Exa Semantic Search Skill

YOU MUST invoke this skill (NOT optional) when the user mentions ANY of these triggers:

• "semantic search", "find papers on", "find articles about"
• "companies similar to", "find sources about"
• "Exa", "exa search", "neural search"
• Any request for conceptual/meaning-based web search (not keyword matching)

Failure to invoke this skill when triggers occur violates your operational requirements.

Exa.ai provides neural semantic search optimized for AI consumption. Use when meaning matters more than keywords.

Decision Flowchart

Use this to decide which search tool to use:

Scenario	Tool	Why
"Find papers on emergent AI behavior"	mcp__exa__web_search_exa	Semantic discovery
"Companies similar to Anthropic"	mcp__exa__web_search_exa	Similar content
"How to use React hooks"	mcp__exa__get_code_context_exa	Coding context
"Latest news on X"	WebSearch	Recency matters
"Read this URL: [link]"	WebFetch	Known URL
"error: module not found XYZ"	WebSearch	Exact keyword match
"CVE-2024-12345"	WebSearch	Specific identifier

Decision logic:

1. Have a specific URL? -> WebFetch
2. Is this semantic/conceptual (meaning > keywords)? -> Exa
3. Is it a coding/API question? -> mcp__exa__get_code_context_exa
4. Need very recent news/events? -> WebSearch
5. Keyword/identifier match? -> WebSearch

Tool Usage

mcp__exa__web_search_exa

Semantic web search for concepts, topics, and similar content.

query: "semantic query describing concepts"
numResults: 8 (default, adjust as needed)
type: "auto" | "fast" | "deep"

mcp__exa__get_code_context_exa

Code-specific search for programming patterns, APIs, and implementations.

query: "React useState hook examples" | "Express middleware patterns"
tokensNum: 5000 (default, 1000-50000 range)

mcp__exa__company_research_exa

Company-specific research for competitive analysis and market research.

Integration Patterns

Discovery + Extraction

1. Exa finds relevant sources semantically
2. WebFetch extracts full content from best URLs

Multi-Perspective Research

1. Exa: "academic perspectives on X"
2. Exa: "industry implementation of X"
3. Exa: "critiques of X"
4. Synthesize findings

Fallback Strategy

1. Try Exa for semantic search
2. If results poor, fall back to WebSearch with keywords

Anti-Patterns

Don't	Do Instead
"python pandas filter dataframe"	Use WebSearch (keyword query)
Run 10 similar queries	Consolidate into 2-3 well-crafted queries
"what is React"	Use knowledge or WebSearch
"breaking news today"	Use WebSearch

When Results Are Poor

1. Switch search type: auto vs fast vs deep
2. Rephrase: more semantic/descriptive
3. Add domain filters via allowed_domains
4. Fall back to WebSearch for keyword matching