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Abstracts away source-specific API differences and query syntax variations, routing requests to appropriate backends and aggregating results into a consistent schema for downstream processing.","intents":["search for papers across multiple academic databases without switching between interfaces","find papers on a topic and get consistent metadata regardless of source","discover papers from specialized repositories like IACR for cryptography research","build literature review tools that query multiple sources programmatically"],"best_for":["researchers building automated literature discovery systems","LLM agents that need to autonomously search academic literature","teams building knowledge synthesis tools across multiple domains"],"limitations":["rate limits vary by source — some APIs (Google Scholar) have aggressive throttling requiring backoff strategies","search result quality and relevance ranking differs significantly between sources; no cross-source result normalization","some sources (Google Scholar) may require proxy rotation or session management to avoid blocking","no full-text search capability — limited to metadata and abstract search on most sources"],"requires":["MCP client implementation (e.g., Claude Desktop, custom MCP host)","network access to arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, and IACR APIs","API keys for sources that require authentication (Semantic Scholar API key recommended for higher rate limits)"],"input_types":["text query string","structured search parameters (author, year range, subject area)"],"output_types":["JSON array of paper metadata objects","structured fields: title, authors, abstract, publication date, source, DOI, URL"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adamamer20-paper-search-mcp-openai__cap_1","uri":"capability://search.retrieval.pdf.download.and.retrieval.with.source.specific.handling","name":"pdf download and retrieval with source-specific handling","description":"Fetches full-text PDFs from academic repositories using source-aware download strategies. Handles authentication, redirects, and format variations across sources (arXiv direct downloads, PubMed Central's FTP structure, bioRxiv/medRxiv preprint servers). Implements fallback chains when primary sources are unavailable, attempting alternative mirrors or formats.","intents":["download full PDF of a paper identified through search results","retrieve papers programmatically without manual browser navigation","build offline literature databases by batch-downloading papers","handle cases where PDFs are behind paywalls by checking open-access mirrors"],"best_for":["automated literature review pipelines that need full-text access","researchers building local paper repositories","LLM agents that need to fetch and analyze full paper content"],"limitations":["paywall-protected papers cannot be downloaded unless open-access version exists in indexed sources","PDF download success rates vary by source — some repositories have stricter access controls","no OCR fallback for image-based PDFs; text extraction depends on PDF being digitally encoded","large batch downloads may trigger rate limiting or IP blocking on some sources"],"requires":["MCP client with file I/O capabilities","network access to PDF hosting servers","sufficient disk space for storing downloaded PDFs","optional: local PDF storage path configuration"],"input_types":["paper identifier (DOI, arXiv ID, PubMed ID, or direct URL)","source type hint to optimize download strategy"],"output_types":["binary PDF file","download status/metadata (success, source used, file size, timestamp)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adamamer20-paper-search-mcp-openai__cap_2","uri":"capability://data.processing.analysis.full.text.extraction.and.normalization.from.pdfs","name":"full-text extraction and normalization from pdfs","description":"Extracts and parses text content from downloaded PDFs into structured, normalized formats. Applies heuristics to identify paper sections (abstract, introduction, methods, results, discussion), handles multi-column layouts, and removes boilerplate (headers, footers, page numbers). Outputs clean text suitable for downstream NLP analysis, embedding generation, or LLM consumption.","intents":["extract full paper text for semantic analysis or summarization","feed paper content into LLM for question-answering or synthesis","build embeddings from paper text for similarity search","parse structured sections (abstract, methods) for targeted analysis"],"best_for":["LLM agents that need to reason over full paper content","teams building semantic search systems over academic literature","researchers analyzing paper structure or methodology patterns"],"limitations":["extraction quality degrades on scanned/image-based PDFs without OCR support","section detection heuristics may fail on non-standard paper layouts (e.g., some conference proceedings)","mathematical equations and tables are extracted as text, losing semantic structure","multi-column layouts may produce garbled text order without column-aware parsing","extracted text may contain artifacts (ligatures, encoding issues) from PDF rendering"],"requires":["PDF file (local or from download capability)","PDF parsing library (e.g., pdfplumber, PyPDF2, or similar)","sufficient memory for large PDFs (100+ page papers)"],"input_types":["PDF file path or binary PDF content"],"output_types":["plain text string","structured JSON with sections (abstract, introduction, methods, results, discussion, references)","metadata (page count, extraction confidence, detected language)"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adamamer20-paper-search-mcp-openai__cap_3","uri":"capability://data.processing.analysis.consistent.metadata.normalization.across.heterogeneous.sources","name":"consistent metadata normalization across heterogeneous sources","description":"Transforms source-specific metadata schemas (arXiv's XML structure, PubMed's MEDLINE format, Google Scholar's HTML scraping results) into a unified JSON schema. Normalizes author names, dates, identifiers (DOI, PMID, arXiv ID), and subject classifications. Handles missing fields gracefully with fallbacks and confidence scores, enabling consistent filtering and citation generation.","intents":["filter search results by consistent criteria (year, author, subject) regardless of source","generate citations in standard formats (BibTeX, APA) from heterogeneous sources","deduplicate papers found across multiple sources using normalized identifiers","build structured datasets of papers with consistent field types"],"best_for":["teams building citation management tools","researchers aggregating papers from multiple sources into a database","LLM agents that need to reason over paper metadata consistently"],"limitations":["some sources provide incomplete metadata — normalized output may have null fields","author name normalization is heuristic-based and may fail on non-Latin scripts or complex names","subject classification varies by source (MeSH for PubMed, arXiv categories, ACM CCS) — no cross-source mapping","publication date formats vary; some sources only provide year, others full date","DOI availability varies by source; some papers lack persistent identifiers"],"requires":["raw metadata from search or download operations","mapping configuration for source-specific field extraction"],"input_types":["source-specific metadata objects (XML, JSON, or parsed HTML)"],"output_types":["normalized JSON object with standard schema","fields: title, authors (normalized array), publication_date, abstract, source, identifiers (DOI, PMID, arXiv_id), subject_categories, url"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adamamer20-paper-search-mcp-openai__cap_4","uri":"capability://tool.use.integration.mcp.protocol.integration.for.llm.agent.tool.calling","name":"mcp protocol integration for llm agent tool calling","description":"Exposes all paper search, download, and extraction capabilities as MCP tools that Claude and other LLM agents can invoke directly. Implements MCP's tool schema specification with proper input validation, error handling, and streaming support for long-running operations. Enables agents to autonomously discover, retrieve, and analyze papers without human intervention.","intents":["enable Claude to search for papers and cite them in responses","build autonomous research agents that can fetch and analyze papers","integrate paper search into multi-step reasoning chains (search → download → extract → analyze)","allow LLM agents to maintain context across multiple paper retrievals"],"best_for":["developers building Claude-based research assistants","teams creating autonomous literature review agents","LLM application builders who want paper search as a native capability"],"limitations":["MCP tool invocation adds latency (network round-trip to MCP server) vs direct library calls","tool output size is limited by MCP message size constraints — very large PDFs may need chunking","error handling depends on MCP client implementation; some clients may not handle streaming responses","no built-in caching — repeated searches for same query hit source APIs each time","MCP server must be running and accessible; no offline mode"],"requires":["MCP-compatible client (Claude Desktop, custom MCP host, or compatible LLM platform)","MCP server implementation (this artifact provides the server)","network connectivity to MCP server and academic source APIs"],"input_types":["MCP tool invocation with JSON parameters","tool names: search_papers, download_pdf, extract_text, get_metadata"],"output_types":["MCP tool result JSON","structured data (search results, extracted text, metadata)","error messages with diagnostic information"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adamamer20-paper-search-mcp-openai__cap_5","uri":"capability://automation.workflow.batch.paper.search.and.download.with.progress.tracking","name":"batch paper search and download with progress tracking","description":"Supports querying multiple search terms or downloading multiple papers in a single operation, with progress tracking and error recovery. Implements rate-limit awareness to avoid triggering source API throttling, uses exponential backoff for retries, and provides detailed status reporting per item. Enables efficient bulk literature discovery without manual iteration.","intents":["search for papers across multiple related queries in one operation","download a list of papers identified from search results","build literature databases by bulk-importing papers from multiple sources","monitor progress of long-running batch operations"],"best_for":["researchers conducting comprehensive literature reviews","teams building automated paper ingestion pipelines","LLM agents that need to gather papers on multiple related topics"],"limitations":["rate limiting is source-specific and may cause batch operations to slow down or fail partway through","no built-in deduplication across batch results — same paper may appear multiple times if found in multiple sources","error recovery is