Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
日本語の概要は準備中です。原文の説明を表示しています。
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.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
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 frameworksまだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
日本語の概要は準備中です。原文の説明を表示しています。
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
日本語の概要は準備中です。原文の説明を表示しています。
Engineering operating model for teams where AI agents generate a large share of implementation output.
日本語の概要は準備中です。原文の説明を表示しています。
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
日本語の概要は準備中です。原文の説明を表示しています。
REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。