MCP Duty Pharma
MCP 듀티 파마
MCP 듀티 파마(MCP Duty Pharma)는 야간, 주말, 공휴일에도 영업해야 하는 약국을 찾는 데 도움을 드립니다. 긴급 상황이든 심야에 필요한 상황이든, 이 도구를 사용하면 항상 어디로 가야 할지 알 수 있습니다.
📋 시스템 요구 사항
파이썬 3.10+
Related MCP server: Hong Kong Health Data MCP Server
📦 종속성
필요한 모든 종속성을 설치하세요:
지엑스피1
필수 패키지
fastmcp : 모델 컨텍스트 프로토콜 서버를 구축하기 위한 프레임워크
geoPy : 위치에 접근하고 지오코딩/역지오코딩을 수행하는 Python 라이브러리입니다.
httpx : HTTP 요청을 위한 간단하고 직관적인 API를 제공하는 Python용 HTTP 클라이언트입니다.
모든 종속성은 pyproject.toml 에 지정됩니다.
📑 목차
🛠️ MCP 도구
이 MCP 서버는 대규모 언어 모델(LLM)에 다음과 같은 도구를 제공합니다.
근처 근무 약국 찾기
오늘 영업하는 가장 가까운 약국 10곳을 주소와의 거리순으로 정렬하여 알아보세요.
🚀 시작하기
저장소를 복제합니다.
git clone https://github.com/lsaavedr/mcp-duty-pharma.git
cd mcp-duty-pharma📦 설치
이 MCP 서버는 Claude Desktop이나 다른 곳에 설치할 수 있습니다. 이 서버를 사용하려면 설정 파일에 다음 구성을 추가하세요.
JSON 형식으로
{
"MCP Duty Pharma": {
"command": "uv",
"args": ["tool", "run", "mcp_duty_pharma"]
}
}yaml 형식으로
mcpServers:
- name: MCP Duty Pharma
command: uv
args:
- tool
- run
- mcp_duty_pharma🔒 안전 기능
속도 제한: 각 지오코딩 호출은 사용 제한을 위반하는 과도한 요청을 방지하기 위해 속도 제한(예: 1초 지연)됩니다.
오류 처리: geopy 예외(시간 초과, 서비스 오류)를 포착하고 충돌 대신 안전한 [] 결과를 반환합니다.
📚 개발 문서
이 서버를 확장하거나 수정하려면:
각 도구가 어떻게 구현되었는지, duty-pharma가 어떻게 통합되었는지 알아보려면 duty-pharma.py를 확인하세요.
경계 상자, 언어 설정, 고급 데이터 추출 등 고급 사용법은 geopy의 공식 문서를 참조하세요.
더 많은 데이터 소스를 보려면 지역 정부 API를 살펴보세요.
Available Tools
1 toolget_nearby_duty_pharmaciesB
Get ten closest pharmacies on duty today, sorted by distance to the given address.
| Name | Required | Description | Default |
|---|---|---|---|
| address | Yes |
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. While it mentions the tool returns 'ten closest pharmacies' and sorts by distance, it fails to describe critical behaviors such as response format, error handling, data freshness, rate limits, or authentication requirements. For a location-based query tool 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?
The description is a single, well-structured sentence that efficiently conveys the core functionality: action, resource, quantity, constraints, and sorting. Every word earns its place with no redundancy or fluff, making it easy to parse and front-loaded with essential information.
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 moderate complexity (location-based query with filtering), lack of annotations, and no output schema, the description is minimally complete. It covers the basic purpose and parameter intent but omits details on output structure, error cases, and operational constraints. It meets the bare minimum for understanding what the tool does but not how it behaves fully.
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 has 0% description coverage, so the description must compensate. It adds meaning by explaining that the 'address' parameter is used to calculate distance and filter results, which goes beyond the schema's bare 'Address' title. However, it doesn't specify address format requirements, validation rules, or handling of ambiguous inputs, leaving some semantic gaps.
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 ten closest pharmacies on duty today, sorted by distance to the given address.' It specifies the verb ('Get'), resource ('pharmacies'), and key constraints ('ten closest', 'on duty today', 'sorted by distance'). However, with no sibling tools mentioned, it cannot demonstrate differentiation from alternatives.
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 explicit guidance on when to use this tool versus alternatives. It implies usage for finding nearby duty pharmacies but offers no information about prerequisites, limitations, or scenarios where other tools might be more appropriate. With no siblings listed, this is a missed opportunity for basic context.
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
get_nearby_duty_pharmacies
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 and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name follows a clear verb_noun pattern (get_nearby_duty_pharmacies).
One tool is too few for a server with a domain like pharmacy duty information, as it lacks essential operations such as filtering by time, getting pharmacy details, or updating duty status. This severely limits agent functionality.
The tool surface is severely incomplete for the domain; it only provides a list of nearby duty pharmacies without supporting operations like checking specific pharmacy hours, verifying duty status, or managing pharmacy data, leading to significant gaps in agent workflows.
Maintenance
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