What can you do with this server?

The DOI Citation Verifier server prevents AI citation hallucination by verifying academic citations against 600+ million publications across nine authoritative databases (CrossRef, OpenAlex, PubMed, zbMATH, ERIC, HAL, INSPIRE-HEP, Semantic Scholar, DBLP) — no API keys required. * Verify a single citation (verifyCitation): Check whether a specific paper exists using any combination of title, authors, year, DOI, or journal name. Searches all 9 databases in parallel and returns a verified result with a valid DOI, or a warning if no match is found. * Batch verify citations (batchVerifyCitations): Verify multiple citations in a single call, with optional identifiers to track each result. * Find verified papers (findVerifiedPapers): Search by keywords, topic, or author name across one or all databases. Supports filtering by publication year range, limiting results per source (1–20), and targeting a specific database (e.g., PubMed for biomedical, DBLP for computer science, zbMATH for mathematics, ERIC for education, HAL for French/European research, INSPIRE-HEP for high-energy physics). All returned citations are real and include valid, clickable DOIs.

Which integrations are available for this server?

Verifies academic citations and resolves DOIs against the DOI.org API to ensure citations include valid, clickable DOI links. Searches and verifies biomedical and life sciences publications across PubMed's 35+ million papers, enabling citation validation and paper discovery in medical research.

How do I use DOI Citation Verifier?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@DOI Citation Verifier verify the paper 'Attention Is All You Need' by Vaswani et al. from 2017" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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DOI Citation Verifier

by tfscharff

Overview Schema Related Servers Score Discussions

TypeScript

Remote

🚀 Quick Install

npx -y github:tfscharff/doi-mcp

Or add to your Claude Desktop config:

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

Related MCP server: CiteStamp MCP server

The Problem This Solves

Large language models sometimes "hallucinate" academic citations - citing papers that don't exist, misattributing real titles to wrong authors, or mixing up publication details. This MCP server eliminates that problem by:

9-database verification: Checks citations across CrossRef, OpenAlex, PubMed, zbMATH, ERIC, HAL, INSPIRE-HEP, Semantic Scholar, and DBLP
Parallel search: Queries all databases simultaneously for fast results (~1 second)
Comprehensive coverage: 600+ million publications across all disciplines including STEM, humanities, social sciences, and education
DOI-backed citations: Every verified citation includes a valid, clickable DOI

Features

9 Database Search: CrossRef, OpenAlex, PubMed, zbMATH, ERIC, HAL, INSPIRE-HEP, Semantic Scholar, DBLP
Verify Citations: Check if a paper with specific details actually exists across all databases
Find Verified Papers: Search for real papers on a topic and get only verified citations
Parallel Processing: All database queries run simultaneously for maximum speed
LRU Caching: Repeated queries return instantly (5-minute TTL)
Early Exit: High-confidence matches (score ≥8) return immediately without waiting for all databases
Source Selection: Search all databases or target specific sources
Citation Formatting: Returns properly formatted citations with DOIs
Zero Configuration: All databases work out-of-the-box with no API keys required
Fully Tested: 41 tests covering scoring, caching, database adapters, and tool integration

How It Works

When an AI assistant is asked about research or for citations:

Without this MCP: The assistant might cite "According to Smith et al. (2023) in Nature..." referencing a paper that doesn't exist
With this MCP: The assistant uses verifyCitation first, which searches across 9 databases in parallel and returns:
- Verified match with full DOI → Can be cited
- No match found → Cannot cite; must search for real papers instead

Tools

verifyCitation

Primary anti-hallucination tool - Verifies a citation exists across multiple databases before it can be mentioned.

Input:

title (string, optional): Paper title (partial matches accepted)
authors (array, optional): Author names (last names sufficient)
year (number, optional): Publication year
doi (string, optional): DOI if known
journal (string, optional): Journal name

Returns JSON with:

verified: true/false
If verified=true: DOI, title, authors, year, journal, URL, source database
If verified=false: Warning message that no matching publication was found
Match quality indicators for transparency

Example successful verification:

{
  "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

Search for real papers on a topic and return only verified citations with DOIs from multiple databases.

Input:

query (string): Search query (topic, keywords, author names)
source (string, optional): Which database to search - "all" (default), "crossref", "openalex", "pubmed", "zbmath", "eric", "hal", "inspirehep", "semanticscholar", or "dblp"
limit (number, optional): Number of results per source (1-20, default: 5)
yearFrom (number, optional): Minimum publication year
yearTo (number, optional): Maximum publication year

Returns: Array of verified papers from the specified database(s) with complete citation information including source

Example:

// 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" })

Installation

Add to your Claude Desktop config file:

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"]
    }
  }
}

Restart Claude Desktop and the server will be available.

