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
:
json
"exa-web-search": {
"command": "npx",
"args": [
"-y",
"exa-mcp-server",
"tools=web_search_exa,web_search_advanced_exa,get_code_context_exa,crawling_exa,company_research_exa,people_search_exa,deep_researcher_start,deep_researcher_check"
],
"env": { "EXA_API_KEY": "YOUR_EXA_API_KEY_HERE" }
}
Get an API key at
exa.ai.
If you omit the
argument, only a smaller default tool set may be enabled.
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 |
|---|
| string | required | Search query |
| 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 |
|---|
| string | required | Search query |
| number | 8 | Number of results |
| string[] | none | Limit to specific domains |
| string[] | none | Exclude specific domains |
| string | none | ISO date filter (start) |
| 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 |
|---|
| string | required | Code or API search query |
| number | 5000 | Content tokens (1000-50000) |
company_research_exa
Research companies for business intelligence and news.
company_research_exa(companyName: "Anthropic", numResults: 5)
Parameters:
| Param | Type | Default | Notes |
|---|
| string | required | Company name |
| number | 5 | Number of results |
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)
Parameters:
| Param | Type | Default | Notes |
|---|
| string | required | URL to extract |
| number | 5000 | Content tokens |
deep_researcher_start / deep_researcher_check
Start an AI research agent that runs asynchronously.
# Start research
deep_researcher_start(query: "comprehensive analysis of AI code editors in 2026")
# Check status (returns results when complete)
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
# Start async research
deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... do other work ...
deep_researcher_check(researchId: "<id>")
Tips
- Use for broad queries, for filtered results
- Lower (1000-2000) for focused code snippets, higher (5000+) for comprehensive context
- Combine with for thorough company analysis
- Use to get full content from specific URLs found in search results
- is best for comprehensive topics that benefit from AI synthesis
Related Skills
- — Full research workflow using firecrawl + exa together
- — Business-oriented research with decision frameworks