exa-search

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:

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

Fallback:

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