모델 컨텍스트 프로토콜(MCP) 서버를 통해 클로드와 같은 AI 비서가 Exa AI Search API를 사용하여 웹 검색을 수행할 수 있습니다. 이 설정을 통해 AI 모델은 안전하고 통제된 방식으로 실시간 웹 정보를 얻을 수 있습니다.
데모 영상 https://www.loom.com/share/ac676f29664e4c6cb33a2f0a63772038?sid=0e72619f-5bfc-415d-a705-63d326373f60
모델 컨텍스트 프로토콜(MCP)은 Claude Desktop과 같은 AI 앱이 외부 도구 및 데이터 소스에 연결할 수 있도록 하는 시스템입니다. AI 어시스턴트가 사용자의 제어권을 유지하면서 로컬 서비스 및 API를 사용할 수 있는 명확하고 안전한 방법을 제공합니다.
Related MCP server: Exa MCP Server
Exa MCP 서버:
Exa의 강력한 검색 API를 사용하여 AI 도우미가 웹 검색을 수행할 수 있도록 합니다.
제목, URL, 콘텐츠 스니펫을 포함한 구조화된 검색 결과를 제공합니다.
속도 제한 및 오류 사례를 정상적으로 처리합니다.
시작하기 전에 다음 사항을 확인하세요.
Node.js (v18 이상)
Claude Desktop 설치됨
Git 설치됨
다음을 실행하여 Node.js 설치를 확인할 수 있습니다.
지엑스피1
npm install -g exa-mcp-serverSmithery를 통해 Claude Desktop용 Exa MCP 서버를 자동으로 설치하려면:
npx -y @smithery/cli install exa --client claude저장소를 복제합니다.
git clone https://github.com/exa-labs/exa-mcp-server.git
cd exa-mcp-server종속성 설치:
npm install --save axios dotenv프로젝트를 빌드하세요:
npm run build글로벌 링크를 만듭니다(이렇게 하면 서버를 어디서나 실행할 수 있습니다):
npm linkClaude Desktop 앱 설정 내부에서 claude_desktop_config.json을 찾을 수 있습니다.
Claude Desktop 앱을 열고 왼쪽 상단 메뉴 표시줄에서 개발자 모드를 활성화하세요.
활성화되면 설정(왼쪽 상단 메뉴 표시줄에서도 가능)을 열고 개발자 옵션으로 이동하면 '구성 편집' 버튼이 있습니다. 이 버튼을 클릭하면 claude_desktop_config.json 파일이 열리고 필요한 편집 작업을 할 수 있습니다.
또는 (터미널에서 claude_desktop_config.json을 열려는 경우)
Claude Desktop 구성을 엽니다.
code ~/Library/Application\ Support/Claude/claude_desktop_config.jsonClaude Desktop 구성을 엽니다.
code %APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"exa": {
"command": "npx",
"args": ["/path/to/exa-mcp-server/build/index.js"],
"env": {
"EXA_API_KEY": "your-api-key-here"
}
}
}
}your-api-key-here dashboard.exa.ai/api-keys 의 실제 Exa API 키로 바꾸세요.
변경 사항을 적용하려면 다음을 수행하세요.
Claude Desktop을 완전히 종료합니다(창을 닫는 것만이 아닙니다)
Claude Desktop을 다시 시작하세요
Exa 서버가 연결되었는지 확인하려면 🔌 아이콘을 찾으세요.
설정이 완료되면 Claude에게 웹 검색을 요청하세요. 다음은 몇 가지 프롬프트 예시입니다.
Can you search for recent developments in quantum computing?Search for and summarize the latest news about artificial intelligence startups in new york.Find and analyze recent research papers about climate change solutions.서버는 다음을 수행합니다.
검색 요청을 처리합니다
Exa API 쿼리
Claude에게 포맷된 결과를 반환합니다.
나중에 참조할 수 있도록 검색 내용을 캐시합니다.
웹 검색 도구 : Claude가 자연어 쿼리를 사용하여 웹을 검색할 수 있도록 합니다.
오류 처리 : API 오류 및 속도 제한을 우아하게 처리합니다.
유형 안전성 : 적절한 유형 검사를 통한 전체 TypeScript 구현
서버를 찾을 수 없습니다
npm 링크가 올바르게 설정되었는지 확인하세요
Claude Desktop 구성 구문 확인
Node.js가 제대로 설치되었는지 확인하세요
API 키 문제
Exa API 키가 유효한지 확인하세요
Claude Desktop 구성에서 API 키가 올바르게 설정되었는지 확인하세요.
API 키 주위에 공백이나 따옴표가 없는지 확인하세요.
연결 문제
Claude Desktop을 완전히 다시 시작하세요
Claude Desktop 로그 확인:
# macOS
tail -n 20 -f ~/Library/Logs/Claude/mcp*.log문제가 발생하면 MCP 문서를 검토하세요.
