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.
npx skills add https://github.com/loulanyue/awesome-claude-notes --skill exa-search
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"],
"env": { "EXA_API_KEY": "YOUR_EXA_API_KEY_HERE" }
}
Get an API key at exa.ai.
This repo's current Exa setup documents the tool surface exposed here: web_search_exa and get_code_context_exa.
If your Exa server exposes additional tools, verify their exact names before depending on them in docs or prompts.
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 |
| type | string | auto | Search mode |
| livecrawl | string | fallback | Prefer live crawling when needed |
| category | string | none | Optional focus such as company or research paper |
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) |
web_search_exa(query: "Node.js 22 new features", numResults: 3)
get_code_context_exa(query: "Rust error handling patterns Result type", tokensNum: 3000)
web_search_exa(query: "Vercel funding valuation 2026", numResults: 3, category: "company")
web_search_exa(query: "site:linkedin.com/in AI safety researchers Anthropic", numResults: 5)
web_search_exa(query: "WebAssembly component model status and adoption", numResults: 5)
get_code_context_exa(query: "WebAssembly component model examples", tokensNum: 4000)
web_search_exa for current information, company lookups, and broad discoverysite:, quoted phrases, and intitle: to narrow resultstokensNum (1000-2000) for focused code snippets, higher (5000+) for comprehensive contextget_code_context_exa when you need API usage or code examples rather than general web pagesdeep-research — Full research workflow using firecrawl + exa togethermarket-research — Business-oriented research with decision frameworksUse when the user is doing AI/ML work in a scientific domain such as biology, chemistry, physics, astronomy, climate, genomics, materials, medicine, ecology, energy, engineering, math, drug discovery, protein design, weather modeling, theorem proving, single-cell, or PDE solving. Hugging Science is a curated catalog of scientific datasets, models, blog posts, and interactive Spaces. This skill helps discover and use resources via `datasets`, `transformers`, the HF Inference API, `gradio_client`, and methodology citations.
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Semantic search, similar content discovery, and structured research using Exa API. Use when you need semantic/embeddings-based search, finding similar content, or searching by category (company, people, research papers, etc.).
Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. Tools: Tavily Search, Exa Search, Exa Answer, Claude, GPT-4, Gemini via OpenRouter. Capabilities: research, fact-checking, grounded responses, knowledge retrieval. Use for: AI agents, research assistants, fact-checkers, knowledge bases. Triggers: rag, retrieval augmented generation, grounded ai, search and answer, research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
| Build RAG (Retrieval Augmented Generation) pipelines with web search and LLMs. research agent, fact checking, knowledge retrieval, ai research, search + llm, web grounded, perplexity alternative, ai with sources, citation, research pipeline
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
Web search and content extraction with Tavily and Exa via inference.sh CLI. Apps: Tavily Search, Tavily Extract, Exa Search, Exa Answer, Exa Extract. Capabilities: AI-powered search, content extraction, direct answers, research. Use for: research, RAG pipelines, fact-checking, content aggregation, agents. Triggers: web search, tavily, exa, search api, content extraction, research, internet search, ai search, search assistant, web scraping, rag, perplexity alternative
The protocol behind every investigation skill. Use when AI research must proceed without you: search-plan gate, Fact/Inference/Assumption labels, confidence stacking, diffable outputs.
Take loulanyue/awesome-claude-notes-exa-search from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.