Jina AI MCP Tools
Jina AI MCP 도구
Jina AI Search Foundation API와 통합되는 MCP(Model Context Protocol) 서버입니다.
기능
이 MCP 서버는 다음 Jina AI API에 대한 액세스를 제공합니다:
Web Reader - r.jina.ai를 사용하여 웹 페이지에서 콘텐츠 추출
Web Search - s.jina.ai 또는 svip.jina.ai를 사용하여 웹 검색 (
--search-endpoint를 통해 구성 가능)
Related MCP server: Jina Web Search MCP
사전 요구 사항
Jina AI API 키 (선택 사항) - 향상된 기능을 위해 https://jina.ai/?sui=apikey에서 무료 API 키를 받으세요.
Node.js - 버전 16 이상
MCP 서버
stdio 전송 사용 (기본값)
다른 프로세스(예: Claude Desktop, VS Code, Cursor)에 의해 생성되는 로컬 통합의 경우:
{
"mcpServers": {
"jina-mcp-tools": {
"command": "npx",
"args": [
"jina-mcp-tools",
"--transport", "stdio",
"--tokens-per-page", "15000",
"--search-endpoint", "standard"
],
"env": {
"JINA_API_KEY": "your_jina_api_key_here_optional"
}
}
}
}HTTP 전송 사용
HTTP를 통해 액세스할 수 있는 원격 서버 배포의 경우:
서버 시작:
# With API key
JINA_API_KEY=your_api_key npx jina-mcp-tools --transport http --host 127.0.0.1 --port 3000
# Without API key (reader tool only, binds to 127.0.0.1 by default)
npx jina-mcp-tools --transport http --port 3000MCP 클라이언트에서 연결:
MCP Inspector:
npx @modelcontextprotocol/inspector→http://localhost:3000/mcpClaude Code:
claude mcp add --transport http jina-tools http://localhost:3000/mcpVS Code:
code --add-mcp '{"name":"jina-tools","type":"http","url":"http://localhost:3000/mcp"}'
CLI 옵션:
--transport- 전송 유형:stdio또는http(기본값: stdio)--host- HTTP 모드에서 바인딩할 호스트/인터페이스 (기본값:127.0.0.1)--port- HTTP 서버 포트 (기본값: 3000, HTTP 전송에만 해당)--tokens-per-page- 페이지 매김을 위한 페이지당 토큰 수 (기본값: 15000)--search-endpoint- 사용할 검색 엔드포인트:standard(s.jina.ai) 또는vip(svip.jina.ai) (기본값: standard)--cache-size- URL 단위의 리더 캐시 크기 (기본값: 50)--help- 내장 CLI 도움말 표시
프록시 환경 변수
아웃바운드 HTTP 요청은 이제 표준 프록시 환경 변수를 준수합니다. 이는 jina_reader, jina_search, jina_search_vip에 적용됩니다.
지원되는 프록시 변수:
HTTP_PROXY,HTTPS_PROXY,ALL_PROXY소문자 변형도 지원됩니다:
http_proxy,https_proxy,all_proxyNO_PROXY/no_proxy를 통해 제외 항목이 존중됩니다.
예시:
HTTPS_PROXY=http://proxy.internal:8080 npx jina-mcp-tools --transport stdio사용 가능한 도구
jina_reader
웹 페이지 콘텐츠를 추출하고 읽습니다.
매개변수:
url- 읽을 URL (필수)page- 페이지 매김된 콘텐츠의 페이지 번호 (기본값: 1)customTimeout- 초 단위의 타임아웃 재정의 (선택 사항)
기능:
대형 문서를 위한 자동 페이지 매김
즉각적인 후속 페이지 요청을 위한 LRU 캐시 (50개 URL)
GitHub 파일 URL은 자동으로 원시 콘텐츠 URL로 변환됨
마크다운 지원 문서/블로그 호스트의 유지 관리되는 허용 목록에 대해 먼저
Accept: text/markdown을 시도한 다음, 가져오기 실패나 빈 응답이 발생하면r.jina.ai로 대체됨
jina_search / jina_search_vip
웹을 검색합니다. 부분적인 콘텐츠를 반환하며, 전체 콘텐츠를 보려면 jina_reader를 사용하세요. API 키가 필요합니다.
