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Scholar Sidekick

Verify Citation

verifyCitation
Read-onlyIdempotent

Cross-checks a cited title against the paper at its identifier (DOI/PMID/PMCID) to detect fabricated citations where the identifier resolves but the title doesn't match.

Instructions

Verify a claimed citation against the resolved record at its identifier. Detects the dominant AI-driven fabrication pattern documented by Topaz et al. (Lancet 2026): a real, resolvable identifier (DOI / PMID / PMCID / arXiv / etc.) paired with a title that does NOT correspond to the paper at that identifier. Use when the user pastes a citation and asks 'is this real?' or 'check this DOI' — most fabricated citations resolve cleanly under doi.org but their cited title and the resolved title disagree. Single citation per call. Required: title plus exactly one identifier (doi, pmid, pmcid, isbn, arxiv, issn, ads, or whoIrisUrl). Optional refinements: author (first-author family name), year, container (journal). Set screenWithLlm: true to invoke the Stage 3 LLM screen on low-confidence mismatches (catches informal-abbreviation false positives); LLM access is gated to authenticated first-party keys and paid RapidAPI tiers — anonymous callers get 400 LLM_SCREEN_FORBIDDEN. Returns: { verdict: 'matched' | 'mismatch' | 'not_found' | 'ambiguous', confidence: 'high' | 'medium' | 'low', matched: , mismatches: [{field, claimed, resolved, similarity}], candidates: [{item, registries, score}] (when title-search ran), provenance: {stages_run, resolved_via, registries_searched, llm_screen} }. Verdict semantics: 'matched' = claim agrees with resolved record; 'mismatch' = identifier resolves but title does not match (Topaz fabrication pattern); 'ambiguous' = identifier resolves to one paper but the claimed title matches a DIFFERENT paper found via title-search (CITADEL 'citation error' subtype — wrong identifier for a real paper); 'not_found' = neither the identifier nor the title resolves anywhere. No sibling tool overlaps: resolveIdentifier returns metadata for a known-good identifier; verifyCitation is the only tool that cross-checks claimed title vs resolved metadata. Read-only and idempotent — safe to retry. Works anonymously for the non-LLM path; the Stage 3 LLM screen requires authentication — set SCHOLAR_API_KEY (a free ssk key from https://scholar-sidekick.com/account) or use a paid RapidAPI tier. SCHOLAR_API_KEY also raises your rate limit.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
titleYesThe title as it appears in the cited reference. This is the field the verifier cross-checks against the resolved record at the supplied identifier. Required.
doiNoDOI as cited (with or without https://doi.org/ prefix). Provide whichever identifier(s) the cited reference carries; the verifier uses the first one in priority order doi > pmid > pmcid > arxiv > ads > isbn > issn > whoIrisUrl.
pmidNoPubMed ID as cited (digits only, or with 'PMID:' prefix).
pmcidNoPubMed Central ID (e.g. 'PMC1234567' or 'PMCID:1234567').
isbnNoISBN (10- or 13-digit, hyphens tolerated).
arxivNoarXiv ID (e.g. '2301.08745' or 'arXiv:2301.08745'; old-style 'hep-ph/0501023' accepted).
issnNoISSN for journal-level resolution.
adsNoNASA ADS bibcode (19 chars).
whoIrisUrlNoWHO IRIS URL (https://iris.who.int/...).
authorNoFirst-author family name as cited. Refines the verdict — a title-vs-resolved-title match plus an author mismatch raises suspicion of fabrication. Pass only the family name (e.g. 'Topaz', not 'Topaz, Maxim').
yearNoPublication year as cited. Wrong year alone does not flip the verdict, but >=2-year gap from the resolved record lowers confidence.
containerNoJournal or container name as cited (e.g. 'The Lancet', 'Neuroscience'). Soft signal — surfaced as a mismatch field but does not gate the verdict.
screenWithLlmNoOpt-in Stage 3 LLM screen. Fires only when the pre-LLM verdict is mismatch with low confidence (the informal-abbreviation false-positive bucket). Gated: requires an authenticated first-party API key or a paid RapidAPI tier; anonymous / free callers receive 400 LLM_SCREEN_FORBIDDEN. Default false.
Behavior5/5

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

Annotations already indicate read-only, idempotent, and non-destructive nature. The description adds valuable context: it explains the detection logic, verdict semantics ('matched', 'mismatch', etc.), stages (_provenance), and safety guarantees. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is thorough but somewhat verbose. It is well-structured with front-loaded purpose and pattern, and each sentence adds value. However, it could be slightly more concise without losing essential details.

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

Completeness5/5

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

Despite the absence of an output schema, the description fully explains the return value structure, including verdict semantics and _provenance fields. It covers all aspects: input requirements, behavioral nuances, authentication, rate limits, and sibling differentiation, making it highly complete for a complex tool with 13 parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds extra meaning by explaining the identifier priority order, how optional fields (author, year, container) refine the verdict, and the gating of the screenWithLlm parameter. This goes beyond the schema, earning a 4.

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 explicitly states the tool's purpose: verifying a claimed citation by cross-checking the title against the resolved record at its identifier. It also highlights the specific fabrication pattern it detects (Topaz et al.), and distinguishes itself from sibling tools like resolveIdentifier, making the purpose unmistakable.

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?

Clear guidance on when to use (user asks 'is this real?') and how to use (single citation per call, required title plus one identifier, optional refinements). It also specifies conditions for LLM screen, authentication requirements, and explicitly mentions that resolveIdentifier is a sibling but only verifyCitation cross-checks titles.

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

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