Alternative: Global Install

npm install -g github:tfscharff/doi-mcp

Then use this config:

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

Alternative: Clone Locally

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

Config for local install:

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

Troubleshooting

Server not connecting

Check Node.js is installed: node --version (requires v18+)
Check Claude Desktop logs:
- Windows: %APPDATA%\Claude\logs\
- macOS: ~/Library/Logs/Claude/
- Linux: ~/.config/Claude/logs/

npx command fails

npm cache clean --force

Testing locally

npx @modelcontextprotocol/inspector node dist/index.js

Development

# Install dependencies
npm install

# Build
npm run build

# Development with watch mode
npm run dev

# Run tests
npm test

# Run tests in watch mode
npm run test:watch

# Run tests with coverage
npm run test:coverage

Architecture

Version 4.0 uses a modular architecture for maintainability and testability:

src/
├── index.ts              # Entry point
├── server.ts             # MCP server setup
├── types.ts              # Shared interfaces
├── scoring.ts            # Match scoring algorithm
├── cache.ts              # LRU cache (5-min TTL)
├── http.ts               # Fetch utilities
├── tools/                # Tool handlers
│   ├── verifyCitation.ts
│   ├── batchVerifyCitations.ts
│   └── findVerifiedPapers.ts
└── databases/            # Database adapters
    ├── index.ts          # Parallel query orchestrator
    ├── crossref.ts
    ├── openalex.ts
    ├── pubmed.ts
    └── ... (9 adapters total)

Adding a new database:

Create src/databases/newdb.ts with config, search(), and normalize()
Import and add to src/databases/index.ts
Add tests in tests/databases/newdb.test.ts

Example Usage

Before this MCP (citation hallucination):

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

After this MCP (verified citations only):

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

Verification catches fake citations:

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."

Database Coverage

All databases are queried in parallel for maximum speed (~1 second total):

General Databases

CrossRef: 150+ million scholarly publications across all disciplines
OpenAlex: 250+ million scholarly works across all disciplines
Semantic Scholar: 200+ million papers with AI-powered search

Specialized Databases

PubMed: 35+ million biomedical and life sciences publications
zbMATH: 4+ million mathematics publications
DBLP: Comprehensive computer science bibliography (journals and conferences)
ERIC: 1.7+ million education research publications
HAL: 4.4+ million French/European scholarly documents (2.5M English)
INSPIRE-HEP: 1.7+ million high-energy physics publications

Total Coverage

600+ million publications across all academic disciplines with specialized depth in STEM, computer science, biomedical sciences, mathematics, and education research.

License

MIT

Contributing

Contributions are welcome! Please feel free to submit issues or pull requests.

API Documentation

Resources

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

Name	Required	Description	Default
`citations`	Yes	Array 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

Name	Required	Description	Default
`query`	Yes	Search query (topic, keywords, author names)
`limit`	No	Number of results per source
`yearFrom`	No	Minimum publication year
`yearTo`	No	Maximum publication year
`source`	No	Which source to search	all

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

Name	Required	Description
`title`	No	Paper title (partial matches accepted)
`authors`	No	Author names (last names sufficient)
`year`	No	Publication year
`doi`	No	DOI if known
`journal`	No	Journal 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. Dates show when Glama detected each change.

3 tool updatesv1.0.0Feb 8, 2026
- First observedbatchVerifyCitations
- First observedfindVerifiedPapers
- First observedverifyCitation

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4.3/5.0

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

ActivitySlowing

ResponsivenessSyncing

Resources

GitHub Repository

Need Help?

Reddit Discussion

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Looking for Admin?

If you are the server author, to access and configure the admin panel.

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🚀 Quick Install

The Problem This Solves

Features

How It Works

Tools

verifyCitation

findVerifiedPapers

Installation

Alternative: Global Install

Alternative: Clone Locally

Troubleshooting

Server not connecting

npx command fails

Testing locally

Development

Architecture

Example Usage

Database Coverage

General Databases

Specialized Databases

Total Coverage

License

Contributing

Related

API Documentation

Resources

Available Tools

TDQS

TDQS

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Citation Guard

CiteStamp MCP serverofficial

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SciWeave MCP Serverofficial

Latest Blog Posts

MCP directory API