Scholar Sidekick MCP vs Infranodus MCP Server… | AllMCPs
Side-by-Side Model Context Protocol Comparison
Scholar Sidekick MCP vs Infranodus MCP Server Infranodus
In-depth architectural comparison of the Scholar Sidekick MCP and Infranodus MCP Server Infranodus MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Scholar Sidekick MCP
Research · Local stdio
Quality: 63/100 (Good) | Auth: API Key required
Infranodus MCP Server Infranodus
Research · Local stdio
Quality: 68/100 (Great) | Auth: API Key required
Verdict Summary: Choose Scholar Sidekick MCP if you need specialized Research tools running via a local process. Choose Infranodus MCP Server Infranodus if your workspace requires Research integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Scholar Sidekick MCP when:
You need dedicated capabilities in the Research domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (Free / Open Source).
Resolve any scholarly identifier (DOI, PMID, PMCID, ISBN, arXiv, ISSN, ADS bibcode, WHO IRIS URL) into structured CSL JSON, format in 10,000+ citation styles, or export to BibTeX/RIS/EndNote/Zotero RDF/CSV. Single or batch. Install: npx -y scholar-sidekick-mcp.
Map text into knowledge graphs to create a structured representation of conceptual relations and t…
Category & Scope
Tools & Capabilities Breakdown
Scholar Sidekick MCP Tools (7)
verifyCitation
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: <resolved record or null>, 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.
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Scholar Sidekick MCP is categorized under Research and uses a local stdio subprocess. In contrast, Infranodus MCP Server Infranodus belongs to Research using local stdio subprocess. Select Scholar Sidekick MCP when you need capabilities focused on research and Infranodus MCP Server Infranodus when you require tools for research.
Verify a WHOLE bibliography in one call — the batch counterpart to verifyCitation. Each entry runs the same fabrication check (real, resolvable identifier paired with a title that does NOT match the resolved paper; Topaz et al., Lancet 2026) plus a retraction lookup, and the tool returns a per-entry verdict table and a corpus summary. Use when the user pastes a reference list, a .bib / .ris file, or asks to 'check all these citations at once' / 'audit my bibliography' / 'which of these references are fake or retracted'. Input: EITHER `bibliography` (raw BibTeX / RIS / CSL-JSON text — format auto-detected) OR `claims` (an array of pre-parsed {title + identifier} objects), not both. Capped at 25 entries per call; excess is dropped and reported via `truncated`. `checks` defaults to ['retraction'] (pass [] to skip); `screenWithLlm` opt-in per entry (same auth gating as verifyCitation). Returns: { format, entries: [{ index, sourceKey?, status: 'ok'|'error', verdict: 'matched' | 'mismatch' | 'not_found' | 'ambiguous', confidence, matched, mismatches, retraction: { checked, doi, isRetracted, hasCorrections, hasConcern, notices } | null, _provenance }], parseErrors: [{ index, error, message }], truncated, summary: { total, matched, mismatch, ambiguous, not_found, errored, retracted } }. Per-entry leniency: one entry that fails to resolve becomes status:'error' without failing the batch. This audits citation IDENTITY (does each identifier resolve to the claimed work, and is it retracted) — it does NOT check whether a source supports the claim it is cited for. Read-only and idempotent. Works anonymously for the non-LLM path; SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) or a paid RapidAPI tier raises rate limits and enables the optional LLM screen.
