공식 Model Context Protocol 서버로 구동되는 경량 서버로, 영어 단어의 정의를 무료 dictionaryapi.dev API로 조회하는 define 도구 하나를 제공합니다.
이 프로젝트는 MCP 서버가 어떻게 구성되는지, 클라이언트와 어떻게 통신하는지, 그리고 서버를 공개적으로 배포하는 방법을 이해하기 위한 학습 목적으로 만들어졌습니다.
단일 도구를 제공합니다: define(word: str) -> str
단어가 주어지면 품사와 정의를 반환합니다
API 키나 인증이 필요 없습니다
Related MCP server: yifanli-mcp-add-demo
Python 3.10+
uv (권장) 또는 pip
# 1. Clone this repo
git clone <your-repo-url>
cd my-mcp-server
# 2. Install dependencies
uv sync옵션 A — MCP Inspector (시각적 테스트 도구, 초보자에게 가장 쉬움):
uv run mcp dev server.py이렇게 하면 브라우저 기반 인스펙터가 열리며, 전체 AI 클라이언트 없이도 define 도구를 직접 호출하고 요청/응답을 확인할 수 있습니다.
옵션 B — Claude Desktop에 연결하기:
Claude Desktop 구성 파일(claude_desktop_config.json)에 다음을 추가합니다:
{
"mcpServers": {
"dictionary-lookup": {
"command": "uv",
"args": [
"--directory",
"/absolute/path/to/my-mcp-server",
"run",
"server.py"
]
}
}
}Claude Desktop을 다시 시작한 다음, 다음과 같이 질문합니다: "'ephemeral'을 사전 도구로 정의해 줘."
입력: define("ephemeral")
출력: ephemeral (adjective): lasting for a very short time.
이 서버는 Smithery에 게시되어 있습니다: <link once deployed>
자체 복사본을 배포하려면:
이 저장소를 GitHub에 푸시합니다.
원격 호스팅을 위해 server.py가 streamable-http 전송을 지원하는지 확인합니다.
Render/Railway 등에 호스팅하거나 Smithery의 게시 프로세스를 사용합니다.
Smithery에서 "URL로 게시"를 선택하고 호스팅된 서버의 엔드포인트를 가리킵니다.
클라이언트가 서버의 도구를 발견하는 방법, 도구 호출이 이루어지고 응답되는 방법, 그리고 실제로 동작하는 서버가 Smithery 같은 마켓플레이스를 통해 공개적으로 공유되는 방법 등 MCP의 기초를 배우기 위해 만들어졌습니다.
Look up the definition of an English word and return it as text.
| Name | Required | Description | Default |
|---|---|---|---|
| word | Yes |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full behavioral burden. It conveys a read-only lookup behavior by saying 'Look up' and specifies the return type as 'text.' It does not cover edge cases like unknown words or multiple definitions, but these are not critical for a simple dictionary lookup tool.
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, front-loaded sentence that directly states the action, scope, and output. Every word earns its place and there is no unnecessary elaboration or repetition.
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 single-parameter read-only lookup tool with an output schema, the description is substantially complete. It covers the input type ('English word'), the operation, and the return format ('text'). It does not mention failure behavior, but that is a minor gap given the tool's simplicity.
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 input schema only has a parameter titled 'Word' with zero description coverage. The description compensates by specifying that the word must be an English word, which adds meaningful semantic context beyond the schema. Though format and edge cases are not detailed, the parameter and its expected value are straightforward.
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 uses a specific verb ('Look up') and a clear resource ('the definition of an English word'), and it states the output form ('return it as text'). There are no sibling tools, so no differentiation is needed. This makes the tool's purpose immediately obvious.
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 clearly implies when to use the tool: whenever an agent needs to retrieve the meaning of an English word. It also implicitly excludes non-English words. Because there are no sibling tools or alternatives, explicit when-not-to-use guidance is not necessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0defineScored across 1 tool
There is only one tool, so there is no possibility of confusing it with another tool. Its purpose is immediately clear and unambiguous.
The single tool name 'define' is a clear imperative verb that matches its action. With only one tool, there are no mixed naming conventions or inconsistencies.
One tool feels thin for a server labeled as a dictionary lookup service, though it does cover the core definition lookup need. It is borderline rather than extreme.
The core lookup functionality is covered well, but a dictionary server might reasonably include related operations like synonyms, usage examples, or pronunciation. These are minor gaps rather than fatal omissions.
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