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Stanford CrossRef MCP Server

Bring Crossref
to Pydantic AI

Learn how to connect Stanford CrossRef to Pydantic AI and start using 16 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.

MCP Inspector GDPR Free for Subscribers
Get Citations CountGet Funder WorksGet JournalGet Journal WorksGet PublisherGet Reference ListResolve DoiSearch By AffiliationSearch By OrcidSearch FundersSearch JournalsSearch PreprintsSearch PublishersSearch Recent WorksSearch WorksValidate Doi

Compatible with every major AI agent and IDE

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Stanford CrossRef

What is the Stanford CrossRef MCP Server?

Connect to the CrossRef API — the authoritative source for DOI metadata and scholarly publishing infrastructure.

What you can do

  • DOI Resolution — Resolve any DOI to complete bibliographic metadata
  • Works Search — Search 150M+ DOI-registered works with advanced filters
  • Journal Registry — Query journals by title or ISSN with coverage metrics
  • Publisher Profiles — Explore academic publishers (Elsevier, Springer, Wiley)
  • Funder Registry — Search funding organizations (NIH, NSF, ERC, Wellcome Trust)
  • ORCID Lookup — Find works by researcher ORCID identifier
  • Affiliation Search — Search works by institutional affiliation
  • Citation Counts — Get citation and reference counts for any DOI
  • Reference Lists — Extract complete bibliographies from published works
  • Preprint Search — Find preprints registered with CrossRef
  • DOI Validation — Verify whether a DOI is valid and registered
  • Recent Works — Monitor the latest DOI registrations

Who is this for?

  • Researchers — DOI resolution and bibliography management
  • Librarians — journal evaluation and publisher analysis
  • Research Administrators — funder tracking and institutional output
  • Bibliometricians — large-scale publication analytics

Built-in capabilities (16)

get_citations_count

The "is-referenced-by-count" is the number of times other works cite this DOI. The "references-count" is how many references this work cites. Quick way to assess a paper's impact. Get citation count for a DOI

get_funder_works

Use the funder ID from search_funders (e.g. "100000002" for NIH). Essential for understanding research funding landscapes and tracking funded output. Get works funded by a specific funding organization

get_journal

Returns title, publisher, subjects, total DOI count, current and backfile counts, metadata coverage percentages, and quality flags. Get journal details by ISSN

get_journal_works

Can be filtered with an optional text query. Useful for browsing a journal's publication history or searching within a specific journal. Get articles published in a specific journal

get_publisher

Returns name, DOI prefix, total/current/backfile DOI counts, metadata coverage scores, and quality flags. Get publisher details with output metrics

get_reference_list

Returns all cited references with their DOIs (when available), authors, titles, journals, and years. Essential for bibliography analysis, finding source material, and understanding a paper's intellectual foundations. Get full reference list (bibliography) for a DOI

resolve_doi

Returns title, authors, journal, publisher, publication date, volume, issue, pages, citation count, reference count, subject areas, and license information. The definitive tool for getting structured metadata from any DOI. Resolve a DOI to full bibliographic metadata

search_by_affiliation

Use institution names like "Stanford University", "MIT", "Harvard Medical School". Can be combined with a topic query. Search works by institutional affiliation

search_by_orcid

ORCID is the universal researcher identifier. Format: "0000-0002-1825-0097". Essential for finding the complete publication record of a researcher across all journals and publishers. Find works by ORCID author identifier

search_funders

Examples: "National Institutes of Health", "National Science Foundation", "European Research Council", "Wellcome Trust". Search funding organizations worldwide

search_journals

Returns journal titles, ISSNs, publishers, subject areas, total DOI counts, and metadata coverage scores. Use this to find journal identifiers and evaluate journal metrics. Search academic journals by title or ISSN

search_preprints

This covers preprints from bioRxiv, medRxiv, SSRN, ChemRxiv, and other preprint servers that register DOIs with CrossRef. Search registered preprints across all servers

search_publishers

Returns publisher names, DOI prefixes, and total DOI counts. Search academic publishers

search_recent_works

Default is last 7 days. Use this to monitor the latest publications across all journals and publishers. Find the most recently registered DOIs

search_works

Supports full-text query, filters, sorting, and pagination. Filter syntax: "from-pub-date:2024-01-01", "type:journal-article", "has-orcid:true", "has-references:true", "is-update:false". Sort options: "relevance", "published", "indexed", "is-referenced-by-count". Search 150M+ DOI-registered academic works

validate_doi

Returns whether the DOI exists in CrossRef, along with basic metadata (title, type, publisher) if valid. Useful for quality-checking reference lists and citation data. Check if a DOI is valid and registered

Why Pydantic AI?

Pydantic AI validates every Stanford CrossRef tool response against typed schemas, catching data inconsistencies at build time. Connect 16 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

  • Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

  • Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Stanford CrossRef integration code

  • Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

  • Dependency injection system cleanly separates your Stanford CrossRef connection logic from agent behavior for testable, maintainable code

P
See it in action

Stanford CrossRef in Pydantic AI

AI AgentVinkius
High Security·Kill Switch·Plug and Play
Why Vinkius

Stanford CrossRef and 4,000+ other MCP servers. One platform. One governance layer.

Teams that connect Stanford CrossRef to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.

4,000+MCP Servers ready
<40msCold start
60%Token savings
Raw MCP
Vinkius
Server catalogFind and host yourself4,000+ managed
InfrastructureSelf-hostedSandboxed V8 isolates
Credential handlingPlaintext in configVault + runtime injection
Data loss preventionNoneConfigurable DLP policies
Kill switchNoneGlobal instant shutdown
Financial circuit breakersNonePer-server limits + alerts
Audit trailNoneEd25519 signed logs
SIEM log streamingNoneSplunk, Datadog, Webhook
HoneytokensNoneCanary alerts on leak
Custom domainsNot applicableDNS challenge verified
GDPR complianceManual effortAutomated purge + export
Enterprise Security

Why teams choose Vinkius for Stanford CrossRef in Pydantic AI

The Stanford CrossRef MCP Server runs on Vinkius-managed infrastructure inside AWS — a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts. All 16 tools execute in hardened sandboxes optimized for native MCP execution.

Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

Stanford CrossRef
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

The Vinkius Advantage

How Vinkius secures Stanford CrossRef for Pydantic AI

Every tool call from Pydantic AI to the Stanford CrossRef MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.

< 40msCold start
Ed25519Signed audit chain
60%Token savings
FAQ

Frequently asked questions

01

Do I need an API key?

No. The CrossRef API is fully public. Including a mailto address gets you into the "polite pool" with higher rate limits, which this server handles automatically.

02

What is a DOI?

A Digital Object Identifier (DOI) is a unique, permanent identifier assigned to academic publications, datasets, and other research outputs. Format: "10.1038/s41586-021-03819-2". CrossRef is the largest DOI registration agency with over 150 million registered DOIs.

03

Can I search by ORCID?

Yes. You can look up all works associated with a researcher's ORCID identifier. ORCID provides a unique, persistent identifier for researchers — similar to what DOI does for publications.

04

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

05

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

06

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Stanford CrossRef MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

07

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

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