wehub-resource-sync
6.9 KiB
6.9 KiB
Tavily MCP Server
Purpose: Web search and real-time information retrieval for research and current events
Triggers
- Web search requirements beyond Claude's knowledge cutoff
- Current events, news, and real-time information needs
- Market research and competitive analysis tasks
- Technical documentation not in training data
- Academic research requiring recent publications
- Fact-checking and verification needs
- Deep research investigations requiring multi-source analysis
/sc:researchcommand activation
Choose When
- Over WebSearch: When you need structured search with advanced filtering
- Over WebFetch: When you need multi-source search, not single page extraction
- For research: Comprehensive investigations requiring multiple sources
- For current info: Events, updates, or changes after knowledge cutoff
- Not for: Simple questions answerable from training, code generation, local file operations
Works Best With
- Sequential: Tavily provides raw information → Sequential analyzes and synthesizes
- Playwright: Tavily discovers URLs → Playwright extracts complex content
- Context7: Tavily searches for updates → Context7 provides stable documentation
- Serena: Tavily performs searches → Serena stores research sessions
Configuration
Requires TAVILY_API_KEY environment variable from https://app.tavily.com
Search Capabilities
- Web Search: General web searches with ranking algorithms
- News Search: Time-filtered news and current events
- Academic Search: Scholarly articles and research papers
- Domain Filtering: Include/exclude specific domains
- Content Extraction: Full-text extraction from search results
- Freshness Control: Prioritize recent content
- Multi-Round Searching: Iterative refinement based on gaps
Examples
"latest TypeScript features 2024" → Tavily (current technical information)
"OpenAI GPT updates this week" → Tavily (recent news and updates)
"quantum computing breakthroughs 2024" → Tavily (recent research)
"best practices React Server Components" → Tavily (current best practices)
"explain recursion" → Native Claude (general concept explanation)
"write a Python function" → Native Claude (code generation)
Search Patterns
Basic Search
Query: "search term"
→ Returns: Ranked results with snippets
Domain-Specific Search
Query: "search term"
Domains: ["arxiv.org", "github.com"]
→ Returns: Results from specified domains only
Time-Filtered Search
Query: "search term"
Recency: "week" | "month" | "year"
→ Returns: Recent results within timeframe
Deep Content Search
Query: "search term"
Extract: true
→ Returns: Full content extraction from top results
Quality Optimization
- Query Refinement: Iterate searches based on initial results
- Source Diversity: Ensure multiple perspectives in results
- Credibility Filtering: Prioritize authoritative sources
- Deduplication: Remove redundant information across sources
- Relevance Scoring: Focus on most pertinent results
Integration Flows
Research Flow
1. Tavily: Initial broad search
2. Sequential: Analyze and identify gaps
3. Tavily: Targeted follow-up searches
4. Sequential: Synthesize findings
5. Serena: Store research session
Fact-Checking Flow
1. Tavily: Search for claim verification
2. Tavily: Find contradicting sources
3. Sequential: Analyze evidence
4. Report: Present balanced findings
Competitive Analysis Flow
1. Tavily: Search competitor information
2. Tavily: Search market trends
3. Sequential: Comparative analysis
4. Context7: Technical comparisons
5. Report: Strategic insights
Deep Research Flow (DR Agent)
1. Planning: Decompose research question
2. Tavily: Execute planned searches
3. Analysis: Assess URL complexity
4. Routing: Simple → Tavily extract | Complex → Playwright
5. Synthesis: Combine all sources
6. Iteration: Refine based on gaps
Advanced Search Strategies
Multi-Hop Research
Initial_Search:
query: "core topic"
depth: broad
Follow_Up_1:
query: "entities from initial"
depth: targeted
Follow_Up_2:
query: "relationships discovered"
depth: deep
Synthesis:
combine: all_findings
resolve: contradictions
Adaptive Query Generation
Simple_Query:
- Direct search terms
- Single concept focus
Complex_Query:
- Multiple search variations
- Boolean operators
- Domain restrictions
- Time filters
Iterative_Query:
- Start broad
- Refine based on results
- Target specific gaps
Source Credibility Assessment
High_Credibility:
- Academic institutions
- Government sources
- Established media
- Official documentation
Medium_Credibility:
- Industry publications
- Expert blogs
- Community resources
Low_Credibility:
- User forums
- Social media
- Unverified sources
Performance Considerations
Search Optimization
- Batch similar searches together
- Cache search results for reuse
- Prioritize high-value sources
- Limit depth based on confidence
Rate Limiting
- Maximum searches per minute
- Token usage per search
- Result caching duration
- Parallel search limits
Cost Management
- Monitor API usage
- Set budget limits
- Optimize query efficiency
- Use caching effectively
Integration with DR Agent Architecture
Planning Strategy Support
Planning_Only:
- Direct query execution
- No refinement needed
Intent_Planning:
- Clarify search intent
- Generate focused queries
Unified:
- Present search plan
- Adjust based on feedback
Multi-Hop Execution
Hop_Management:
- Track search genealogy
- Build on previous results
- Detect circular references
- Maintain hop context
Self-Reflection Integration
Quality_Check:
- Assess result relevance
- Identify coverage gaps
- Trigger additional searches
- Calculate confidence scores
Case-Based Learning
Pattern_Storage:
- Successful query formulations
- Effective search strategies
- Domain preferences
- Time filter patterns
Error Handling
Common Issues
- API key not configured
- Rate limit exceeded
- Network timeout
- No results found
- Invalid query format
Fallback Strategies
- Use native WebSearch
- Try alternative queries
- Expand search scope
- Use cached results
- Simplify search terms
Best Practices
Query Formulation
- Start with clear, specific terms
- Use quotes for exact phrases
- Include relevant keywords
- Specify time ranges when needed
- Use domain filters strategically
Result Processing
- Verify source credibility
- Cross-reference multiple sources
- Check publication dates
- Identify potential biases
- Extract key information
Integration Workflow
- Plan search strategy
- Execute initial searches
- Analyze results
- Identify gaps
- Refine and iterate
- Synthesize findings
- Store valuable patterns