| name | exa-search |
| description | Use when searching for concepts, ideas, or similar content without exact keywords; when user asks "find similar to...", needs semantic discovery, research across perspectives, or explicitly mentions exa |
Exa Semantic Search
Overview
Exa.ai provides neural semantic search optimized for AI consumption. Use when meaning matters more than keywords.
Decision Flowchart
digraph exa_decision {
rankdir=TB;
node [shape=box];
start [label="Need to search the web?" shape=diamond];
known_url [label="Do you have\na specific URL?" shape=diamond];
semantic [label="Is this semantic/conceptual?\n(meaning > keywords)" shape=diamond];
recent [label="Need very recent\nnews/events?" shape=diamond];
code [label="Is this a coding/\nAPI question?" shape=diamond];
webfetch [label="WebFetch" shape=box style=filled fillcolor=lightblue];
websearch [label="WebSearch" shape=box style=filled fillcolor=lightgreen];
exa_web [label="mcp__exa__web_search_exa" shape=box style=filled fillcolor=lightyellow];
exa_code [label="mcp__exa__get_code_context_exa" shape=box style=filled fillcolor=lightyellow];
start -> known_url [label="yes"];
start -> known_url [label="no" style=invis];
known_url -> webfetch [label="yes"];
known_url -> semantic [label="no"];
semantic -> code [label="yes"];
semantic -> recent [label="no"];
code -> exa_code [label="yes"];
code -> exa_web [label="no"];
recent -> websearch [label="yes"];
recent -> websearch [label="no"];
}
Quick Reference
| 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 |
Tool Usage
mcp__exa__web_search_exa
query: "semantic query describing concepts"
numResults: 8 (default, adjust as needed)
type: "auto" | "fast" | "deep"
mcp__exa__get_code_context_exa
query: "React useState hook examples" | "Express middleware patterns"
tokensNum: 5000 (default, 1000-50000 range)
Integration Patterns
Discovery + Extraction:
- Exa finds relevant sources semantically
- WebFetch extracts full content from best URLs
Multi-perspective research:
- Exa: "academic perspectives on X"
- Exa: "industry implementation of X"
- Exa: "critiques of X"
- Synthesize
Fallback:
- Try Exa for semantic search
- 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
- Switch search type:
auto vs fast vs deep
- Rephrase: more semantic/descriptive
- Add domain filters via
allowed_domains
- Fall back to WebSearch for keyword matching
