{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_ai-research-airesearch","slug":"ai-research-airesearch","name":"Airesearch","type":"mcp","url":"https://smithery.ai/servers/ai-research/Airesearch","page_url":"https://unfragile.ai/ai-research-airesearch","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:ai-research/Airesearch"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_ai-research-airesearch__cap_0","uri":"capability://tool.use.integration.mcp.protocol.server.instantiation.and.lifecycle.management","name":"mcp protocol server instantiation and lifecycle management","description":"Airesearch implements a Model Context Protocol (MCP) server that handles bidirectional communication between Claude (or other MCP-compatible clients) and research tools. It manages server initialization, request routing, resource discovery, and graceful shutdown using the MCP specification's transport layer (stdio, SSE, or custom). The server exposes tools and resources through MCP's standardized schema, allowing clients to dynamically discover and invoke capabilities without hardcoded integrations.","intents":["I want to connect Claude to research tools without building custom API wrappers","I need Claude to discover available research capabilities at runtime","I want to expose research functionality through a standardized protocol that works with multiple MCP clients"],"best_for":["Teams building AI research workflows with Claude","Developers integrating research tools into MCP-compatible applications","Organizations standardizing on MCP for tool orchestration"],"limitations":["MCP transport overhead adds ~50-200ms per request depending on transport mechanism (stdio vs network)","No built-in caching of tool schemas — clients must re-discover on each connection","Limited to MCP specification constraints — cannot expose arbitrary streaming or WebSocket patterns outside MCP's defined message types"],"requires":["MCP client implementation (e.g., Claude desktop app, custom MCP client library)","Python 3.8+ or Node.js 16+ depending on Airesearch implementation","Network connectivity if using network-based MCP transport"],"input_types":["JSON-RPC requests following MCP specification","Tool invocation parameters as structured JSON","Resource URIs and query parameters"],"output_types":["JSON-RPC responses with tool results","Structured resource data (research papers, datasets, metadata)","Error responses with diagnostic information"],"categories":["tool-use-integration","mcp-protocol"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_1","uri":"capability://search.retrieval.research.paper.discovery.and.retrieval.via.semantic.search","name":"research paper discovery and retrieval via semantic search","description":"Airesearch provides semantic search capabilities to discover research papers from academic databases (likely arXiv, PubMed, or similar) using natural language queries. It converts user queries into embeddings and matches them against indexed paper metadata and abstracts, returning ranked results with relevance scores. The implementation likely uses vector similarity search (cosine distance or similar) against pre-indexed embeddings, enabling researchers to find papers without learning database-specific query syntax.","intents":["I want to search for papers on a topic using natural language without knowing exact keywords","I need to find related work for my research with semantic relevance ranking","I want to discover papers across multiple academic databases with a single query"],"best_for":["Researchers conducting literature reviews","PhD students building related work sections","Teams building AI-powered research assistants"],"limitations":["Search results limited to indexed databases — cannot discover very recent preprints if indexing lag exists","Semantic search may miss papers with non-standard terminology or domain-specific jargon not well-represented in embeddings","No full-text search capability — only searches metadata and abstracts, not paper body content"],"requires":["Access to academic paper indices (arXiv API, PubMed API, or similar)","Embedding model for query vectorization (likely OpenAI embeddings or open-source alternative)","Network connectivity to academic databases"],"input_types":["Natural language query strings","Optional filters (publication date range, author, venue)"],"output_types":["Ranked list of papers with titles, authors, abstracts, URLs","Relevance scores and metadata (publication date, citation count)"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_2","uri":"capability://data.processing.analysis.research.paper.content.extraction.and.summarization","name":"research paper content extraction and summarization","description":"Airesearch extracts structured content from research papers (title, authors, abstract, methodology, results, conclusions) and generates summaries at multiple granularity levels (abstract-only, key findings, full paper summary). It likely uses PDF parsing with layout-aware extraction (e.g., pdfplumber or similar) combined with LLM-based summarization to produce coherent, hierarchical summaries that preserve research intent. The extraction preserves citations and references for downstream analysis.","intents":["I want to quickly understand a paper's contributions without reading the full text","I need to extract methodology and results sections for comparison with other papers","I want to get structured metadata from papers for building research databases"],"best_for":["Researchers triaging large numbers of papers","Teams building research knowledge bases","Literature review automation workflows"],"limitations":["PDF extraction quality varies with paper formatting — tables and figures may not extract cleanly","Summarization may lose nuanced details or miss important caveats in methodology","Cannot extract content from papers behind paywalls — requires open-access or author-provided PDFs","Processing time scales with paper length — 50+ page papers may take 10-30 seconds per paper"],"requires":["PDF file access (local or via URL)","PDF parsing library (pdfplumber, PyPDF2, or similar)","LLM access for summarization (OpenAI API, local model, or similar)","Sufficient token budget for processing long papers"],"input_types":["PDF files or URLs to papers","Optional summarization level parameter (abstract, key-findings, full)"],"output_types":["Structured JSON with title, authors, abstract, extracted