checkRetraction
Check whether a single scholarly work has been retracted, corrected, or had an expression of concern raised. Use when the user asks 'has this paper been retracted?' or wants to verify a paper's standing before citing it (clinical, regulatory, evidence-synthesis contexts). For multi-paper bibliography audits (clinical guidelines, systematic reviews), loop one call per identifier — the tool intentionally rejects batch input to keep retraction-status results unambiguous per work. Sourced from Crossref `updated-by` (which mirrors Retraction Watch). Resolves DOI/PMID/PMCID/arXiv/ADS inputs to a DOI before lookup; ISBN inputs always return doi=null and reason='no_doi' since books are not in the retraction graph. Single identifier per call — does NOT accept comma/newline batches; loop one call per identifier for multiple papers. Returns: { doi, resolvedFrom?, reason?, result } where result has isRetracted, hasCorrections, hasConcern (booleans), notices (array of {type: 'retraction'|'correction'|'expression-of-concern', label, doi, date, source}), and title; result is null when no DOI could be resolved and reason explains why ('no_doi'). No sibling tool overlaps this — resolveIdentifier returns metadata but not retraction status. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier; Crossref is queried server-side with its own caching.
checkOpenAccess
Check whether a single scholarly work is openly accessible and where to find the best legal version. Use when the user asks 'is this open access?', 'where can I read this for free?', or wants the OA license/version before reusing or redistributing. Sourced from Unpaywall. Resolves DOI/PMID/PMCID/arXiv/ISBN/ADS inputs to a DOI before lookup; inputs that don't map to a DOI return doi=null and reason='no_doi'. Single identifier per call — does NOT accept comma/newline batches; loop one call per identifier for multiple papers. Returns: { doi, resolvedFrom?, reason?, result } where result has isOa (boolean), oaStatus ('gold' | 'green' | 'hybrid' | 'bronze' | 'closed'), title, bestLocation ({url, hostType: 'publisher' | 'repository', license, version: 'submittedVersion' | 'acceptedVersion' | 'publishedVersion'} or null), and locations (array of the same shape); result is null when no DOI could be resolved and reason explains why ('no_doi'). No sibling tool overlaps this — resolveIdentifier returns metadata but not OA status. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier; Unpaywall is queried server-side with its own caching.
resolveIdentifier
Resolve scholarly identifiers to structured CSL JSON metadata (title, authors, journal, year, identifiers). Use when the user wants raw bibliographic data to inspect, transform, or feed into another tool — not a formatted citation. Common single-shot conversions: PMID → PMCID, arXiv → DOI, ISBN → CSL JSON, WHO IRIS URL → structured metadata. Accepts DOI, PMID, PMCID, ISBN, arXiv ID, ISSN, NASA ADS bibcode, or WHO IRIS URL, with or without prefixes (PMID:, arXiv:, ISBN hyphens, https://doi.org/...). Pass a single identifier or a comma/newline-separated batch — one round trip per call. Returns: a JSON array of CSL items, each with id, type, title, author[], issued.date-parts, container-title, DOI/PMID/PMCID/ISBN/ISSN/URL when available. Use formatCitation instead when the user wants a finished citation string in a specific style; use exportCitation when they want a downloadable bibliography file. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier; the underlying REST API caches repeated identical requests and surfaces cache state in the x-scholar-cache response header.
formatCitation
Format scholarly identifiers into a finished citation in a specific style. Use when the user wants a paste-ready citation string for a manuscript, slide, message, footnote, or in-line reference. Style defaults to vancouver if unspecified; ask the user before defaulting if any ambiguity exists (e.g. 'Harvard' and 'Chicago' have multiple variants — confirm which one). Supports five hand-tuned builtins (vancouver, ama, apa, ieee, cse) plus any of 10,000+ CSL style IDs (chicago-author-date, harvard-cite-them-right, modern-language-association, nature, bmj, the-lancet, etc.). Alias and dependent-style resolution apply, so 'harvard' resolves to 'harvard-cite-them-right' and the canonical ID is reported back as styleUsed. Output defaults to text; pass output=html for marked-up HTML or output=json for structured CSL items. Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: one of { text, html, items } depending on the output parameter, followed by a metadata block ({formatter: 'builtin' | 'csl', styleUsed, requestId, warnings?}) appended as a second text content item — surface this to the user when they care about reproducibility. Use resolveIdentifier instead when the user wants raw metadata to inspect or transform; use exportCitation when they want a downloadable bibliography file. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.
