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Implements MCP's resource and tool abstractions to standardize how AI systems access scholarly content without direct API integration, using a server-client architecture where scholarmcp acts as the protocol bridge between Claude/other LLMs and backend academic sources.","intents":["I want my AI agent to search for peer-reviewed papers on a specific topic without hardcoding API credentials","I need to build a research assistant that can retrieve scholarly documents and pass them as context to an LLM","I want to standardize how multiple AI applications access academic databases through a single MCP server"],"best_for":["AI researchers building literature review agents","Teams deploying LLM-powered research assistants","Developers integrating scholarly content into multi-tool AI workflows"],"limitations":["Limited to scholarly sources exposed by the MCP server implementation — no guarantee of coverage across all major academic databases","Query latency depends on backend academic API response times, typically 2-5 seconds per search","No built-in caching or local indexing — each query hits the remote source","Authentication to underlying academic databases must be configured at server deployment time, not per-client"],"requires":["MCP client implementation (Claude desktop, custom agent framework, or MCP SDK)","Network access to scholarmcp server instance","API credentials for underlying academic sources (if required by backend)","Python 3.8+ or Node.js 16+ (depending on server runtime)"],"input_types":["text query strings","structured search filters (author, year, topic, etc.)","DOI or paper identifiers"],"output_types":["structured JSON with paper metadata (title, authors, abstract, publication date)","full-text document content (PDF or plaintext)","citation information in BibTeX or standard formats"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_1","uri":"capability://tool.use.integration.mcp.tool.schema.exposure.for.academic.queries","name":"mcp-tool-schema-exposure-for-academic-queries","description":"Registers academic search and retrieval operations as MCP tools with standardized JSON schemas, enabling LLM clients to discover available search capabilities (e.g., 'search by keyword', 'fetch by DOI', 'filter by publication date') and invoke them with type-safe argument validation. Uses MCP's tool registry pattern where scholarmcp defines tool schemas that Claude and other LLM clients can introspect and call with structured parameters.","intents":["I want my LLM to know what academic search operations are available without reading documentation","I need the LLM to validate search parameters before sending them to the backend","I want to extend the search capabilities (add filters, new sources) without modifying the LLM application code"],"best_for":["LLM application developers building research workflows","Teams standardizing tool interfaces across multiple MCP servers","Researchers prototyping multi-agent systems with shared academic access"],"limitations":["Tool schemas are static at server startup — dynamic schema updates require server restart","Schema validation happens at MCP level but doesn't guarantee backend academic API compatibility","No built-in rate limiting or quota management per tool invocation","Complex nested search filters may exceed MCP's JSON schema expressiveness"],"requires":["MCP client with tool calling support (Claude 3.5+, or custom agent framework)","scholarmcp server running and accessible","Understanding of JSON schema for parameter validation"],"input_types":["JSON-encoded tool arguments matching registered schemas","text search queries","structured filter objects (date ranges, author lists, etc.)"],"output_types":["JSON tool results with paper metadata","error messages with validation details","structured search result sets"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_2","uri":"capability://memory.knowledge.context.window.efficient.document.streaming","name":"context-window-efficient-document-streaming","description":"Streams scholarly document content through MCP's resource protocol in chunks, allowing LLM clients to retrieve large papers incrementally without loading entire documents into memory or context windows. Uses MCP's resource URI pattern to reference documents and supports partial content retrieval, enabling agents to fetch abstracts, sections, or full text on-demand while managing token budgets.","intents":["I want to retrieve only the abstract and introduction of a paper to stay within my LLM's context limit","I need to stream a 50-page PDF into my agent without loading it all at once","I want to fetch specific sections of papers (methods, results) based on what my agent needs"],"best_for":["Developers building long-running research agents with limited context windows","Teams processing large document collections through LLM pipelines","Applications requiring selective document content retrieval"],"limitations":["Streaming adds latency for small documents (< 5KB) compared to single-request retrieval","Chunk size is fixed by server configuration — no per-request granularity control","No built-in document parsing — returns raw text or PDF content without semantic section extraction","Streaming state is not persisted — reconnection requires restarting document fetch"],"requires":["MCP client with resource streaming support","scholarmcp server with document streaming enabled","Sufficient network bandwidth for streaming (typically 1-10 Mbps)"],"input_types":["document URIs or identifiers","optional section/range parameters (if supported)"],"output_types":["streamed plaintext or markdown content","PDF binary chunks","structured section metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_3","uri":"capability://search.retrieval.multi.source.academic.database.aggregation","name":"multi-source-academic-database-aggregation","description":"Abstracts multiple academic data sources (PubMed, arXiv, CrossRef, etc.) behind a unified MCP interface, allowing clients to query across sources with a single tool call. Implements source-agnostic search and result normalization, translating source-specific APIs into consistent JSON schemas that LLM clients can consume uniformly without knowing which backend provided the result.","intents":["I want to search across PubMed and arXiv simultaneously without writing separate integrations","I need my agent to find papers regardless of which academic database hosts them","I want to deduplicate results across sources and return the best match"],"best_for":["Researchers needing cross-disciplinary paper discovery","Teams