citeguard
citeguard
Retracted papers get cited for years after retraction. Andrew Wakefield's fraudulent 1998 paper linking the MMR vaccine to autism — retracted in 2010 — has been cited well over a thousand times since its retraction, by researchers who had no easy way to know. A 2026 JMIR study found that freely available AI tools "cannot reliably flag retracted literature," right as AI-assisted research and writing has exploded. citeguard closes that gap: check a citation against Crossref — free, no API key — before it goes in a paper, a summary, or a review.
Three surfaces, one verified detection algorithm:
Surface | For | Location |
Python library + CLI | academic writers, scripts | |
MCP server | AI agents doing research/writing | |
GitHub Action | CI on a lab's or journal's repo |
Why this is built the way it is
The detection logic isn't guessed at from documentation — it was built by
querying Crossref's real API for known cases and reading the actual
response shapes, then writing tests against the saved real responses
(committed in tests/fixtures/, shared by both the Python and TypeScript
implementations). Two real papers anchor the ground truth:
Wakefield et al., 1998, The Lancet (the MMR-autism paper): the publisher's own Crossref metadata has no structured retraction data — an
update-tocheck alone would silently miss it. What actually carries the signal is a separateupdated-byfield, where Crossref has backfilled the retraction (and an earlier 2004 correction) from the Retraction Watch database itself. Missing this field would have meant missing the single most famous retracted paper in medicine.Mehra et al., 2020, The Lancet (the Surgisphere-linked COVID/hydroxychloroquine paper): here the publisher did attach structured data directly, via
update-to. A different field, a different provenance, same underlying fact.Watson & Crick, 1953, Nature serves as the clean control in every test suite — a definitely-real, definitely-not-retracted paper that must never be flagged.
So the checker looks at three independent signals, in order: update-to
(publisher-asserted), updated-by (often Retraction-Watch-sourced,
catching what publishers miss), and a title-prefix fallback
("RETRACTED:", "WITHDRAWN:", etc.) for older or unlinked cases with no
structured metadata on either field. Each signal is mapped to the right
severity — an "Expression of Concern:" title is not the same thing as a
retraction, and earlier versions of this code collapsing that distinction
was itself a bug caught by testing against real titles, not just
synthetic ones. See src/citeguard/analyze.py
for the fully-commented implementation.
Related MCP server: verification-mcp
Quick start
CLI:
pip install -e .
citeguard doi 10.1016/S0140-6736(97)11096-0
citeguard file references.bib --fail-on retracted # for CI, see belowMCP server (add to your client's config, e.g. .mcp.json):
{ "mcpServers": { "citeguard": { "command": "node", "args": ["/path/to/citeguard/mcp-server/dist/index.js"] } } }GitHub Action (in another repo's workflow):
- uses: wedo911/citeguard@main
with:
path: references.bib
fail-on: concern # never | retracted | concern | correctedWhat this is not
Not proof a paper's content is correct. It only checks retraction status, not whether a non-retracted paper's findings hold up.
Not exhaustive. The title-prefix heuristic only catches the publisher conventions it's been tested against; a clean result means "no known signal found," not "guaranteed never retracted."
Not a bulk-scraping tool. It's built for the size of a real bibliography (tens of citations), with a small fixed delay between Crossref requests and an optional persistent cache (
src/citeguard/cache.py) — good API citizenship for a free public service, not a tool for scanning millions of DOIs.
Running the tests
# Python (55 tests, including against the real fixtures above)
pip install -e ".[dev]" && pytest -v
# MCP server (17 tests against the same real fixtures)
cd mcp-server && npm install && npm run build && npm testBoth suites are network-free and deterministic — they run against the
committed real API responses, not live calls, so they're fast and don't
depend on Crossref being reachable. Live end-to-end behavior (the actual
CLI, the actual MCP tool, hitting the real API) was separately verified
by hand during development; the GitHub Action additionally has its own
CI job (action-smoke-test) that runs the real composite action against
a known-retracted and a known-clean bibliography on every push, so the
Action itself — not just the underlying library — is continuously
verified against the live API.
Contributing
New signal types, additional publisher title conventions, and
false-positive reports are all welcome. If you add a case, prefer adding
it as a real, cited Crossref fixture over a synthetic one where possible
— that's what caught the two real bugs this project's own test suite
found during development (a URL-encoding bug in the DOI request path, and
a BibTeX parser that could swallow an adjacent entry when parsing a
malformed @comment block).
License
MIT — see LICENSE.
This server cannot be installed
Maintenance
Related MCP Servers
- AlicenseAqualityDmaintenancePrevents citation hallucination by verifying academic citations against CrossRef's database of 150+ million publications before they can be mentioned, ensuring every citation includes a valid DOI.315MIT
- FlicenseNot gradedqualityBmaintenanceEnables AI agents to fact-check claims, verify citations, and check source freshness using Wikipedia, Wikidata, Crossref, and Wayback Machine.1
- AlicenseNot gradedqualityCmaintenanceVerifies citations in reference lists by checking DOIs against public registries to catch AI-hallucinated or mismatched citations.MIT

CiteStamp MCP serverofficial
AlicenseNot gradedqualityCmaintenanceGround citations before your agent emits them by checking references against public scholarly registries and flagging hallucinated or retracted ones.MIT
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Catch AI-fabricated citations (real DOI + fake title). Retraction, open-access, 10,000+ CSL styles.
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AI research grounded in 300M scientific works — every citation a verifiable DOI.
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