Generates jQuery-based client code examples for AIApp BaaS authentication integration in vanilla JavaScript applications
Provides Next.js middleware and API route templates for integrating with AIApp BaaS authentication APIs
Generates React components, hooks, and Context patterns for AIApp BaaS authentication system integration with TypeScript support
Provides pre-styled authentication UI components using Tailwind CSS classes for AIApp BaaS integration
Generates TypeScript type definitions and typed client code for AIApp BaaS authentication API integration
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@BaaS-MCP search for React authentication examples with social login"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
AIApp BaaS 인증 시스템 MCP 서버
@mbaas/baas-mcp는 AIApp BaaS 인증 시스템과의 연동을 위한 표준 입출력 기반 MCP(Model Context Protocol) 서버입니다.
이 서버는 LLM(대형 언어 모델)이 AIApp BaaS 공식 문서에서 키워드 기반으로 관련 정보를 탐색하고, 프레임워크별 클라이언트 코드를 자동 생성할 수 있도록 다양한 MCP 도구를 제공합니다.
Related MCP server: Better Auth MCP Server
이 패키지는 Node.js 18+ 환경에서 동작합니다.
아래와 같이 설정하여 설치할 수 있습니다.
{
"mcpServers": {
"baas-mcp": {
"command": "npx",
"args": ["-y", "@mbaas/baas-mcp@latest"]
}
}
}{
"mcpServers": {
"baas-mcp": {
"command": "npx",
"args": [
"-y",
"@mbaas/baas-mcp@latest",
"--project-id=your-actual-project-id"
]
}
}
}search-documents설명: AIApp BaaS 인증 시스템 문서를 키워드 배열로 검색합니다. API 문서, 구현 가이드, 보안 가이드, 예제 코드 등 모든 문서를 통합 검색합니다.
파라미터:
keywords: string[] — 검색할 키워드 배열
category?: string — 카테고리 필터 (api|templates|security|examples|dev|frameworks|errors|config)
searchMode?: string — 검색 모드 (broad|balanced|precise)
get-document-by-id설명: 문서 ID로 특정 BaaS 인증 시스템 문서의 전체 내용을 조회합니다.
파라미터:
id: number — 조회할 문서의 ID
get-project-config설명: 현재 MCP 서버에 설정된 프로젝트 ID를 확인합니다.
파라미터: 없음
MIT License
📧 Email: mbaas.tech@gmail.com
📖 문서: https://docs.aiapp.link
Built with ❤️ by mBaaS Team
문서 ID로 특정 BaaS 인증 시스템 문서의 전체 내용을 조회합니다.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | 조회할 문서의 ID (search-documents 결과에서 확인 가능) | |
| includeMetadata | No | 문서 메타데이터 포함 여부 (기본값: false) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only states it's a retrieval operation. It doesn't disclose behavioral traits like authentication requirements, rate limits, error conditions, response format, or whether it's idempotent. For a read operation with zero annotation coverage, this leaves significant gaps.
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?
Single sentence that efficiently conveys the core purpose without unnecessary words. However, it could be slightly more front-loaded by mentioning the system context earlier, but overall it's appropriately concise.
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 and no output schema, the description is incomplete for a tool that retrieves document content. It doesn't explain what 'entire content' means, what format it returns, or any behavioral constraints. The context signals show moderate complexity (2 parameters), but the description doesn't adequately address the gaps left by missing structured data.
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 fully documents both parameters. The description doesn't add any parameter meaning beyond what's in the schema (e.g., it doesn't explain what 'entire content' includes versus metadata). Baseline 3 is appropriate when schema does the heavy lifting.
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 ('retrieve entire content') and resource ('specific BaaS authentication system document'), with the qualifier 'by document ID' providing specificity. It distinguishes from 'search-documents' by focusing on single-document retrieval rather than searching, though it doesn't explicitly differentiate from 'get-project-config'.
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 when you have a specific document ID (referencing 'search-documents' for obtaining IDs), but doesn't explicitly state when to use this tool versus 'get-project-config' or provide any exclusion criteria. The guidance is partial but not comprehensive.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
현재 MCP 서버에 설정된 프로젝트 ID를 확인합니다.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are empty, so the description carries the full burden. It implies a read-only operation ('check'), but does not disclose behavioral traits such as authentication needs, rate limits, or what happens if no project ID is set. It adds minimal context beyond the basic action.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded and appropriately sized for a simple tool.
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 simplicity (0 parameters, no output schema, empty annotations), the description is adequate but lacks depth. It covers the basic purpose but does not provide context on return values or potential errors, which could be helpful for an agent despite the low complexity.