building comprehensive literature review tools","Developers reducing integration complexity by centralizing academic APIs"],"limitations":["Result normalization may lose source-specific metadata or ranking signals","Query latency is bounded by slowest source — parallel requests add complexity","Source availability is not guaranteed — failures in one source may degrade overall results","Deduplication across sources requires fuzzy matching, which may miss or incorrectly merge papers","Rate limits are per-source, requiring careful quota management across aggregated APIs"],"requires":["scholarmcp server with multi-source configuration","API credentials for each enabled academic source","Network access to all configured sources"],"input_types":["unified search queries","optional source filter parameters","structured search criteria (author, date, topic)"],"output_types":["normalized JSON result sets with source attribution","deduplicated paper records","source-specific metadata preserved in result objects"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_4","uri":"capability://search.retrieval.citation.graph.traversal.and.relationship.extraction","name":"citation-graph-traversal-and-relationship-extraction","description":"Enables agents to navigate citation relationships between papers, extracting references from a paper and recursively fetching cited works. Implements graph traversal patterns where scholarmcp maintains citation relationships and allows clients to explore 'papers that cite this work', 'papers cited by this work', and 'related papers by co-authors', using MCP tools to expose graph navigation as composable operations.","intents":["I want to find all papers that cite a specific foundational work","I need to trace the research lineage of a topic by following citation chains","I want to discover related work by finding papers with overlapping references"],"best_for":["Researchers conducting systematic literature reviews","Teams building knowledge graph applications from academic content","Developers creating citation-aware research discovery tools"],"limitations":["Citation graph traversal can explode exponentially — a popular paper may have thousands of citing works","No built-in depth limiting or cycle detection — agents may get stuck in citation loops","Citation metadata is only as complete as the underlying academic sources","Traversal latency grows with graph depth — 3+ levels of citation fetching may exceed agent timeouts","No caching of citation relationships — repeated traversals hit the backend multiple times"],"requires":["scholarmcp server with citation graph support","Academic sources that expose citation metadata (CrossRef, Semantic Scholar, etc.)","Agent framework with loop detection and depth limiting"],"input_types":["paper identifiers (DOI, arXiv ID, PubMed ID)","traversal direction (citing, cited-by, related)","optional depth limit parameter"],"output_types":["lists of related paper identifiers","citation relationship metadata","graph structure representations (if supported)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_5","uri":"capability://search.retrieval.author.and.institution.profile.lookup","name":"author-and-institution-profile-lookup","description":"Provides author and institution metadata retrieval through MCP tools, allowing agents to fetch researcher profiles, publication histories, and institutional affiliations. Implements author disambiguation (matching 'John Smith' across papers) and institution normalization, returning structured profiles with publication counts, research areas, and co-author networks that help agents understand research context and credibility.","intents":["I want to verify the credibility of a paper by checking the author's publication history","I need to find all papers by a specific researcher across different name variations","I want to discover researchers at a particular institution working on my topic"],"best_for":["Researchers evaluating paper credibility and author expertise","Teams building researcher discovery and networking tools","Developers creating author-centric research workflows"],"limitations":["Author disambiguation is imperfect — common names may match multiple researchers incorrectly","Institution normalization depends on source data quality — may miss affiliations or use outdated names","Publication histories are incomplete if sources don't index all author works","No real-time profile updates — researcher profiles may lag behind actual publications by weeks","Privacy concerns with exposing detailed researcher profiles without consent"],"requires":["scholarmcp server with author/institution indexing","Academic sources with author and affiliation metadata (ORCID, CrossRef, Semantic Scholar)"],"input_types":["author names (with optional disambiguation hints)","institution names or identifiers","optional research area filters"],"output_types":["author profile objects with publication counts and research areas","institution profiles with researcher lists","co-author network data","publication history summaries"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_6","uri":"capability://search.retrieval.semantic.similarity.and.topic.clustering","name":"semantic-similarity-and-topic-clustering","description":"Computes semantic similarity between papers and clusters results by research topic using embeddings or topic modeling, exposed through MCP tools. Allows agents to find 'papers similar to this one' or 'papers in the same research area' without explicit keyword matching, using vector similarity or LDA-based clustering to group related work semantically rather than syntactically.","intents":["I want to find papers similar to a given work without relying on keywords or citations","I need to cluster search results by research topic to help users navigate large result sets","I want to discover papers in adjacent research areas that might be relevant"],"best_for":["Researchers exploring related work in unfamiliar domains","Teams building semantic search and discovery interfaces","Developers creating topic-aware research recommendation systems"],"limitations":["Embedding quality depends on training data — may not capture domain-specific nuances","Clustering is static or infrequently updated — new papers may not appear in clusters immediately","Semantic similarity can be unintuitive — papers with high similarity may not be obviously related","Computational cost of embedding and clustering limits real-time updates","No interpretability — agents cannot explain why papers are grouped together"],"requires":["scholarmcp server with embedding or clustering