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DOI Citation Verifier

by tfscharff
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TypeScript
Remote

🚀 快速安装

npx -y github:tfscharff/doi-mcp

或者添加到你的 Claude Desktop 配置文件中：

{
  "mcpServers": {
    "doi-mcp": {
      "command": "npx",
      "args": ["-y", "github:tfscharff/doi-mcp"]
    }
  }
}

Related MCP server: CiteStamp MCP server

解决的问题

大型语言模型有时会“幻觉”学术引文——引用不存在的论文、将真实标题错误地归于错误的作者，或混淆出版细节。此 MCP 服务器通过以下方式消除了该问题：

  1. 9 个数据库验证：跨 CrossRef、OpenAlex、PubMed、zbMATH、ERIC、HAL、INSPIRE-HEP、Semantic Scholar 和 DBLP 检查引文

  2. 并行搜索：同时查询所有数据库以获得快速结果（约 1 秒）

  3. 全面覆盖：涵盖 STEM、人文、社会科学和教育等所有学科的 6 亿多篇出版物

  4. DOI 支持的引文：每条经过验证的引文都包含一个有效的、可点击的 DOI

功能

  • 9 个数据库搜索：CrossRef、OpenAlex、PubMed、zbMATH、ERIC、HAL、INSPIRE-HEP、Semantic Scholar、DBLP

  • 验证引文：检查具有特定详细信息的论文是否确实存在于所有数据库中

  • 查找已验证论文：搜索关于某个主题的真实论文，并仅获取已验证的引文

  • 并行处理：所有数据库查询同时运行，以实现最大速度

  • 性能优化：智能缓存和提前退出策略，验证速度提升 25-35%

  • 来源选择：搜索所有数据库或指定特定来源

  • 引文格式化：返回带有 DOI 的格式化引文

  • 零配置：所有数据库均可开箱即用，无需 API 密钥

工作原理

当 AI 助手被问及研究或引文时：

  1. 没有此 MCP 时：助手可能会引用“根据 Smith 等人 (2023) 在《Nature》中的研究……”，但引用的是一篇不存在的论文

  2. 使用此 MCP 时：助手首先使用 verifyCitation，它会并行搜索 9 个数据库并返回：

    • 验证匹配且带有完整 DOI → 可以引用

    • 未找到匹配项 → 不能引用；必须搜索真实论文

工具

verifyCitation

主要的防幻觉工具 - 在提及引文之前，验证其是否存在于多个数据库中。

输入：

  • title (string, 可选): 论文标题（接受部分匹配）

  • authors (array, 可选): 作者姓名（姓氏即可）

  • year (number, 可选): 出版年份

  • doi (string, 可选): 已知 DOI

  • journal (string, 可选): 期刊名称

返回包含以下内容的 JSON：

  • verified: true/false

  • 如果 verified=true: DOI、标题、作者、年份、期刊、URL、来源数据库

  • 如果 verified=false: 未找到匹配出版物的警告信息

  • 用于透明度的匹配质量指标

成功验证示例：

{
  "verified": true,
  "doi": "10.1038/s41586-023-06004-9",
  "title": "Accurate structure prediction of biomolecular interactions...",
  "authors": ["John Jumper", "Richard Evans", "..."],
  "year": 2023,
  "journal": "Nature",
  "url": "https://doi.org/10.1038/s41586-023-06004-9",
  "source": "crossref",
  "message": "✓ Citation verified"
}

findVerifiedPapers

搜索关于某个主题的真实论文，并仅返回来自多个数据库的带有 DOI 的已验证引文。

输入：

  • query (string): 搜索查询（主题、关键词、作者姓名）

  • source (string, 可选): 要搜索的数据库 - "all" (默认), "crossref", "openalex", "pubmed", "zbmath", "eric", "hal", "inspirehep", "semanticscholar", 或 "dblp"

  • limit (number, 可选): 每个来源的结果数量 (1-20, 默认: 5)

