A
licenseNot graded
qualityD
maintenanceEnables web content retrieval and semantic search capabilities through the Jina AI API. Provides tools to fetch content from URLs and perform intelligent web searches with natural language queries.
3
MIT
MCP JinaAI Search Server
by spences10
mcp-jinaai-search
⚠️ 공지사항
이 저장소는 더 이상 유지되지 않습니다.
이 도구의 기능은 이제 여러 MCP 도구를 하나의 통합 패키지로 결합한 mcp-omnisearch 에서 사용할 수 있습니다.
대신 mcp-omnisearch를 사용하세요.
Jina.ai의 검색 API를 LLM과 통합하기 위한 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버는 웹에서 LLM 친화적이고 깔끔한 콘텐츠를 검색하도록 최적화된 효율적이고 포괄적인 웹 검색 기능을 제공합니다.
Related MCP server: Jina AI Remote MCP Server
특징
🔍 Jina.ai 검색 API를 통한 고급 웹 검색
🚀 빠르고 효율적인 콘텐츠 검색
📄 구조가 보존된 깔끔한 텍스트 추출
🧠 LLM에 최적화된 콘텐츠
🌐 문서를 포함한 다양한 콘텐츠 유형 지원
🏗️ 모델 컨텍스트 프로토콜 기반
🔄 성능을 위한 구성 가능한 캐싱
🖼️ 선택적 이미지 및 링크 수집
🌍 브라우저 로케일을 통한 현지화 지원
🎯 응답 크기에 대한 토큰 예산 제어
구성
이 서버를 사용하려면 MCP 클라이언트를 통한 구성이 필요합니다. 다음은 다양한 환경에 대한 예시입니다.
클라인 구성
Cline MCP 설정에 다음을 추가하세요.
지엑스피1
WSL 구성을 사용한 Claude Desktop
WSL 환경의 경우 Claude Desktop 구성에 다음을 추가하세요.
{
"mcpServers": {
"jinaai-search": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-search"
]
}
}
}환경 변수
서버에는 다음 환경 변수가 필요합니다.
JINAAI_API_KEY: Jina.ai API 키 (필수)
API
서버는 구성 가능한 매개변수를 사용하여 단일 MCP 도구를 구현합니다.
찾다
Jina.ai Reader를 사용하여 웹을 검색하고 깔끔하고 LLM 친화적인 콘텐츠를 얻으세요. URL과 깔끔한 콘텐츠가 포함된 상위 5개 결과를 제공합니다.
매개변수:
query(문자열, 필수): 검색 쿼리format(문자열, 선택 사항): 응답 형식("json" 또는 "text")입니다. 기본값은 "text"입니다.no_cache(부울, 선택 사항): 최신 결과에 대해 캐시를 사용하지 않습니다. 기본값은 false입니다.token_budget(숫자, 선택 사항): 이 요청에 대한 최대 토큰 수browser_locale(문자열, 선택 사항): 콘텐츠를 렌더링하기 위한 브라우저 로캘stream(부울, 선택 사항): 대용량 페이지에 대해 스트림 모드를 활성화합니다. 기본값은 false입니다.gather_links(부울, 선택 사항): 응답 끝에서 모든 링크를 수집합니다. 기본값은 false입니다.gather_images(부울, 선택 사항): 응답이 끝날 때 모든 이미지를 수집합니다. 기본값은 false입니다.image_caption(부울, 선택 사항): 콘텐츠의 이미지에 캡션을 추가합니다. 기본값은 false입니다.enable_iframe(부울, 선택 사항): iframe에서 콘텐츠를 추출합니다. 기본값은 false입니다.enable_shadow_dom(부울, 선택 사항): shadow DOM에서 콘텐츠를 추출합니다. 기본값은 false입니다.resolve_redirects(부울, 선택 사항): 최종 URL로의 리디렉션 체인을 따릅니다. 기본값은 true입니다.
개발
설정
저장소를 복제합니다
종속성 설치:
pnpm install프로젝트를 빌드하세요:
pnpm run build개발 모드에서 실행:
pnpm run dev출판
변경 세트를 만듭니다.
pnpm changeset패키지 버전:
pnpm version빌드 및 게시:
pnpm release기여하다
기여를 환영합니다! 풀 리퀘스트를 제출해 주세요.
특허
MIT 라이센스 - 자세한 내용은 LICENSE 파일을 참조하세요.
감사의 말
모델 컨텍스트 프로토콜을 기반으로 구축됨
Available Tools
1 toolsearchB
Search the web and get clean, LLM-friendly content using Jina.ai Reader. Returns top 5 results with URLs and clean content.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query | |
| format | No | Response format (json or text) | text |
| no_cache | No | Bypass cache for fresh results | |
| token_budget | No | Maximum number of tokens for this request | |
| browser_locale | No | Browser locale for rendering content | |
| stream | No | Enable stream mode for large pages | |
| gather_links | No | Gather all links at the end of the response | |
| gather_images | No | Gather all images at the end of the response | |
| image_caption | No | Caption images in the content | |
| enable_iframe | No | Extract content from iframes | |
| enable_shadow_dom | No | Extract content from shadow DOM | |
| resolve_redirects | No | Follow redirect chains to final URL |
TDQS
B3.1/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool returns 'clean, LLM-friendly content' and 'top 5 results with URLs and clean content,' which gives some behavioral context. However, it lacks critical information about rate limits, authentication requirements, error conditions, or what constitutes 'clean' content, leaving significant gaps for a tool with 12 parameters.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise and front-loaded: a single sentence that communicates the core functionality, method, and output format. Every word earns its place with zero redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 12 parameters and no output schema, the description provides basic purpose and output format but lacks sufficient behavioral context. Without annotations covering safety, limits, or authentication, and with no output schema to explain return values, the description should do more to compensate for these gaps, especially given the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
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 12 parameters. The description doesn't add any parameter-specific information beyond what's already in the schema descriptions. According to guidelines, when schema coverage is high (>80%), the baseline score is 3 even with no parameter information in the description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
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 and get clean, LLM-friendly content using Jina.ai Reader.' It specifies the action (search), resource (web content), and processing method (Jina.ai Reader). However, without sibling tools, it cannot demonstrate differentiation from alternatives, preventing a score of 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, prerequisites, or contextual constraints. It mentions returning 'top 5 results' but doesn't explain when this limitation is appropriate or when other search tools might be better suited.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.0- First observed
search
TDQS
B3.2/5.0
Scored across 1 tool
Disambiguation5/5
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool 'search' has a clear and singular purpose, making it impossible for an agent to misselect among non-existent alternatives.
Naming Consistency5/5
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The name 'search' follows a simple verb pattern, which is appropriate and unambiguous for its function.
Tool Count2/5
A single tool is too few for a server named 'MCP JinaAI Search Server', which suggests a broader search functionality scope. While the tool covers basic web search, the server lacks additional tools for advanced operations like filtering, pagination, or domain-specific searches, making it feel thin and under-scoped.
Completeness2/5
The server is severely incomplete for a search domain. It only offers a basic search tool without any supporting operations such as refining queries, handling multiple result pages, or accessing search history. This creates significant gaps that could lead to agent failures when more complex search tasks are required.
Maintenance
ActivityInactive
ResponsivenessNo issues
Related MCP Connectors
LLM-ready web search + instant answers + URL-to-clean-text fetch for agents and RAG.
Jina AI Reader/Search MCP — turn any URL into clean LLM-ready markdown, plus web search.
Web search for AI agents — one tool across 6 engines, routed to the cheapest + cached.
The best web search for your AI Agent
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