Generate academic citations from DOI, arXiv, titles, or URLs in BibTeX, APA, MLA formats.
MCP: Model Context Protocol Server
A lightweight server that exposes and interprets the Model Context Protocol (MCP) for model-centric workflows. It surfaces contextual model metadata, usage guidance, and tooling endpoints to integrate with downstream components and catalogs.
🛠️ Key Features
Provides standardized Model Context information for catalogs and tooling
Supports integration with model cards, prompts, and evaluation data
Lightweight, CLI-friendly interface for developer workflows
Extensible through common data fields (name, description, topics, tools)
🚀 Use Cases
Catalog detail pages displaying model context and capabilities
Developer onboarding and quickstart documentation for MCP-enabled models
Automated indexing for search engines and repository crawlers
Tooling pipelines that consume model metadata for deployment
⚡ Developer Benefits
Clear, machine-readable metadata about models and their context
Easy integration with existing BibTeX/APA/MLA-style citation workflows via related tooling
Lightweight server footprint suitable for local and CI environments
⚠️ Limitations
Source data fields may vary; rely on provided name, description, topics, toolCount, and readmeExcerpt
May not cover all edge cases for non-standard MCP implementations
Readme excerpt length can be limited by the source material
OneCite is a command-line and Python toolkit that turns messy, mixed-format references — DOIs, PMIDs, arXiv IDs, ISBNs, URLs, and BibTeX fragments — into auditable BibTeX or CSL-JSON records. Strong identifiers follow documented metadata-service routes; ordinary ambiguous plain-text references are returned as candidates for review and are not auto-promoted by process.
AI-assisted writing, automated literature pipelines, and copy-paste research habits produce ever more reference objects in ever more formats — and ever more chances for wrong, fabricated, or mismatched bibliographic data. Reference managers (Zotero), parsers (AnyStyle, GROBID), format converters (Citation.js), and identifier-to-BibTeX helpers (doi2bib, Manubot) each solve one slice of the problem. OneCite targets the under-served step before references enter a manuscript, systematic review, or manager: an auditable normalization layer that routes strong identifiers (DOI, PMID, arXiv, ISBN, URL, data/software DOIs) to the applicable metadata services, completes available metadata, reports unresolved entries, and produces BibTeX or CSL-JSON.
OneCite is not another reference manager, and process does not auto-accept fuzzy title matches. Strong identifiers are resolved through documented source routes; explicitly labelled thesis/dissertation citations have a separate OpenAIRE/BASE route and can fall back to fields parsed from the input. Ordinary ambiguous text is returned as ranked candidates through onecite suggest for human review rather than silently emitted as source-resolved output. That process/suggest separation, together with machine-readable JSON/NDJSON, exit codes, and deterministic offline checks, makes OneCite a scriptable building block for agents, batch jobs, and reproducible reviews rather than a GUI library. Source resolution does not establish that a work is authentic, unretracted, or correctly described by upstream metadata.
Features
Feature
Description
Candidate Suggestions
Search incomplete plain-text references with onecite suggest without promoting them to resolved bibliography output.
Multiple Formats
Input .txt/.bib → Output BibTeX or CSL-JSON.
4-stage Pipeline
A 4-stage process (parse → identify → enrich → format) with explicit unresolved entries.
Field Completion
Fill available fields returned by metadata sources, such as journal, volume, pages, authors, and abstract.
Normal process and suggest runs can make input-dependent outbound requests.
For example, DOI resolution sends the DOI to Crossref (and sometimes a
fallback registry); suggest sends citation queries to Crossref, Semantic
Scholar, and arXiv; arbitrary URL input is fetched from the supplied host; and
thesis queries can be sent to OpenAIRE and BASE. Google Scholar is an optional,
scraping-based suggest fallback that is off by default and may be blocked or
challenged by a CAPTCHA.
OneCite does not provide a privacy-compliance guarantee or a persistent cache
of ordinary live responses. Providers, proxies, output files, and logs may
retain data. Review and redact confidential input before use. See
Privacy and external services for the exact
process/suggest routes, transmitted fields, source-health limits, and the
offline benchmark/doctor boundary.
Quick Start
Install and try OneCite in a few steps.
1. Installation
The current public PyPI release is 0.1.1. This working tree documents the
unreleased 0.2.0 candidate, so install from the checkout when verifying
candidate-only behavior:
bash
# Current stable public release
pip install onecite
# Unreleased 0.2.0 candidate, from the repository checkout
python -m pip install -e .
2. Create an Input File
Create a file named references.txt with your mixed-format references:
text
# references.txt
# Add blank lines between entries to avoid misidentification
10.1038/nature14539
arXiv:1706.03762
ISBN:9780262035613
https://github.com/tensorflow/tensorflow
10.5281/zenodo.3233118
arXiv:2103.00020
Smith, J. (2020). Neural Architecture Search. PhD Thesis. Stanford University.
3. Run OneCite
Execute the command to process your file and generate a clean .bib output.
bash
onecite process references.txt -o results.bib --quiet
4. View Output
Your results.bib file now contains entries of different types.