sections","Generated summaries at specified granularity","Citation references and bibliography"],"categories":["data-processing-analysis","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_3","uri":"capability://search.retrieval.citation.graph.traversal.and.relationship.mapping","name":"citation graph traversal and relationship mapping","description":"Airesearch enables traversal of citation networks to discover related papers, influential works, and research lineage. It implements graph traversal algorithms (BFS, DFS, or PageRank-style citation weighting) to find papers that cite a given work, papers cited by a work, and papers in the same citation cluster. The implementation likely queries citation indices (CrossRef, Semantic Scholar API, or similar) and builds transitive relationships, allowing researchers to explore research genealogy and impact.","intents":["I want to find all papers that cite a specific work to understand its impact","I need to trace the research lineage from a foundational paper to recent work","I want to find papers in the same research cluster without manual keyword searching"],"best_for":["Researchers mapping research landscapes","Teams analyzing research trends and influence","Citation analysis and bibliometric studies"],"limitations":["Citation data may be incomplete or delayed — recent papers may not have full citation information","Graph traversal depth is limited to prevent combinatorial explosion — typically 2-3 hops maximum","Citation indices have different coverage — some papers may not be indexed in all sources","No real-time citation updates — data is typically refreshed daily or weekly"],"requires":["Access to citation index APIs (CrossRef, Semantic Scholar, or similar)","API keys for citation services if rate-limited","Graph traversal library (networkx or similar) for local computation"],"input_types":["Paper identifier (DOI, arXiv ID, or title)","Traversal direction (citing papers, cited papers, or both)","Traversal depth limit (number of hops)"],"output_types":["Graph structure with papers as nodes and citations as edges","Ranked lists of related papers by citation weight or recency","Metadata for each discovered paper"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_4","uri":"capability://search.retrieval.research.dataset.discovery.and.metadata.extraction","name":"research dataset discovery and metadata extraction","description":"Airesearch discovers research datasets from repositories (Kaggle, Zenodo, Figshare, or domain-specific repositories) using semantic search and metadata matching. It extracts dataset metadata (size, format, license, description, citation information) and provides access to dataset documentation and schemas. The implementation queries dataset indices and parses repository APIs to provide standardized dataset information regardless of source repository.","intents":["I want to find datasets relevant to my research question","I need to understand dataset structure and licensing before using it","I want to discover benchmark datasets for evaluating my models"],"best_for":["Researchers building datasets for studies","ML engineers finding training data","Teams conducting reproducible research"],"limitations":["Dataset discovery limited to indexed repositories — private or institutional datasets not discoverable","Metadata quality varies by repository — some datasets lack detailed documentation","No automatic dataset download or integration — requires manual access to repository","License information may be incomplete or ambiguous for older datasets"],"requires":["Access to dataset repository APIs (Kaggle API, Zenodo API, etc.)","API keys for authenticated access to some repositories","Network connectivity to dataset repositories"],"input_types":["Natural language dataset queries","Optional filters (domain, data type, size range, license type)"],"output_types":["Ranked list of datasets with metadata","Dataset descriptions and documentation links","Schema information and sample records","License and citation information"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_5","uri":"capability://data.processing.analysis.research.methodology.comparison.and.synthesis","name":"research methodology comparison and synthesis","description":"Airesearch analyzes and compares methodologies across multiple papers, extracting methodology descriptions, parameters, and results to enable systematic comparison. It uses structured extraction (likely with LLM-based parsing) to identify methodology components (data preprocessing, model architecture, training procedures, evaluation metrics) and creates comparison matrices. The synthesis capability identifies common patterns, variations, and trade-offs across methodologies, helping researchers understand the landscape of approaches.","intents":["I want to compare methodologies across papers to understand best practices","I need to identify which methodology components correlate with better results","I want to synthesize a methodology survey from multiple papers"],"best_for":["Researchers conducting methodology surveys","Teams designing new research approaches","PhD students understanding methodology landscape"],"limitations":["Methodology extraction depends on paper clarity — poorly documented methods may not extract cleanly","Comparison matrices are only as good as extracted information — missing details reduce comparison quality","No automatic identification of methodological equivalence — different names for similar approaches may not be recognized","Requires processing multiple papers — scales with number of papers in comparison"],"requires":["Access to paper content (via extraction capability)","LLM for methodology parsing and synthesis","Sufficient token budget for processing multiple papers"],"input_types":["List of paper identifiers or full paper content","Optional methodology aspects to focus on (e.g., 'data preprocessing', 'model architecture')"],"output_types":["Structured methodology components extracted from each paper","Comparison matrices showing methodology variations","Synthesis summary identifying patterns and trade-offs","Recommendations for methodology selection"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_6","uri":"capability://planning.reasoning.research.hypothesis.generation.and.validation.planning","name":"research hypothesis generation and validation planning","description":"Airesearch