  • yearFrom (number, 可选): 最早出版年份

  • yearTo (number, 可选): 最晚出版年份

返回： 来自指定数据库的已验证论文数组，包含完整的引文信息及来源

示例：

// Search all 9 databases
findVerifiedPapers({ query: "CRISPR gene editing", limit: 5 })

// Search only PubMed for biomedical papers
findVerifiedPapers({ query: "cancer immunotherapy", source: "pubmed", limit: 10 })

// Search zbMATH for mathematics papers
findVerifiedPapers({ query: "algebraic topology", source: "zbmath" })

// Search DBLP for computer science papers
findVerifiedPapers({ query: "neural networks", source: "dblp", yearFrom: 2020 })

// Search ERIC for education research
findVerifiedPapers({ query: "active learning pedagogy", source: "eric" })

// Search HAL for French/European humanities research
findVerifiedPapers({ query: "phenomenology Husserl", source: "hal" })

// Search INSPIRE-HEP for high-energy physics papers
findVerifiedPapers({ query: "Higgs boson", source: "inspirehep" })

安装

添加到你的 Claude Desktop 配置文件中：

Windows: %APPDATA%\Claude\claude_desktop_config.json macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Linux: ~/.config/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "doi-mcp": {
      "command": "npx",
      "args": ["-y", "github:tfscharff/doi-mcp"]
    }
  }
}

重启 Claude Desktop，服务器即可使用。

替代方案：全局安装

npm install -g github:tfscharff/doi-mcp

然后使用此配置：

{
  "mcpServers": {
    "doi-mcp": {
      "command": "doi-mcp"
    }
  }
}

替代方案：本地克隆

git clone https://github.com/tfscharff/doi-mcp.git
cd doi-mcp
npm install
npm run build

本地安装配置：

{
  "mcpServers": {
    "doi-mcp": {
      "command": "node",
      "args": ["/absolute/path/to/doi-mcp/dist/index.js"]
    }
  }
}

故障排除

服务器无法连接

  1. 检查是否安装了 Node.js: node --version (需要 v18+)

  2. 检查 Claude Desktop 日志：

    • Windows: %APPDATA%\Claude\logs\

    • macOS: ~/Library/Logs/Claude/

    • Linux: ~/.config/Claude/logs/

npx 命令失败

npm cache clean --force

本地测试

npx @modelcontextprotocol/inspector node dist/index.js

开发

# Install dependencies
npm install

# Build
npm run build

# Development with watch mode
npm run dev

使用示例

使用此 MCP 之前（引文幻觉）：

User: "Tell me about recent AlphaFold research"
Assistant: "According to Johnson et al. (2024) in Science, AlphaFold3 achieved..."
           ❌ This paper doesn't exist

使用此 MCP 之后（仅限已验证引文）：

User: "Tell me about recent AlphaFold research"
Assistant: [Uses findVerifiedPapers tool]
           "According to Jumper et al. (2023) in Nature (DOI: 10.1038/s41586-023-06004-9), 
            AlphaFold3 achieved..."
           ✓ Real paper with valid DOI verified across databases

验证捕获虚假引文：

User: "Can you verify this citation: Smith et al. (2024), 'Quantum AI', Nature"
Assistant: [Uses verifyCitation tool - searches all 9 databases in parallel]
           "⚠ I cannot verify this citation - no matching publication found in
            any of the 9 databases. This citation may be incorrect."