per-item; failure on one paper doesn't prevent others from processing, but partial failures require manual retry","progress tracking is asynchronous — clients must poll for status or implement callback handlers","batch size limits may apply depending on MCP client implementation"],"requires":["MCP client with support for long-running operations or streaming responses","list of search queries or paper identifiers","sufficient time for batch operation to complete (may take minutes for large batches)"],"input_types":["array of search query strings","array of paper identifiers (DOI, arXiv ID, etc.)","optional: batch configuration (max concurrent requests, timeout per item)"],"output_types":["array of results (one per input item)","per-item status (success, error, retry count)","aggregate statistics (total processed, success rate, failed items)"],"categories":["automation-workflow","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_adamamer20-paper-search-mcp-openai__cap_6","uri":"capability://search.retrieval.source.specific.search.parameter.mapping.and.query.optimization","name":"source-specific search parameter mapping and query optimization","description":"Translates high-level search queries into source-specific query syntax and parameters. Maps common search fields (author, title, year range, subject) to each source's native query language (arXiv's field prefixes, PubMed's MeSH terms, Google Scholar's operators). Optimizes queries for each source's search algorithm to improve result relevance and reduce noise.","intents":["search across sources using a unified query syntax without learning source-specific operators","optimize search queries for better result relevance on each source","filter results by structured criteria (year range, author, subject) consistently across sources","build advanced search interfaces that abstract source differences"],"best_for":["teams building unified search interfaces across multiple sources","LLM agents that need to formulate effective searches without source-specific knowledge","researchers who want consistent search behavior across repositories"],"limitations":["not all search fields are supported by all sources — some sources may ignore unsupported filters","query optimization is heuristic-based and may not produce optimal results for all query types","complex boolean queries may not translate correctly across sources with different query languages","source-specific ranking algorithms mean same query produces different result ordering per source","some sources (Google Scholar) don't expose advanced search parameters via API"],"requires":["search query in unified format or natural language","optional: source hints or preferences"],"input_types":["unified search query object with fields: keywords, author, title, year_min, year_max, subject_category","or natural language query string"],"output_types":["source-specific query strings or parameter objects","mapping metadata (which fields were translated, which were dropped)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":52,"verified":false,"data_access_risk":"moderate","permissions":["MCP client implementation (e.g., Claude Desktop, custom MCP host)","network access to arXiv, PubMed, bioRxiv, medRxiv, Google Scholar, Semantic Scholar, and IACR APIs","API keys for sources that require authentication (Semantic Scholar API key recommended for higher rate limits)","MCP client with file I/O capabilities","network access to PDF hosting servers","sufficient disk space for storing downloaded PDFs","optional: local PDF storage path configuration","PDF file (local or from download capability)","PDF parsing library (e.g., pdfplumber, PyPDF2, or similar)","sufficient memory for large PDFs (100+ page papers)"],"failure_modes":["rate limits vary by source — some APIs (Google Scholar) have aggressive throttling requiring backoff strategies","search result quality and relevance ranking differs significantly between sources; no cross-source result normalization","some sources (Google Scholar) may require proxy rotation or session management to avoid blocking","no full-text search capability — limited to metadata and abstract search on most sources","paywall-protected papers cannot be downloaded unless open-access version exists in indexed sources","PDF download success rates vary by source — some repositories have stricter access controls","no OCR fallback for image-based PDFs; text extraction depends on PDF being digitally encoded","large batch downloads may trigger rate limiting or IP blocking on some sources","extraction quality degrades on scanned/image-based PDFs without OCR support","section detection heuristics may fail on non-standard paper layouts (e.g., some conference proceedings)","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.822664441602283,"quality":0.39,"ecosystem":0.5900000000000001,"match_graph":0.25,"freshness":0.6,"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":"2026-05-03T15:18:25.565Z","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=adamamer20-paper-search-mcp-openai","compare_url":"https://unfragile.ai/compare?artifact=adamamer20-paper-search-mcp-openai"}},"signature":"9abnjEiJ7G2HDq15YftULU/pP/TdeDX775iR7/FSQIG/3nzHjb3LQA2YJT1fIzGgCcB/6VcTfeqzdSLTR+gyBw==","signedAt":"2026-06-19T19:12:06.901Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/adamamer20-paper-search-mcp-openai","artifact":"https://unfragile.ai/adamamer20-paper-search-mcp-openai","verify":"https://unfragile.ai/api/v1/verify?slug=adamamer20-paper-search-mcp-openai","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"}}