -o .firecrawl/search-topic.json --json

Never read entire output files at once. Use incremental reads:

wc -l .firecrawl/file.md && head -50 .firecrawl/file.md
grep -n "keyword" .firecrawl/file.md

Synthesis

After gathering sources, synthesize findings for the user:

• Lead with the answer — what did you find?
• Cite sources — include URLs for key claims
• Flag conflicts — note when sources disagree
• Note gaps — what couldn't you find or verify?
• Suggest follow-ups — what else could be explored?

Keep synthesis concise. The user wants insights, not a dump of everything you read.

Parallel Research

When a research question has multiple independent angles, run searches in parallel:

# These can run simultaneously:
tavily_search("competitor A pricing")     # angle 1
tavily_search("competitor B pricing")     # angle 2
tavily_search("industry pricing trends")  # angle 3

For Firecrawl, run parallel scrapes:

firecrawl scrape "<url-1>" -o .firecrawl/1.md &
firecrawl scrape "<url-2>" -o .firecrawl/2.md &
wait