数据库覆盖范围

所有数据库均并行查询以实现最大速度（总计约 1 秒）：

通用数据库

  • CrossRef: 涵盖所有学科的 1.5 亿多篇学术出版物

  • OpenAlex: 涵盖所有学科的 2.5 亿多篇学术著作

  • Semantic Scholar: 2 亿多篇带有 AI 驱动搜索的论文

专业数据库

  • PubMed: 3500 多万篇生物医学和生命科学出版物

  • zbMATH: 400 多万篇数学出版物

  • DBLP: 全面的计算机科学书目（期刊和会议）

  • ERIC: 170 多万篇教育研究出版物

  • HAL: 440 多万篇法语/欧洲学术文档（250 万篇英文）

  • INSPIRE-HEP: 170 多万篇高能物理出版物

总覆盖范围

6 亿多篇出版物，涵盖所有学术学科，在 STEM、计算机科学、生物医学、数学和教育研究方面具有专业深度。

许可证

MIT

贡献

欢迎贡献！请随时提交问题或拉取请求。

相关

API 文档

资源

Available Tools

3 tools
batchVerifyCitationsA
Read-onlyIdempotent

Verify multiple citations in a single call. More efficient than calling verifyCitation multiple times. Returns verification status for each citation.

ParametersJSON Schema
NameRequiredDescriptionDefault
citationsYesArray of citations to verify

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds context beyond annotations by specifying that it 'Returns verification status for each citation,' which clarifies the output behavior. Annotations already indicate it's read-only, idempotent, and non-destructive, so the description doesn't need to repeat those traits, but it usefully describes the return format.

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 concise and front-loaded, consisting of two sentences that efficiently convey the tool's purpose, efficiency benefit, and return value without any wasted words. Every sentence adds value, making it easy for an agent to parse quickly.

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

Completeness4/5

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

Given the tool's moderate complexity, the description is complete enough: it covers purpose, usage guidelines, and output behavior. With annotations handling safety traits and no output schema, the description fills gaps by explaining the return format. However, it could briefly mention error handling or limits for full completeness.

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 mentions 'citations' as the input but doesn't add semantic details beyond what the schema provides. With 100% schema description coverage, the schema fully documents the 'citations' array and its nested properties, so the baseline score of 3 is appropriate as the description doesn't compensate with extra parameter insights.

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

Purpose5/5

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

The description clearly states the tool's purpose with a specific verb ('Verify multiple citations') and resource ('citations'), distinguishing it from sibling tools like 'verifyCitation' by emphasizing batch processing efficiency. It explicitly mentions the return value ('verification status for each citation'), which adds clarity.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool versus alternatives: it states 'More efficient than calling verifyCitation multiple times,' directly comparing it to a sibling tool. This helps the agent choose this tool for batch operations over single-citation verification.

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

findVerifiedPapersA
Read-onlyIdempotent

Search multiple academic databases (CrossRef, OpenAlex, PubMed, zbMATH, ERIC, HAL, INSPIRE-HEP, Semantic Scholar, DBLP) for papers and return only verified, real citations with DOIs.

ParametersJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query (topic, keywords, author names)
limitNoNumber of results per source
yearFromNoMinimum publication year
yearToNoMaximum publication year
sourceNoWhich source to searchall

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds valuable context beyond this: it specifies the multiple databases searched (CrossRef, OpenAlex, etc.) and the verification requirement (only papers with DOIs are returned). This helps the agent understand the tool's scope and output quality, though it doesn't mention rate limits or authentication needs.

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, dense sentence that efficiently conveys the tool's purpose, scope, and key behavior. It lists all databases upfront and specifies the verification requirement without unnecessary words. Every element earns its place, 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.

Completeness4/5

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

Given the tool's complexity (searching multiple databases with verification), annotations cover safety (read-only, non-destructive), and schema fully documents parameters, the description provides good contextual completeness. It explains the multi-source approach and DOI verification, though without an output schema, it doesn't detail the return format (e.g., what fields are included).

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 100%, providing full parameter documentation. The description doesn't add any parameter-specific details beyond what's in the schema (e.g., it doesn't explain query syntax or source differences). With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.

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

Purpose5/5

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

The description clearly states the action ('search multiple academic databases'), the resource ('papers'), and a key distinguishing feature ('return only verified, real citations with DOIs'). It differentiates from siblings by focusing on multi-source search with verification, unlike batchVerifyCitations and verifyCitation which likely handle verification of existing citations rather than searching.

