Jina.ai Grounding MCP Server
mcp-jinaai-접지
⚠️ 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 Grounding API를 LLM과 통합하기 위한 모델 컨텍스트 프로토콜(MCP) 서버입니다. 이 서버는 효율적이고 포괄적인 웹 콘텐츠 기반 기능을 제공하며, 사실 기반의 실시간 웹 콘텐츠를 통해 LLM 응답을 향상시키도록 최적화되어 있습니다.
Related MCP server: MCP JinaAI Search Server
특징
🌐 Jina.ai Grounding API를 통한 고급 웹 콘텐츠 접지
🚀 실시간 콘텐츠 검증 및 사실 확인
📚 포괄적인 웹 콘텐츠 분석
🔄 LLM에 최적화된 깔끔한 형식
🎯 정확한 콘텐츠 관련성 점수
🏗️ 모델 컨텍스트 프로토콜 기반
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-grounding": {
"command": "wsl.exe",
"args": [
"bash",
"-c",
"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-grounding"
]
}
}
}환경 변수
서버에는 다음 환경 변수가 필요합니다.
JINAAI_API_KEY: Jina.ai API 키 (필수)
API
서버는 웹 콘텐츠로 LLM 응답을 구축하기 위한 MCP 도구를 구현합니다.
지상 콘텐츠
Jina.ai Grounding을 사용하여 실시간 웹 콘텐츠로 LLM 응답을 구체화합니다.
매개변수:
query(문자열, 필수): 웹 콘텐츠와 함께 사용할 텍스트no_cache(부울, 선택 사항): 최신 결과에 대해 캐시를 사용하지 않습니다. 기본값은 false입니다.format(문자열, 선택 사항): 응답 형식("json" 또는 "text")입니다. 기본값은 "text"입니다.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
package.json의 버전 업데이트
프로젝트를 빌드하세요:
pnpm run buildnpm에 게시:
pnpm run 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 toolground_statementA
Ground a statement using real-time web search results to check factuality. When providing URLs via the references parameter, ensure they are publicly accessible and contain relevant information about the statement. If the URLs do not contain the necessary information, try removing the URL restrictions to search the entire web.
| Name | Required | Description | Default |
|---|---|---|---|
| statement | Yes | Statement to be grounded | |
| references | No | Optional list of URLs to restrict search to. Only provide URLs that are publicly accessible and contain information relevant to the statement. If the URLs do not contain the necessary information, the grounding will fail. For best results, either provide URLs you are certain contain the information, or omit this parameter to search the entire web. | |
| no_cache | No | Whether to bypass cache for fresh results |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It describes key traits like using real-time web search, the impact of URL restrictions (grounding may fail if URLs lack info), and the option to bypass cache. However, it omits details such as rate limits, authentication needs, or specific error handling, leaving some behavioral aspects unclear.
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 and front-loaded, starting with the core purpose. Both sentences earn their place by adding useful context about URL handling, though it could be slightly more streamlined by avoiding minor redundancy with the schema (e.g., repeating URL accessibility advice).
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 complexity (fact-checking with web search) and no annotations or output schema, the description is moderately complete. It covers the main purpose and parameter usage but lacks details on output format, error cases, or performance expectations, which are important for an agent to use it effectively without structured output guidance.
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 already documents all parameters thoroughly. The description adds minimal value beyond the schema by reiterating guidance on the references parameter (e.g., ensuring URLs are accessible and relevant), but it doesn't provide additional semantic context or examples not covered in the schema, warranting a baseline score.
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 with a specific verb ('ground') and resource ('statement'), explaining it uses real-time web search to check factuality. It distinguishes the action from generic search by specifying the grounding objective, and with no sibling tools, this level of specificity is excellent.
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 clear context on when to use the tool (for fact-checking statements) and includes guidance on the references parameter (e.g., ensure URLs are publicly accessible and relevant, or omit to search the entire web). However, it lacks explicit alternatives or exclusions, as there are no sibling tools, so it doesn't fully address when-not-to-use scenarios.
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
ground_statement
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'ground_statement' has a clearly defined and distinct purpose: fact-checking statements using web search.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'ground_statement' follows a clear verb_noun pattern, which would be consistent if more tools were added.
A single tool is too few for a server that appears to handle grounding/verification tasks, as it suggests an incomplete or minimal surface. Typically, such a domain might include tools for different grounding methods, batch processing, or related operations.
The server is severely incomplete for its apparent grounding/fact-checking domain. It lacks essential operations like grounding multiple statements, verifying against specific sources, or handling different input formats, which limits agent workflows.
Maintenance
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