View Complete Output (results.bib)
bibtex
@article{LeCun2015Deep,
doi = "10.1038/nature14539",
title = "Deep learning",
author = "LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey",
journal = "Nature",
year = 2015,
volume = 521,
number = 7553,
pages = "436-444",
publisher = "Springer Science and Business Media LLC",
url = "https://doi.org/10.1038/nature14539",
type = "journal-article",
abstract = "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction...",
}
@inproceedings{Vaswani2017Attention,
arxiv = "1706.03762",
title = "Attention Is All You Need",
author = "Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia",
year = 2017,
booktitle = "Advances in Neural Information Processing Systems (NeurIPS)",
url = "https://arxiv.org/abs/1706.03762",
}
# ... and 5 more entries ...
📖 Advanced Usage
Direct String and Stdin Input
bash
onecite process "10.1038/nature14539"
onecite suggest "Attention is all you need, Vaswani et al., NIPS 2017"echo"10.1038/nature14539" | onecite process -
🐍 Use as a Python Library
Use OneCite directly in your Python scripts.
python
from onecite import process_references
result = process_references(
input_content="10.1038/nature14539",
input_type="txt",
template_name="journal_article_full",
output_format="bibtex",
)
print('\n\n'.join(result['results']))
💻 CLI Commands & Options
OneCite provides a command-line interface with the following commands and options:
onecite process
The main command for processing references through the OneCite pipeline.
Usage:
bash
onecite process <input_file> [OPTIONS]
Arguments:
input_file - Input file path, - for stdin, or a strong identifier/reference string
Options:
Option
Short
Description
Default
--input-type
Input format: txt or bib
txt
--template
Fallback BibTeX entry-type preset when auto-detection is inconclusive
journal_article_full
--output-format
Output format: bibtex or csl-json for downstream tools that consume CSL-JSON
bibtex
--output
-o
Output file path (default: stdout)
-
--quiet
-q
Suppress verbose logging output
False
--json
Print a stable JSON envelope instead of BibTeX text
False
--ndjson
Print newline-delimited JSON events for streaming automation workflows
False
--fail-on-unresolved
Return exit code 2 when any entry cannot be resolved
False
Examples:
bash
# Process a text file
onecite process references.txt -o results.bib
# Process a BibTeX file with auto-detection
onecite process references.bib
# Use stdinecho"10.1038/nature14539" | onecite process -
# Process a direct string (DOI)
onecite process "10.1038/nature14539"# Process with custom template
onecite process references.txt --template conference_paper
# Quiet mode for scripts
onecite process references.txt -o results.bib --quiet
# Automation-friendly JSON with unresolved-entry exit-code handling
onecite process references.txt --json --fail-on-unresolved
# Streaming NDJSON for automation
onecite process references.txt --ndjson
# CSL-JSON item output (a development fixture verifies Pandoc 3.10 consumption)
onecite process references.txt --output-format csl-json -o references.json
The development evidence verifies Pandoc 3.10 consumption of representative
emitted items. Quarto, standalone citeproc, and reference-manager import
workflows are not separately validated in this release.
Report fields. Beyond results and failed_entries, the processing
report carries two audit signals:
warnings — non-blocking review warnings on resolved entries. Most
importantly text_metadata_mismatch: the input text around a resolved DOI
appears to describe a different work (the classic hallucinated
title+DOI pairing). The DOI remains the resolved identifier and the entry resolves,
but it is flagged for review instead of silently emitted as clean output.
duplicates — the same work appeared more than once in the batch (bare
DOI, PMID, formatted citation). It is emitted once; repeats are reported
with the emitted entry's cite key.
Failed entries carry the original input excerpt (raw_text) and a reason
code — doi_not_found (no registry record was returned after the implemented
fallback), no_strong_identifier (ambiguous text; use onecite suggest),
source_error (a source/identity failure surfaced on that route), and more.
These codes make important cases distinguishable, but they are not a complete
provider trace: some PMID, ISBN, and DataCite request errors currently collapse
into the same unresolved reason as a lookup miss.
onecite suggest
Search for candidate matches without producing BibTeX or returning a
validation passed status.
bash
onecite suggest "Attention is all you need, Vaswani et al., NIPS 2017" --json
Candidates are for review, not source-resolved citations. Each suggestion
discloses the health of the consulted scholarly indexes in a sources
list. If a source was rate-limited or errored, the suggestion status becomes
candidates_found_incomplete / no_candidates_incomplete — the correct
match may be missing from the list entirely, and the candidate list must
not be treated as exhaustive. Candidates whose year contradicts the year
cited in the query are penalized and flagged with year_conflict. To turn
a reviewed candidate into source-resolved BibTeX, resolve its DOI through
onecite process "<doi>".
Optional Google Scholar fallback.suggest accepts --google-scholar
(requires the optional scholarly package: pip install onecite[scholar]).
