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cite-mcp

by ndchikin
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Python
Remote

CiteAssist MCP 서버

대장간 배지

CiteAs 및 Google Scholar에서 BibTeX 형식의 인용 데이터를 제공하는 모델 컨텍스트 프로토콜 서버입니다. 인용 검색 기능을 애플리케이션에 직접 통합하여 연구 워크플로우를 개선하세요.

구성 요소

도구

  • get_citeas_data - CiteAs에서 지정된 리소스에 대한 BibTeX 형식의 인용문을 검색합니다.

    • resource (문자열, 필수): DOI, URL, 키워드

  • get_scholar_data - Google Scholar에서 BibTeX 형식의 인용문 검색

    • query (문자열, 필수): 검색 쿼리

    • results (정수, 선택 사항): 결과 수(기본값: 2)

Related MCP server: BibTeX MCP Server

빠른 시작

설치하다

클로드 데스크탑

MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json

Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json

개발/미공개 서버 구성:

지엑스피1

게시된 서버 구성:

"mcpServers": {
  "reference-mcp": {
    "command": "uvx",
    "args": [
      "reference-mcp"
    ]
  }
}

Smithery를 통해 설치

Smithery를 통해 Claude Desktop용 reference-mcp를 자동으로 설치하려면:

npx -y @smithery/cli install @ndchikin/reference-mcp --client claude

개발

건축 및 출판

배포를 위해 패키지를 준비하려면:

  1. 종속성 동기화 및 잠금 파일 업데이트:

uv sync

  1. 패키지 배포 빌드:

uv build

이렇게 하면 dist/ 디렉토리에 소스와 휠 배포판이 생성됩니다.

  1. PyPI에 게시:

uv publish

참고: 환경 변수나 명령 플래그를 통해 PyPI 자격 증명을 설정해야 합니다.

  • 토큰: --token 또는 UV_PUBLISH_TOKEN

  • 또는 사용자 이름/비밀번호: --username / UV_PUBLISH_USERNAME 및 --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 /path/to/project/dir run reference-mcp

Inspector를 실행하면 브라우저에서 접근하여 디버깅을 시작할 수 있는 URL이 표시됩니다.

Available Tools

2 tools
get_citeas_dataC

Retrieve BibTeX-formatted citation for the specified resource (e.g., DOI, URL, keyword) from the CiteAs

ParametersJSON Schema
NameRequiredDescriptionDefault
resourceYes

TDQS

C2.9/5.0
Behavior2/5

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 states the tool retrieves data, implying a read-only operation, but doesn't cover important aspects like error handling, rate limits, authentication needs, or response format details beyond 'BibTeX-formatted'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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 parameter usage. It's appropriately sized for a simple tool with one parameter, though it could be slightly more structured with separate usage guidance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the lack of annotations and output schema, the description is incomplete for a tool that presumably returns structured citation data. It doesn't explain what happens with invalid inputs, the format of returned BibTeX, or how this differs from the sibling tool's functionality.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaningful context by explaining that the 'resource' parameter can be a DOI, URL, or keyword, which goes beyond the schema's generic 'string' type. However, with 0% schema description coverage and only one parameter, this provides adequate but not comprehensive semantic information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Retrieve' and the resource 'BibTeX-formatted citation', specifying it's from the CiteAs service. It distinguishes from the sibling tool 'get_scholar_data' by focusing on citation formatting rather than scholarly data, though the distinction could be more explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides minimal guidance by mentioning the resource parameter can be a DOI, URL, or keyword, but offers no explicit advice on when to use this tool versus the sibling 'get_scholar_data'. It lacks context on alternatives or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

get_scholar_dataC

Retrieve results BibTeX-formatted citations for publications matching the query from the Google Scholar

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYes
resultsNo

TDQS

C2.9/5.0
Behavior2/5

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 the retrieval action and output format but lacks details on rate limits, authentication needs, error handling, or what happens if no results are found. This is a significant gap for a tool that likely interacts with an external API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence with no wasted words. It front-loads the key action and resource, making it easy to scan and understand quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity of querying an external service like Google Scholar, no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks crucial details such as response format beyond 'BibTeX-formatted', error cases, or usage constraints, leaving significant gaps for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the schema provides no parameter descriptions. The description adds some semantics by explaining that 'query' matches publications and 'results' retrieves citations, but it doesn't specify query syntax, result limits, or default behavior beyond the schema's default value. It partially compensates but not fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb ('Retrieve') and resource ('BibTeX-formatted citations for publications'), specifying the format and source (Google Scholar). It doesn't explicitly differentiate from the sibling tool 'get_citeas_data', but the purpose is well-defined and not tautological.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus the sibling 'get_citeas_data', nor any context about prerequisites, alternatives, or exclusions. The description only states what it does, not when it should be used.

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. 2 tool updatesv1.0.0
    • First observedget_citeas_data
    • First observedget_scholar_data
Deploy Server
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TDQS

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes: get_citeas_data retrieves a citation for a specific resource (DOI, URL, keyword), while get_scholar_data searches for multiple publications matching a query. There is no overlap in functionality, making tool selection unambiguous.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern (get_citeas_data and get_scholar_data), using the same verb 'get' and a descriptive noun indicating the data source. The naming is predictable and uniform throughout the set.

Tool Count2/5

With only two tools, the server feels thin for a citation management domain. While the tools cover basic retrieval from two sources, there are no operations for managing citations (e.g., save, update, delete) or handling metadata, which limits functionality for typical agent workflows.

Completeness2/5

The tool set is severely incomplete for citation management. It only provides retrieval from two sources, lacking essential operations like creating, updating, deleting, or organizing citations. There are no tools for formatting, exporting, or integrating with reference managers, leaving significant gaps in the domain coverage.

Maintenance

ActivityInactive
ResponsivenessNo issues

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