exa-search

name	exa-search
description	Neural search via Exa MCP for web, code, and company research. Use when the user needs web search, code examples, company intel, people lookup, or AI-powered deep research with Exa's neural search engine.
origin	ECC

Exa Search

Neural search for web content, code, companies, and people via the Exa MCP server.

When to Activate

• User needs current web information or news
• Searching for code examples, API docs, or technical references
• Researching companies, competitors, or market players
• Finding professional profiles or people in a domain
• Running background research for any development task
• User says "search for", "look up", "find", or "what's the latest on"

MCP Requirement

Exa MCP server must be configured. Add to ~/.claude.json:

"exa-web-search": {
  "command": "npx",
  "args": ["-y", "exa-mcp-server"],
  "env": { "EXA_API_KEY": "YOUR_EXA_API_KEY_HERE" }
}

Get an API key at exa.ai.

Core Tools

web_search_exa

General web search for current information, news, or facts.

web_search_exa(query: "latest AI developments 2026", numResults: 5)

Parameters:

Param	Type	Default	Notes
query	string	required	Search query
numResults	number	8	Number of results

web_search_advanced_exa

Filtered search with domain and date constraints.

web_search_advanced_exa(
  query: "React Server Components best practices",
  numResults: 5,
  includeDomains: ["github.com", "react.dev"],
  startPublishedDate: "2025-01-01"
)

Parameters:

Param	Type	Default	Notes
query	string	required	Search query
numResults	number	8	Number of results
includeDomains	string[]	none	Limit to specific domains
excludeDomains	string[]	none	Exclude specific domains
startPublishedDate	string	none	ISO date filter (start)
endPublishedDate	string	none	ISO date filter (end)

get_code_context_exa

Find code examples and documentation from GitHub, Stack Overflow, and docs sites.

get_code_context_exa(query: "Python asyncio patterns", tokensNum: 3000)

Parameters:

Param	Type	Default	Notes
query	string	required	Code or API search query
tokensNum	number	5000	Content tokens (1000-50000)

company_research_exa

Research companies for business intelligence and news.

company_research_exa(companyName: "Anthropic", numResults: 5)

people_search_exa

Find professional profiles and bios.

people_search_exa(query: "AI safety researchers at Anthropic", numResults: 5)

crawling_exa

Extract full page content from a URL.

crawling_exa(url: "https://example.com/article", tokensNum: 5000)

deep_researcher_start / deep_researcher_check

Start an AI research agent that runs asynchronously.

deep_researcher_start(query: "comprehensive analysis of AI code editors in 2026")
result = deep_researcher_check(researchId: "<id from start>")

Usage Patterns

Quick Lookup

web_search_exa(query: "Node.js 22 new features", numResults: 3)

Code Research

get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)

Company Due Diligence

company_research_exa(companyName: "Vercel", numResults: 5)
web_search_advanced_exa(query: "Vercel funding valuation 2026", numResults: 3)

Technical Deep Dive

deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... later ...
results = deep_researcher_check(researchId: "<id>")

Tips

• Use web_search_exa for broad queries, web_search_advanced_exa for filtered results
• Lower tokensNum (1000-2000) for focused code snippets, higher (5000+) for comprehensive context
• Combine company_research_exa with web_search_advanced_exa for thorough company analysis
• Use crawling_exa to get full content from specific URLs found in search results
• deep_researcher_start is best for comprehensive topics that benefit from AI synthesis

Related Skills

• deep-research — Full research workflow using firecrawl + exa together
• market-research — Business-oriented research with decision frameworks
• external-scout-and-plan — External research + implementation planning (uses exa-search)