MCP PubMed Search
pubmed_search MCP 서버
MCP를 통해 PubMed 검색
구성 요소
자원
서버는 다음을 사용하여 간단한 메모 저장 시스템을 구현합니다.
개별 노트에 액세스하기 위한 사용자 지정 노트:// URI 체계
각 노트 리소스에는 이름, 설명 및 텍스트/일반 MIME 유형이 있습니다.
프롬프트
서버는 단일 프롬프트를 제공합니다.
summarize-notes: 저장된 모든 노트의 요약을 생성합니다.
세부 수준(간략/상세)을 제어하기 위한 선택적 "스타일" 인수
현재 모든 노트와 스타일 선호도를 결합하여 프롬프트를 생성합니다.
도구
서버는 하나의 도구를 구현합니다.
add-note: 서버에 새 메모를 추가합니다.
필수 문자열 인수로 "name"과 "content"를 사용합니다.
서버 상태를 업데이트하고 클라이언트에게 리소스 변경 사항을 알립니다.
Related MCP server: pubmed-search
구성
[TODO: 구현에 맞는 구성 세부 정보 추가]
빠른 시작
설치하다
클로드 데스크탑
MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
개발
건축 및 출판
배포를 위해 패키지를 준비하려면:
종속성 동기화 및 잠금 파일 업데이트:
지엑스피1
패키지 배포 빌드:
uv build이렇게 하면 dist/ 디렉토리에 소스와 휠 배포판이 생성됩니다.
PyPI에 게시:
uv publish참고: 환경 변수나 명령 플래그를 통해 PyPI 자격 증명을 설정해야 합니다.
토큰:
--token또는UV_PUBLISH_TOKEN또는 사용자 이름/비밀번호:
--username/UV_PUBLISH_USERNAME및--password/UV_PUBLISH_PASSWORD
디버깅
MCP 서버는 stdio를 통해 실행되므로 디버깅이 어려울 수 있습니다. 최상의 디버깅 환경을 위해서는 MCP Inspector 사용을 강력히 권장합니다.
다음 명령을 사용하여 npm 통해 MCP Inspector를 시작할 수 있습니다.
npx @modelcontextprotocol/inspector uv --directory /Users/nofuture/Documents/GitHub/pubmed_search run pubmed-searchInspector를 실행하면 브라우저에서 접근하여 디버깅을 시작할 수 있는 URL이 표시됩니다.
Available Tools
1 toolpubmed_searchC
Search PubMed medical literature database
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Medical/scientific search query | |
| max_results | No | Maximum number of results (1-15) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions searching but doesn't describe response format, pagination, rate limits, authentication needs, or error conditions. For a search tool with zero annotation coverage, this leaves significant behavioral 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, efficient sentence with zero wasted words. It's appropriately sized and front-loaded, communicating the core purpose immediately without unnecessary elaboration.
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 no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the search returns, how results are structured, or any behavioral constraints. The 100% schema coverage helps with parameters but doesn't compensate for the lack of output and behavioral context.
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 100% description coverage, thoroughly documenting both parameters (query and max_results). The description doesn't add any parameter-specific information beyond what's already in the schema, so it meets the baseline 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 action ('Search') and target resource ('PubMed medical literature database'), providing a specific verb+resource combination. However, since there are no sibling tools mentioned, it cannot demonstrate differentiation from alternatives, preventing a perfect 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, prerequisites, or contextual constraints. It simply states what the tool does without offering usage instructions or exclusions.
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
pubmed_search
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined as searching the PubMed database, leaving no room for misselection.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against. The tool name 'pubmed_search' follows a clear verb_noun pattern that is appropriate for its function.
One tool is too few for a server named 'MCP PubMed Search', which suggests a broader scope for interacting with PubMed. A single search tool feels thin and incomplete, lacking operations like filtering results, retrieving article details, or managing queries.
The tool surface is severely incomplete for a PubMed search domain. While searching is a core function, there are obvious gaps such as no ability to get article metadata, filter searches, save results, or handle citations, which limits agent workflows significantly.
Maintenance
Related MCP Connectors
Auditable MCP server for PubMed, Europe PMC, ClinicalTrials.gov, and bioRxiv/medRxiv queries
PubMed MCP — wraps the NCBI E-utilities API (biomedical literature, free, no auth)
Search biomedical literature, get article details, find related articles, and explore MeSH terms
Related MCP Servers
- AlicenseAqualityDmaintenanceAn MCP server that provides access to PubMed articles through the Entrez API.2113 PyPI172MIT
- AlicenseNot gradedqualityFmaintenanceMCP server for PubMed search and literature summarization52MIT
- AlicenseBqualityDmaintenanceThis MCP server provides 16 intelligent tools for searching, retrieving, and linking biomedical literature from PubMed and PMC. It enables LLM applications to perform complex queries, batch processing, and cross-database linking.168MIT
- AlicenseNot gradedqualityDmaintenanceThis server enables searching PubMed articles, fetching metadata, and downloading PMC PDFs.4MIT