models","Pre-computed embeddings for academic papers (or on-demand embedding service)","Sufficient storage for embedding vectors (typically 1-10GB for large corpora)"],"input_types":["paper identifiers or full text","optional similarity threshold parameters","optional topic count for clustering"],"output_types":["similarity scores between papers","clustered result sets with topic labels","topic distribution vectors","nearest-neighbor paper lists"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_7","uri":"capability://data.processing.analysis.publication.metadata.extraction.and.normalization","name":"publication-metadata-extraction-and-normalization","description":"Extracts and normalizes publication metadata (title, authors, abstract, publication date, journal, volume, pages, DOI) from heterogeneous academic sources into consistent JSON schemas. Handles format variations across sources (e.g., different author name formats, date representations) and validates metadata completeness, allowing agents to work with standardized paper records regardless of source.","intents":["I want to extract structured metadata from papers without parsing different source formats","I need to validate that a paper record has all required fields before using it","I want to normalize author names and dates across different academic databases"],"best_for":["Teams building paper databases or knowledge bases from academic sources","Developers creating citation management tools","Researchers aggregating papers from multiple sources"],"limitations":["Normalization is lossy — source-specific metadata (e.g., keywords, subject classifications) may be dropped","Metadata completeness varies by source — some papers may lack abstracts or publication dates","Author name normalization can introduce errors (e.g., 'J. Smith' vs 'John Smith')","Date format conversion may lose precision (e.g., preprint dates vs publication dates)","No validation against external authorities — normalized data may still contain errors"],"requires":["scholarmcp server with metadata extraction","Academic sources with structured metadata APIs"],"input_types":["raw paper records from academic sources","paper identifiers for metadata lookup"],"output_types":["normalized JSON paper objects with standard fields","metadata validation reports","author and date normalization mappings"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_2646788106xh-scholarmcp__cap_8","uri":"capability://search.retrieval.full.text.search.with.advanced.filtering","name":"full-text-search-with-advanced-filtering","description":"Implements full-text search across paper abstracts and full text with support for advanced filters (date range, author, journal, publication type, citation count), exposed through MCP tools. Uses inverted indexes or full-text search engines (Elasticsearch, Solr) to enable fast keyword queries combined with structured filters, allowing agents to narrow results by multiple criteria simultaneously.","intents":["I want to search for papers on 'machine learning' published in the last 2 years by specific authors","I need to find highly-cited papers in a specific journal within a date range","I want to search full text, not just titles and abstracts"],"best_for":["Researchers conducting targeted literature searches","Teams building advanced paper discovery interfaces","Developers creating research filtering and sorting tools"],"limitations":["Full-text indexing requires significant storage (10-100GB for large corpora)","Search latency increases with index size — queries on millions of papers may take seconds","Advanced filters are only available for indexed metadata — custom fields require re-indexing","Boolean query syntax may be unfamiliar to non-technical users","No relevance ranking beyond keyword frequency — results may not be ordered by importance"],"requires":["scholarmcp server with full-text search engine","Full-text indexes built and maintained for academic sources","Sufficient storage for inverted indexes"],"input_types":["keyword queries (with optional boolean operators)","structured filter objects (date range, author list, journal, etc.)","optional sorting parameters (date, relevance, citation count)"],"output_types":["ranked lists of matching papers","result counts per filter","highlighted snippets showing keyword context"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (Claude desktop, custom agent framework, or MCP SDK)","Network access to scholarmcp server instance","API credentials for underlying academic sources (if required by backend)","Python 3.8+ or Node.js 16+ (depending on server runtime)","MCP client with tool calling support (Claude 3.5+, or custom agent framework)","scholarmcp server running and accessible","Understanding of JSON schema for parameter validation","MCP client with resource streaming support","scholarmcp server with document streaming enabled","Sufficient network bandwidth for streaming (typically 1-10 Mbps)"],"failure_modes":["Limited to scholarly sources exposed by the MCP server implementation — no guarantee of coverage across all major academic databases","Query latency depends on backend academic API response times, typically 2-5 seconds per search","No built-in caching or local indexing — each query hits the remote source","Authentication to underlying academic databases must be configured at server deployment time, not per-client","Tool schemas are static at server startup — dynamic schema updates require server restart","Schema validation happens at MCP level but doesn't guarantee backend academic API compatibility","No built-in rate limiting or quota management per tool invocation","Complex nested search filters may exceed MCP's JSON schema expressiveness","Streaming adds latency for small documents (< 5KB) compared to single-request retrieval","Chunk size is fixed by server configuration — no per-request granularity control","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.28,"ecosystem":0.38999999999999996,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:25.061Z","last_scraped_at":"2026-05-03T15:19:09.933Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=2646788106xh-scholarmcp","compare_url":"https://unfragile.ai/compare?artifact=2646788106xh-scholarmcp"}},"signature":"6r0E5fpHb6toHLIawpdzM91HoBTn+ep709UULtFAieZR43VTgkA2IIQQT4xaQlve+/+EajqQsd8ePK95WEWODg==","signedAt":"2026-06-20T02:12:14.469Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/2646788106xh-scholarmcp","artifact":"https://unfragile.ai/2646788106xh-scholarmcp","verify":"https://unfragile.ai/api/v1/verify?slug=2646788106xh-scholarmcp","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}