등록된 도구는 --search-endpoint에 따라 다릅니다:
jina_search→standard(s.jina.ai, 기본값)jina_search_vip→vip(svip.jina.ai)
매개변수:
query- 검색 쿼리 (필수)count- 결과 수 (기본값: 5)siteFilter- 특정 도메인으로 제한 (예: "github.com")
라이선스
MIT
링크
Available Tools
1 tooljina_readerJina Web ReaderC
Read and extract content from web page.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the webpage to read and extract content from | |
| page | No | Page number for paginated content (1-indexed) | |
| customTimeout | No | Override timeout in whole seconds (1-2147483) for slow sites |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, but it discloses nothing about authentication requirements, rate limits, JavaScript rendering, truncation/pagination behavior, or error handling. The presence of customTimeout implies slow sites exist but the description never explains that tradeoff.
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?
It is a single short sentence that is front-loaded with the core action, with zero filler. However, the extreme brevity is under-specification rather than true efficiency for a tool this thin on context.
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?
With no annotations and no output schema, the description must explain what comes back and how pagination and timeouts behave; it does neither. An agent cannot tell whether the response is raw HTML, markdown, or plain text, nor how paginated content is returned.
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 url, page, and customTimeout are each documented in the schema itself; baseline 3 applies. The description adds no extra meaning beyond what the schema already provides.
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 states a specific verb pair (read/extract) and resource (web page), so an agent knows this fetches and parses page content. With no sibling tools to distinguish from, there is nothing more to disambiguate, but it does not clarify output form (markdown vs HTML vs text).
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?
There is no when-to-use guidance, no mention of alternatives, and no conditions under which this tool should be preferred or avoided. The description is purely a statement of function.
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
v1.3.0- Changed
jina_reader8 fields changed- changed
Input schema / $schemaPrevious value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema" - changed
Input schema / properties / customTimeout / descriptionPrevious value: -"Override timeout in seconds for slow sites"New value: +"Override timeout in whole seconds (1-2147483) for slow sites" - added
Input schema / properties / customTimeout / exclusiveMinimumAdded value: +0 - added
Input schema / properties / customTimeout / maximumAdded value: +2147483 - changed
Input schema / properties / customTimeout / typePrevious value: -"number"New value: +"integer" - added
Input schema / properties / page / exclusiveMinimumAdded value: +0 - added
Input schema / properties / page / maximumAdded value: +9007199254740991 - changed
Input schema / properties / page / typePrevious value: -"number"New value: +"integer"
1 tool update
v1.2.4- Changed
jina_reader1 field changed- removed
Input schema / additionalPropertiesRemoved value: -false
1 tool update
v1.2.0- First observed
jina_reader
TDQS
Scored across 1 tool
There is only one tool, so there is no possibility of confusion or misselection. Its purpose is clearly distinct.
With a single tool named jina_reader, consistency is trivially satisfied. It follows a readable snake_case pattern.
A server branded as 'Jina AI MCP Tools' with only one tool seems too few for the apparent scope. Jina AI typically offers multiple capabilities (search, embeddings, reranking), so one tool feels under-provisioned.
The surface only covers web page reading, with no search, embedding, reranking, or other operations implied by the Jina AI brand. This is a severely incomplete toolset for the stated purpose.
Maintenance
Related MCP Connectors
Jina AI Reader/Search MCP — turn any URL into clean LLM-ready markdown, plus web search.
Web search and page reading for AI agents, including JSON APIs, RSS feeds and YouTube.
Web search and page fetch for AI agents. Results come from a shared cache; cached calls cost less.
Web search, AI agent, and content extraction via You.com APIs
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- AlicenseBqualityFmaintenanceEnables efficient web search integration with Jina.ai's Search API, offering clean, LLM-optimized content retrieval with support for various content types and configurable caching.128 npm3MIT
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