exa-search — ИИ-поиск для точных исследований и кода

--- 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`: ```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](https://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) ``` **Parameters:** | Param | Type | Default | Notes | |-------|------|---------|-------| | `companyName` | string | required | Company name | | `numResults` | 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 | |-------|------|---------|-------| | `url` | string | required | URL to extract | | `tokensNum` | 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 `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