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?
The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description does not add parameter details, but this is appropriate given the lack of parameters, earning a baseline score of 4.
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: '현재 MCP 서버에 설정된 프로젝트 ID를 확인합니다' translates to 'Check the project ID configured in the current MCP server.' This specifies the verb ('check') and resource ('project ID'), though it doesn't explicitly differentiate from sibling tools like get-document-by-id or search-documents, which focus on documents rather than configuration.
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. It does not mention any prerequisites, context for usage, or comparisons with sibling tools, leaving the agent to infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AIApp BaaS 인증 시스템 문서를 키워드 배열로 검색합니다. API 문서, 구현 가이드, 보안 가이드, 예제 코드 등 모든 문서를 통합 검색합니다.
사용 예시:
로그인 React 컴포넌트: keywords=['로그인', 'React']
JWT 토큰 설정: keywords=['JWT', '토큰']
HTML 회원가입 폼: keywords=['회원가입', 'HTML']
보안 가이드: keywords=['보안'], category='security'
API 문서: keywords=['API'], category='api'
키워드 배열 사용 (권장):
keywords: ['로그인', 'React'], ['JWT', '토큰'], ['쿠키', '설정']
카테고리 필터링:
category: api, templates, security, examples, dev, frameworks, errors, config
문장 사용 (폴백):
query: "React 로그인 컴포넌트"
| Name | Required | Description | Default |
|---|---|---|---|
| category | No | 검색할 문서 카테고리 (선택사항). api, templates, security, examples, dev, frameworks, errors, config 중 선택 | |
| keywords | Yes | 검색할 키워드 배열. 예: ['로그인', 'React'], ['JWT', '토큰'], ['쿠키', '설정'] | |
| limit | No | 반환할 검색 결과 수 (기본값: 5, 최대: 10) | |
| query | No | 키워드 배열 대신 사용할 검색 문장 (비권장). 예: 'React 로그인 컴포넌트' | |
| searchMode | No | 검색 모드에 따라 결과의 관련성과 정확도가 달라집니다. 검색 모드: - broad: 폭넓은 결과 (관련성 낮아도 포함, 개념 탐색 시) - balanced: 균형잡힌 결과 (일반적인 검색, 기본값) - precise: 정확한 결과만 (정확한 답변 필요 시) | balanced |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by explaining search behavior (unified search across all documents), parameter preferences (keywords recommended, query as fallback), search mode impacts on relevance/accuracy, and result limits. It doesn't mention authentication requirements or rate limits, but covers core operational behavior thoroughly.
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 well-structured with clear sections (purpose, usage examples, keyword usage, category filtering, fallback option). While comprehensive, some sentences could be more concise, and the examples section is quite detailed but necessary for 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?
For a search tool with 5 parameters, 100% schema coverage, but no output schema, the description provides excellent context about what the tool does, how to use it, and behavioral characteristics. It could benefit from mentioning the return format or result structure, but otherwise covers the essential context well.
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 baseline would be 3. However, the description adds significant value by providing concrete usage examples for keywords and category, explaining the relationship between keywords and query parameters, and clarifying search mode impacts. This goes well beyond what the schema provides in isolation.
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 searches AIApp BaaS authentication system documents using keyword arrays, specifying it searches all document types (API docs, implementation guides, security guides, example code). It distinguishes from sibling tools like get-document-by-id (retrieves specific document) and get-project-config (gets configuration).
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 examples with specific keyword combinations and category filters, shows when to use keywords vs query parameters, and indicates keywords are recommended while query is a fallback. It gives clear guidance on search modes (broad, balanced, precise) with when-to-use explanations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Each tool has a clearly distinct purpose with no overlap: get-document-by-id retrieves a specific document by ID, get-project-config returns project configuration, and search-documents performs keyword-based searches across all documents. The descriptions clearly differentiate between individual document lookup, project-level settings, and broad search functionality.
The naming is mostly consistent with a verb-noun pattern (get-document-by-id, get-project-config, search-documents), using kebab-case throughout. The minor deviation is that 'get-document-by-id' includes a preposition ('by-id') while others do not, but the overall pattern remains clear and readable.
With 3 tools, the count is reasonable for a BaaS authentication system server, covering core operations: retrieving documents, checking project config, and searching. It might be slightly thin for full CRUD coverage (e.g., no create/update/delete tools), but it aligns well with a read-focused documentation helper.
The tool set covers key read operations for a documentation system but has notable gaps. It lacks update, delete, or creation tools for documents, which could limit agent workflows if modifications are needed. However, for a search-and-retrieve purpose, it provides essential functionality with get and search tools.
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