Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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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.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Neural search for web content, code, companies, and people via the Exa MCP server.
Exa MCP server must be configured. Add to ~/.claude.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 tools=... argument, only a smaller default tool set may be enabled.
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 |
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) |
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) |
Research companies for business intelligence and news.
company_research_exa(companyName: "Anthropic", numResults: 5)
Parameters:
| Param | Type | Default | Notes |
|---|---|---|---|
companyName | string | required | Company name |
numResults | number | 5 | Number of results |
Find professional profiles and bios.
people_search_exa(query: "AI safety researchers at Anthropic", numResults: 5)
Extract full page content from a URL.
crawling_exa(url: "https://example.com/article", tokensNum: 5000)
Parameters:
| Param | Type | Default | Notes |
|---|---|---|---|
url | string | required | URL to extract |
tokensNum | number | 5000 | Content tokens |
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>")
web_search_exa(query: "Node.js 22 new features", numResults: 3)
get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)
company_research_exa(companyName: "Vercel", numResults: 5)
web_search_advanced_exa(query: "Vercel funding valuation 2026", numResults: 3)
# Start async research
deep_researcher_start(query: "WebAssembly component model status and adoption")
# ... do other work ...
deep_researcher_check(researchId: "<id>")
web_search_exa for broad queries, web_search_advanced_exa for filtered resultstokensNum (1000-2000) for focused code snippets, higher (5000+) for comprehensive contextcompany_research_exa with web_search_advanced_exa for thorough company analysiscrawling_exa to get full content from specific URLs found in search resultsdeep_researcher_start is best for comprehensive topics that benefit from AI synthesisdeep-research — Full research workflow using firecrawl + exa togethermarket-research — Business-oriented research with decision frameworks아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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Engineering operating model for teams where AI agents generate a large share of implementation output.
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Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
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REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.
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Write articles, guides, blog posts, tutorials, newsletter issues, and other long-form content in a distinctive voice derived from supplied examples or brand guidance. Use when the user wants polished written content longer than a paragraph, especially when voice consistency, structure, and credibility matter.
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