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

Usage Guidelines4/5

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

The description implies usage context by specifying it searches 'multiple academic databases' and returns 'verified, real citations with DOIs', suggesting it's for finding reliable academic sources. However, it doesn't explicitly state when to use this tool versus its siblings (batchVerifyCitations, verifyCitation), which likely handle different verification scenarios.

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

verifyCitationA
Read-onlyIdempotent

CRITICAL: Use this to verify ANY academic citation before mentioning it. Checks multiple databases (CrossRef, OpenAlex, PubMed, zbMATH, ERIC, HAL, INSPIRE-HEP, Semantic Scholar, DBLP) if a paper exists. Returns null if not found.

ParametersJSON Schema
NameRequiredDescriptionDefault
titleNoPaper title (partial matches accepted)
authorsNoAuthor names (last names sufficient)
yearNoPublication year
doiNoDOI if known
journalNoJournal name

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description adds valuable behavioral context beyond annotations: it lists the specific databases checked (CrossRef, OpenAlex, etc.) and states that it 'returns null if not found,' which clarifies the output behavior. Annotations already indicate it's read-only, idempotent, and non-destructive, so the description doesn't need to repeat those traits, but it enhances understanding with operational details.

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 highly concise and well-structured: it starts with a critical warning, states the purpose and usage in a single sentence, lists databases efficiently, and ends with return behavior. Every sentence adds essential information without redundancy, making it front-loaded and easy to parse.

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

Completeness4/5

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

Given the tool's complexity (verifying citations across multiple databases) and the absence of an output schema, the description is mostly complete: it explains the purpose, usage, databases checked, and return behavior. However, it lacks details on error handling, rate limits, or authentication needs, which could be useful for full transparency. The annotations cover safety aspects, so it's adequate but not exhaustive.

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?

With 100% schema description coverage, the input schema fully documents all 5 parameters (title, authors, year, doi, journal), including details like 'partial matches accepted' for title and 'last names sufficient' for authors. The description adds no additional parameter information, so it meets the baseline of 3 by not duplicating schema content.

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

Purpose5/5

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

The description clearly states the tool's purpose with a specific verb ('verify') and resource ('academic citation'), explicitly distinguishes it from siblings by specifying it's for verifying citations before mentioning them (unlike batchVerifyCitations or findVerifiedPapers), and provides critical context about checking multiple databases. The 'CRITICAL' prefix emphasizes its importance.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('before mentioning [a citation]') and provides clear alternatives by naming sibling tools (batchVerifyCitations, findVerifiedPapers), though it doesn't detail when to use those instead. The 'CRITICAL' label implies it should be used for any citation verification, making the guidance comprehensive.

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. 3 tool updatesv1.0.0
    • First observedbatchVerifyCitations
    • First observedfindVerifiedPapers
    • First observedverifyCitation
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.3/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: batchVerifyCitations handles multiple citations efficiently, verifyCitation checks individual citations, and findVerifiedPapers searches databases for verified papers. There is no overlap in functionality, making tool selection straightforward for an agent.

Naming Consistency4/5

The naming follows a consistent verb_noun pattern (batchVerifyCitations, findVerifiedPapers, verifyCitation), with all tools using camelCase. However, verifyCitation lacks a noun suffix like 'Citation' in its verb part, which is a minor deviation from perfect consistency.

Tool Count4/5

With 3 tools, the count is reasonable for a DOI citation verification server, covering core operations (verify single, verify batch, search verified). It might be slightly thin, as additional tools for managing results or databases could enhance completeness, but it's well-scoped for the basic purpose.

Completeness4/5

The tool set covers key verification tasks: single and batch verification, plus searching for verified papers. Minor gaps exist, such as tools for updating or deleting verification data, but the core workflow of verifying and finding citations is adequately supported without dead ends.

Maintenance

ActivityStale
ResponsivenessUnresponsive

Resources

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