Products API MCP Server
내 첫 MCP 프로젝트
이 프로젝트는 DummyJSON API에서 데이터를 검색하는 MCP(Model Context Protocol) 서버 사용법을 보여줍니다. Cursor IDE와 기본 에이전트를 사용하여 빌드되었습니다.
목차
Related MCP server: openfoodfacts-mcp-server
특징
제품 API 도구
제품 받기
설명: 선택적 필터링 및 페이지 매김을 통해 제품 목록을 가져옵니다.
매개변수:
id(문자열, 선택 사항): ID로 제품 필터링q(문자열, 선택 사항): 제목으로 제품 필터링category(문자열, 선택 사항): 카테고리별로 제품 필터링brand(문자열, 선택 사항): 브랜드별로 제품 필터링price(숫자, 선택): 가격으로 제품 필터링rating(숫자, 선택): 평점으로 제품 필터링skip(숫자, 선택 사항, 기본값: 0): 건너뛸 제품 수limit(숫자, 선택, 기본값: 10): 반환할 최대 제품 수
반환: 지정된 기준과 일치하는 제품 목록
데이터 출처: DummyJSON API( https://dummyjson.com/products )
설치 및 사용
필수 조건
Node.js(버전 23 이상)
Docker와 Docker Compose
WSL2(Windows를 사용하는 경우)
커서 구성
커서를 사용하여 이 MCP 서버를 구성하려면:
커서 열기
누르다:
Windows/Linux:
Ctrl + Shift + PmacOS:
Cmd + Shift + P
"MCP 서버 구성"을 입력하고 선택하세요.
설정에 따라 적절한 구성을 추가하세요.
Windows(WSL 없음) 또는 Linux의 경우:
지엑스피1
WSL 사용자를 위한:
{
"mcpServers": {
"postgres": {
"command": "wsl.exe",
"args": [
"-e",
"ABSOLUTE_PATH_TO_NODE/.nvm/versions/node/v23.11.0/bin/node",
"ABSOLUTE_PATH_TO_PROJECT/src/index.ts"
]
}
}
}Node.js 경로를 찾으려면 다음을 실행하세요.
which node특허
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여되었습니다.
Available Tools
1 toolget_productsC
Get a list of products with optional filtering and pagination.
| Name | Required | Description | Default |
|---|---|---|---|
| id | No | Filter products by ID | |
| q | No | Filter products by title | |
| category | No | Filter products by category | |
| brand | No | Filter products by brand | |
| price | No | Filter products by price | |
| rating | No | Filter products by rating | |
| skip | No | Number of products to skip | |
| limit | No | Maximum number of products to return |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'optional filtering and pagination,' which hints at read-only behavior and some constraints, but it doesn't detail important aspects like rate limits, authentication needs, error handling, or the format of returned data. For a tool with 8 parameters and no annotations, this is a significant gap.
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 clearly states the tool's purpose and key features (filtering and pagination). It is front-loaded with the main action and avoids unnecessary details, making it highly concise and well-structured.
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 complexity of 8 parameters, no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns, how results are structured, or any behavioral traits beyond basic functionality. For a tool with this level of detail in the schema but lacking output information, more context is needed to be complete.
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 schema description coverage is 100%, meaning all parameters are well-documented in the input schema. The description adds minimal value beyond this by mentioning 'optional filtering and pagination,' which loosely corresponds to parameters like skip and limit, but it doesn't provide additional semantic context or examples. This meets the baseline score of 3 for high schema coverage.
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: 'Get a list of products' specifies the verb (get) and resource (products). It also mentions optional filtering and pagination, which adds useful context. However, since there are no sibling tools, it doesn't need to differentiate from alternatives, so it doesn't reach the highest 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.
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 or any specific contexts for usage. It mentions optional filtering and pagination, but this is more about functionality than usage scenarios. Without any explicit when/when-not instructions or prerequisites, it falls short of higher scores.
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
- First observed
get_products
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool has a clear and distinct purpose of retrieving products with filtering and pagination capabilities.
The single tool name 'get_products' follows a consistent verb_noun pattern, and with only one tool, there is no inconsistency to evaluate. The naming is straightforward and predictable.
A server named 'Products API MCP Server' suggests a domain requiring CRUD operations, but it only provides a single 'get' tool. This is too few for the apparent scope, as it lacks create, update, delete, or other product-related operations, making it severely under-scoped.
The tool set is severely incomplete for a products API domain. It only supports retrieving products, with no coverage for creating, updating, deleting, or managing other aspects like categories or inventory. This will cause significant agent failures in typical product management workflows.
Maintenance
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