assists in generating research hypotheses based on literature analysis and planning validation experiments. It analyzes existing research to identify gaps, contradictions, and unexplored areas, then suggests hypotheses grounded in literature. The validation planning capability outlines experiment designs, required datasets, and evaluation metrics based on similar studies. This uses reasoning patterns (gap analysis, contradiction identification) combined with research knowledge to suggest novel research directions.","intents":["I want to identify research gaps and generate hypotheses for my work","I need to design an experiment to validate my hypothesis","I want to understand what datasets and metrics are standard for my research area"],"best_for":["PhD students planning research projects","Researchers exploring new research directions","Teams conducting systematic literature reviews"],"limitations":["Hypothesis generation is suggestive, not definitive — requires human validation and domain expertise","Validation plans are templates based on similar studies — may not account for domain-specific constraints","No access to unpublished research or institutional knowledge — only based on published literature","Requires substantial literature context — works better with 10+ related papers"],"requires":["Access to research papers and literature (via extraction capability)","LLM for reasoning and synthesis","Domain knowledge from user for validation"],"input_types":["Research topic or area of interest","Existing papers or literature context","Optional constraints (available resources, timeline)"],"output_types":["Generated hypotheses with literature grounding","Gap analysis identifying unexplored areas","Experiment design templates","Required datasets and evaluation metrics","Related work summary"],"categories":["planning-reasoning","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_ai-research-airesearch__cap_7","uri":"capability://search.retrieval.research.reproducibility.verification.and.code.discovery","name":"research reproducibility verification and code discovery","description":"Airesearch discovers and analyzes code repositories associated with research papers (GitHub, Zenodo, supplementary materials) to verify reproducibility and extract implementation details. It parses repository metadata, identifies code language and dependencies, and extracts key implementation components. The verification capability checks for documentation, test coverage, and dependency specifications to assess reproducibility maturity. This enables researchers to evaluate whether papers provide sufficient code and data for reproduction.","intents":["I want to find the code implementation for a paper I'm reading","I need to assess whether a paper is reproducible before building on it","I want to understand the implementation details beyond what's described in the paper"],"best_for":["Researchers evaluating paper reproducibility","Teams implementing published methods","Reproducibility auditors and meta-researchers"],"limitations":["Code discovery depends on authors providing links — many papers lack associated code","Code quality and documentation vary widely — presence of code doesn't guarantee reproducibility","Dependency extraction may be incomplete — some implicit dependencies may not be captured","No automatic code execution or testing — only static analysis of code structure"],"requires":["Access to paper supplementary materials and author websites","GitHub API access for repository analysis","Code parsing libraries for language-specific analysis"],"input_types":["Paper identifier or full paper content","Optional programming language filter"],"output_types":["Links to code repositories","Repository metadata (language, stars, last update)","Dependency lists and version specifications","Documentation quality assessment","Reproducibility maturity score"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":25,"verified":false,"data_access_risk":"high","permissions":["MCP client implementation (e.g., Claude desktop app, custom MCP client library)","Python 3.8+ or Node.js 16+ depending on Airesearch implementation","Network connectivity if using network-based MCP transport","Access to academic paper indices (arXiv API, PubMed API, or similar)","Embedding model for query vectorization (likely OpenAI embeddings or open-source alternative)","Network connectivity to academic databases","PDF file access (local or via URL)","PDF parsing library (pdfplumber, PyPDF2, or similar)","LLM access for summarization (OpenAI API, local model, or similar)","Sufficient token budget for processing long papers"],"failure_modes":["MCP transport overhead adds ~50-200ms per request depending on transport mechanism (stdio vs network)","No built-in caching of tool schemas — clients must re-discover on each connection","Limited to MCP specification constraints — cannot expose arbitrary streaming or WebSocket patterns outside MCP's defined message types","Search results limited to indexed databases — cannot discover very recent preprints if indexing lag exists","Semantic search may miss papers with non-standard terminology or domain-specific jargon not well-represented in embeddings","No full-text search capability — only searches metadata and abstracts, not paper body content","PDF extraction quality varies with paper formatting — tables and figures may not extract cleanly","Summarization may lose nuanced details or miss important caveats in methodology","Cannot extract content from papers behind paywalls — requires open-access or author-provided PDFs","Processing time scales with paper length — 50+ page papers may take 10-30 seconds per paper","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.26,"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":"2026-05-24T12:16:25.635Z","last_scraped_at":"2026-05-03T15:19:06.728Z","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=ai-research-airesearch","compare_url":"https://unfragile.ai/compare?artifact=ai-research-airesearch"}},"signature":"ZIECoZFgQFl2jpYQSC5aVMRcFkqHxklsdcSMtHMEIBUMeSewwITmi91hk4QI1S7Q65AU7YzpvLj/dcOPOK8HAQ==","signedAt":"2026-06-22T04:36:26.840Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/ai-research-airesearch","artifact":"https://unfragile.ai/ai-research-airesearch","verify":"https://unfragile.ai/api/v1/verify?slug=ai-research-airesearch","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"}}