강력한 검색 API를 제공하는 Exa AI
MCP 사양을 위한 모델 컨텍스트 프로토콜
클로드 데스크탑을 위한 인류학
Search and get relevant context for any programming task. Exa-code has the highest quality and freshest context for libraries, SDKs, and APIs. Use this tool for ANY question or task for related to programming. RULE: when the user's query contains exa-code or anything related to code, you MUST use this tool.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query to find relevant context for APIs, Libraries, and SDKs. For example, 'React useState hook examples', 'Python pandas dataframe filtering', 'Express.js middleware', 'Next js partial prerendering configuration' | |
| tokensNum | No | Number of tokens to return (1000-50000). Default is 5000 tokens. Adjust this value based on how much context you need - use lower values for focused queries and higher values for comprehensive documentation. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds value by emphasizing 'highest quality and freshest context' and the programming domain focus, but doesn't disclose additional behavioral traits like rate limits, authentication needs, or response format details. No contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core purpose and usage rule, but includes some redundancy (e.g., repeating 'exa-code' emphasis). Sentences are generally purposeful, though the 'RULE' phrasing could be more integrated. Overall efficient but with minor verbosity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity (2 parameters, no output schema), annotations cover safety aspects, and the description provides clear purpose and usage rules. However, it lacks details on response structure or error handling, which would enhance completeness for a search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing full documentation for both parameters. The description doesn't add meaningful parameter semantics beyond what's in the schema, such as explaining query formulation strategies or token usage trade-offs. Baseline score of 3 is appropriate given the comprehensive schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search and get relevant context for any programming task' with specific focus on 'libraries, SDKs, and APIs.' It distinguishes from the sibling tool 'web_search_exa' by specifying programming-related content, though it doesn't explicitly contrast their differences beyond domain focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Use this tool for ANY question or task related to programming' and includes a mandatory rule: 'when the user's query contains exa-code or anything related to code, you MUST use this tool.' This clearly defines when to use it versus alternatives, though it doesn't specify when NOT to use it for non-programming queries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the web using Exa AI - performs real-time web searches and can scrape content from specific URLs. Supports configurable result counts and returns the content from the most relevant websites.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Websearch query | |
| numResults | No | Number of search results to return (default: 8) | |
| livecrawl | No | Live crawl mode - 'fallback': use live crawling as backup if cached content unavailable, 'preferred': prioritize live crawling (default: 'fallback') | |
| type | No | Search type - 'auto': balanced search (default), 'fast': quick results, 'deep': comprehensive search | |
| contextMaxCharacters | No | Maximum characters for context string optimized for LLMs (default: 10000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety and idempotency. The description adds valuable behavioral context beyond annotations: it mentions real-time web searches, scraping from specific URLs, configurable result counts, and returning content from relevant websites. This provides useful operational details without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately sized at two sentences, front-loading the core purpose. Every sentence adds value: the first defines the tool's function, and the second elaborates on features and output. There's no wasted text, though it could be slightly more structured for optimal clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's moderate complexity, rich annotations (covering safety and idempotency), and 100% schema coverage, the description is reasonably complete. It explains the tool's function and key features. The lack of an output schema is a minor gap, but the description mentions return content, partially compensating. For a read-only search tool, this provides adequate context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents all 5 parameters. The description adds minimal parameter semantics beyond the schema, mentioning only 'configurable result counts' (referencing numResults) and 'content from the most relevant websites' (hinting at query relevance). Since the schema does the heavy lifting, the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search the web using Exa AI - performs real-time web searches and can scrape content from specific URLs.' It specifies the verb (search/scrape) and resource (web/URLs), making the function unambiguous. However, it doesn't explicitly differentiate from its sibling 'get_code_context_exa' beyond mentioning general web search capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for web searches and content scraping, but provides no explicit guidance on when to use this tool versus its sibling 'get_code_context_exa' or other alternatives. It mentions configurable result counts and relevance, which suggests some context, but lacks clear when/when-not directives or named alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0company_research_exacrawling_exadeep_researcher_checkdeep_researcher_startget_code_context_exalinkedin_search_exaweb_search_exa5 fields changedInput schema / properties / contextMaxCharactersAdded value: +{
+ "description": "Maximum characters for context string optimized for LLMs (default: 10000)",
+ "type": "number"
+}Input schema / properties / livecrawlAdded value: +{
+ "description": "Live crawl mode - 'fallback': use live crawling as backup if cached content unavailable, 'preferred': prioritize live crawling (default: 'fallback')",
+ "enum": [
+ "fallback",
+ "preferred"
+ ],
+ "type": "string"
+}Input schema / properties / numResults / descriptionPrevious value: -"Number of search results to return (default: 5)"New value: +"Number of search results to return (default: 8)"Input schema / properties / query / descriptionPrevious value: -"Search query"New value: +"Websearch query"Input schema / properties / typeAdded value: +{
+ "description": "Search type - 'auto': balanced search (default), 'fast': quick results, 'deep': comprehensive search",
+ "enum": [
+ "auto",
+ "fast",
+ "deep"
+ ],
+ "type": "string"
+}company_research_exacrawling_exadeep_researcher_checkdeep_researcher_startlinkedin_search_exaweb_search_exaScored across 2 tools
The two tools have clearly distinct purposes: get_code_context_exa is specialized for programming-related searches with high-quality code context, while web_search_exa is for general web searches and URL scraping. There is no overlap in functionality, making it easy for an agent to choose the right tool based on the query type.
Both tools follow a consistent naming pattern: they use snake_case and start with a verb (get, search) followed by a noun (code_context, web), with a suffix (_exa) indicating the server. This uniformity makes the tool set predictable and easy to understand.
With only 2 tools, the server feels thin for its apparent scope of providing search capabilities via Exa AI. While the tools cover code-specific and general web searches, the lack of additional tools (e.g., for filtering, advanced queries, or other Exa features) limits functionality and suggests an incomplete surface for a search-oriented server.
The tool set is severely incomplete for a search server. It lacks essential operations such as configuring search parameters beyond result counts, handling pagination, saving or managing search history, or accessing other Exa AI features. This creates significant gaps that could lead to agent failures when more complex search tasks are required.
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