exportCitation
Export scholarly identifiers to a bibliography file format ready to write to disk or paste into a reference manager. Use when the user wants a file (.bib, .ris, .nbib, .xml, .rdf, .csv) for Zotero, Mendeley, EndNote, RefWorks, BibTeX/LaTeX, Pandoc, or Excel. Format parameter is required: bib (BibTeX — LaTeX), ris (RIS — most widely supported by reference managers), csl (CSL JSON — Pandoc/Quarto), endnote-xml, endnote-refer, refworks, medline (NBIB — PubMed round-trips, clinical workflows), zotero-rdf, csv (spreadsheet-friendly), or txt (plain-text bibliography rendered with the optional style parameter — txt is the only format that uses style; the others have their own structured shape and ignore it). Accepts the same identifier formats as resolveIdentifier (DOI/PMID/PMCID/ISBN/arXiv/ISSN/ADS/WHO IRIS, prefixes tolerated), single or comma/newline-separated batch — one round trip per call. Returns: { content: string, format: string } where content is the entire bibliography in the requested format as a single string — write it to a file (.bib/.ris/.nbib/etc.) or paste it directly into the target tool. Use formatCitation instead when the user wants in-line citation text (manuscript, slide); use resolveIdentifier when they want raw structured metadata. Read-only and idempotent — safe to retry. Works anonymously against the public Scholar Sidekick API (rate-limited free tier); set SCHOLAR_API_KEY (a free ssk_ key from https://scholar-sidekick.com/account) for higher limits, or RAPIDAPI_KEY for paid RapidAPI tiers. Rate limits follow your tier.
Infranodus MCP Server Infranodus Tools (40)
generate_knowledge_graph
Generate a knowledge graph with main topics, topical clusters, concepts, concepts (nodes) relations (edges) and structural gaps. Only use when explicitly asked to analyze a text or generate a knowledge graph. Do not use for short clarifying questions that you already have an answer to from the context of the conversation.
create_knowledge_graph
Create a knowledge graph in InfraNodus from text or from a URL, save it, and provide its name and a link to it for future use.
generate_ontology_graph
Use AI to generate a reasoning ontology knowledge graph (entities and the relations between them) and optionally save it as a InfraNodus graph. Three sources, provide exactly one: prompt (a topic — one AI call), text (a long document or a structural digest of a project, chunked server-side), or sourceGraphName (an existing graph — e.g. a fully ingested repo, vault, or corpus — whose statements are read back, chunked, and condensed into an ontology). Set ontologyMode: 'codebase' for software projects, or 'procedural' to write a DIGEST of how the project works (prose statements with [[wikilinks]], not relation triples) from an already-uploaded graph — save it as <repo|vault>-<project>-digest for optimize_knowledge_base. Use to get a rich overview, a reasoning map of a topic, or a condensed 'how it fits together' graph of a large corpus.
memory_add_relations
Add relations to the InfraNodus memory from text, save it, and provide its name and a link to it for future use.
memory_get_relations
Provide a list of relations from the InfraNodus memory for a given concept or entity
analyze_existing_graph_by_name
Extract and analyze the content of an existing InfraNodus graph from your account.
analyze_text
Extract and analyze a graph from text, URL, YouTube video transcript, or an existing InfraNodus graph.
generate_content_gaps
Generate content gaps from text, URL, or an existing graph using knowledge graph analysis.
generate_topical_clusters
Generate topics and clusters of keywords from text, URL, or an existing graph using knowledge graph analysis.
generate_research_questions
Analyze text or an existing graph and generate innovative research questions based on the content gaps identified between the topical clusters. Provide either text, url, or graphName. Can be used to improve the text and the discourse it relates to
generate_research_ideas
Analyze text or an existing graph and generate innovative research ideas based on the content gaps identified between the topical clusters inside the text that can be used to improve the text and the discourse it relates to.
generate_responses_from_graph
Use text, URL, or an existing InfraNodus knowledge graph and generate responses and expert advice based on a prompt provided.