SuperClaude/plugins/superclaude/mcp/MCP_Tavily.md

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:research command 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

1. Start with clear, specific terms
2. Use quotes for exact phrases
3. Include relevant keywords
4. Specify time ranges when needed
5. Use domain filters strategically

Result Processing

1. Verify source credibility
2. Cross-reference multiple sources
3. Check publication dates
4. Identify potential biases
5. Extract key information

Integration Workflow

1. Plan search strategy
2. Execute initial searches
3. Analyze results
4. Identify gaps
5. Refine and iterate
6. Synthesize findings
7. Store valuable patterns