Kagi MCP server
kagi-server MCP 서버
Kagi API 통합을 위한 MCP 서버
Kagi Search API를 통합하는 TypeScript 기반 MCP 서버입니다. 다음을 제공하여 핵심 MCP 개념을 보여줍니다.
Kagi의 API를 사용하여 웹 검색 및 기타 작업을 수행하기 위한 도구(현재 비공개 베타 버전)
특징
구현된 도구
kagi_search- Kagi를 사용하여 웹 검색 수행쿼리 문자열과 선택적 제한을 매개변수로 사용합니다.
Kagi의 API에서 검색 결과를 반환합니다.
계획된 도구(아직 구현되지 않음)
kagi_summarize- 웹 페이지 또는 텍스트 요약 생성kagi_fastgpt- Kagi의 FastGPT를 사용하여 빠른 응답 받기kagi_enrich- 특정 주제에 대한 풍부한 뉴스 결과 가져오기
Related MCP server: Kagi MCP Server
개발
종속성 설치:
지엑스피1
서버를 빌드하세요:
npm run build자동 재빌드를 사용한 개발의 경우:
npm run watch환경 설정
Kagi API 키로 루트 디렉토리에 .env 파일을 만듭니다.
KAGI_API_KEY=your_api_key_hereAPI 키를 안전하게 보호하려면 .gitignore 파일에 .env 추가해야 합니다.
설치
Smithery를 통해 설치
Smithery를 통해 Claude Desktop에 Kagi Server를 자동으로 설치하는 방법:
npx @smithery/cli install kagi-server --client claudeClaude Desktop과 함께 사용하려면 서버 구성을 추가하세요.
MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"kagi-server": {
"command": "/path/to/kagi-server/build/index.js",
"env": {
"KAGI_API_KEY": "your_api_key_here"
}
}
}
}디버깅
MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.
npm run inspector검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.
용법
서버가 실행되고 Claude Desktop에 연결되면 웹 검색을 수행할 수 있습니다. 예:
클로드에게 물어보세요: "양자 컴퓨팅의 최신 발전에 대한 정보를 검색할 수 있나요?"
클로드는
kagi_search도구를 사용하여 Kagi의 API에서 결과를 가져옵니다.그러면 클로드가 검색 결과를 요약하거나 분석해 줄 것입니다.
참고: 계획된 도구(summarize, fastgpt, enrich)는 아직 구현되지 않았으므로 사용할 수 없습니다.
기여하다
기여를 환영합니다! 풀 리퀘스트를 제출해 주세요. 기여 가능한 분야는 다음과 같습니다.
계획된 도구(요약, fastgpt, enrich) 구현
오류 처리 및 입력 검증 개선
문서화 및 사용 사례 개선
특허
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다.
로드맵
웹 페이지 및 텍스트 요약을 위한
kagi_summarize도구 구현빠른 응답을 위한
kagi_fastgpt도구 구현풍부한 뉴스 결과를 가져오기 위한
kagi_enrich도구 구현오류 처리를 개선하고 더욱 강력한 입력 검증을 추가합니다.
더욱 포괄적인 사용 예와 문서를 추가하세요
Claude Desktop 및 npx와 함께 쉽게 설치하고 사용할 수 있도록 패키지를 npm에 게시합니다.
Available Tools
1 toolkagi_searchC
Perform web search using Kagi
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions 'Perform web search' which implies read-only behavior, but doesn't disclose any behavioral traits like rate limits, authentication needs, response format, or potential side effects. This leaves significant gaps for a tool with external dependencies.
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 extremely concise—a single sentence with zero waste. It's front-loaded with the core purpose and efficiently communicates the essential function without unnecessary details.
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 no annotations, no output schema, and low schema description coverage, the description is incomplete. It doesn't address behavioral aspects, parameter usage, or result expectations, making it inadequate for a tool that interacts with an external web search service.
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 0%, so the description must compensate. It adds no meaning beyond the schema—doesn't explain what 'query' should contain, how 'limit' affects results, or any parameter nuances. The schema defines types and constraints, but the description offers no semantic context.
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 action ('Perform web search') and the resource/service ('using Kagi'), which is specific and unambiguous. However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, so it doesn't reach the highest score.
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 no guidance on when to use this tool versus alternatives, prerequisites, or any contextual limitations. It simply states what the tool does without offering usage instructions.
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
kagi_search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined as performing web searches using Kagi, leaving no room for misselection.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The name 'kagi_search' follows a clear verb_noun pattern, but consistency cannot be assessed across a set of one.
A single tool is too few for a server that appears to be focused on web search functionality, as it lacks complementary operations like summarization, filtering, or handling search results. This minimal scope feels incomplete for the domain.
The tool surface is severely incomplete for a web search domain, as it only provides raw search capability without tools for processing, refining, or managing search results. This creates significant gaps that will limit agent effectiveness.
Maintenance
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Related MCP Servers
- AlicenseCqualityDmaintenanceAllows the use of Kagi's API for web searching and content enrichment through methods like fastgpt, enrich/web, and enrich/news.32MIT
- AlicenseNot gradedqualityDmaintenanceEnables integration with Kagi search engine services including web search, content summarization from URLs, and AI assistant conversations. Uses session tokens to access Kagi's search API, summarizer, and AI models directly within MCP-compatible applications.10 npm3MIT
- AlicenseAqualityDmaintenanceProvides free web search and URL summarization using Kagi session tokens, compatible with any MCP client.284MIT
- AlicenseNot gradedqualityDmaintenanceProvides web search capabilities to AI assistants using the Kagi search API, enabling parallel queries and formatted results.20 npm3MIT