It is consulted only as a best-effort fallback when CrossRef and Semantic
Scholar return nothing. Because it scrapes a service with no public API, it
is off by default, may be rate-limited or blocked by a CAPTCHA, and is not
guaranteed to be reproducible — it is exposed only on suggest (candidates
for human review), never on process.
Alternative command to display version information.
Usage:
bash
onecite version
onecite templates
List the bundled fallback BibTeX templates and the fields they request.
Usage:
bash
onecite templates
onecite templates --json
onecite benchmark
Run a small deterministic regression suite for covered DOI lookup, arXiv
lookup, PMID/PubMed lookup, GitHub software URLs, Zenodo/DataCite dataset
DOIs, and mixed valid/invalid batches. The command is designed for CI and
automation workflows that need a machine-readable pass/fail check; it is not
a comprehensive citation-accuracy benchmark.
Usage:
bash
onecite benchmark [OPTIONS]
Options:
Option
Description
Default
--cases
Path to a custom benchmark suite JSON file
bundled golden cases
--min-success-rate
Minimum covered-case pass rate required for exit code 0
1.0
--json
Print the benchmark report as JSON
False
--live
Use live external APIs instead of bundled offline fixtures
False
--anti-hallucination
Run the labelled non-fabrication evaluation instead of the golden cases
The repository baseline record is stored at benchmarks/leaderboard.json, with
reproduction instructions in benchmarks/README.md.
Anti-hallucination evaluation
onecite benchmark --anti-hallucination runs a labelled, fully-offline
evaluation of OneCite's core safety property. It resolves real strong
identifiers (class A) into source-resolved BibTeX, leaves ambiguous
plain-text references (class B) and fabricated, non-existent DOIs (class
C — the kind a language model may hallucinate) unresolved rather than
emitting a wrong citation, and flags mismatched pairings (class D — a real
DOI attached to a different paper's title, the most common hallucinated-citation
shape) with a text_metadata_mismatch warning instead of silently emitting them
as clean source-resolved output. It reports three metrics:
resolution rate — fraction of class-A inputs correctly resolved;
non-fabrication rate — fraction of class-B/C inputs correctly left
unresolved (not fabricated). 100% means OneCite invented no citations;
mismatch detection rate — fraction of class-D inputs resolved with
the mismatch warning attached.
A pipeline crash is recorded as error and never counts as correct for any
metric — a clean rejection and a broken pipeline are different outcomes.
The dataset lives at src/onecite/benchmarks/anti_hallucination_cases.json, and the
evaluation is also available from Python via
onecite.run_anti_hallucination_eval().
onecite doctor
Check the local installation health for automation and CI. The doctor
command checks package importability, bundled templates, packaged benchmark
resources, the repository-contained OneCite Skill, and the offline benchmark
regression check.
Usage:
bash
onecite doctor
onecite doctor --json
The JSON output is a stable envelope with schema_version, tool,
command, status, environment, summary, and checks fields.
OneCite Skill for Automated Workflows
The repository includes a local skill package at skills/onecite/SKILL.md.
It gives automation and contributor workflows a repeatable procedure for
reference cleanup, benchmark and doctor checks, and explicit
reporting of unresolved entries.
The skill is repository-contained and does not install itself into any local
tool memory.
Input Type Auto-Detection
When --input-type is not specified, OneCite automatically detects the input type:
Files ending with .bib are treated as BibTeX format
All other files and strings are treated as plain text
Available Templates
OneCite supports several template presets for different entry types:
journal_article_full - Full journal article entry (default)
2 - One or more entries were unresolved when --fail-on-unresolved was used
For onecite benchmark and onecite doctor, exit code 0 means the
configured checks passed and exit code 1 means at least one check failed.
🗺️ Roadmap
OneCite Skill — Repository-contained operating guide for local citation-cleanup workflows
Benchmarking — Small deterministic regression suite, configurable pass-rate gate, and baseline record
Enhanced CLI — Automation-friendly JSON, NDJSON, summaries, and exit codes for reference processing
Anti-hallucination evaluation — Labelled offline eval of the non-fabrication property (resolution, non-fabrication, and mismatch detection rates), gated in CI
Audit-grade reports — Text/DOI mismatch warnings, failure reason codes with original input, DOI-level deduplication, and suggest source-health disclosure
CSL-JSON output — --output-format csl-json emits CSL-JSON items for downstream tools that consume the format; a development fixture verifies Pandoc 3.10 consumption, while Quarto, standalone citeproc, and reference-manager imports are not separately validated
Expanded suggest sources — Direct arXiv candidate search covers the CS venues that CrossRef does not index
Concurrent batch resolution — Parallel source lookups for large reference lists (currently sequential; latency depends on the selected routes and external services)
Larger anti-hallucination dataset — More labelled cases per class and a published live-mode baseline
🤝 Contributing
Contributions are always welcome! Please see CONTRIBUTING.md for development guidelines and instructions on how to submit a pull request.
📄 License
This project is licensed under the MIT License. See the LICENSE file for details.
