{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_zaeyasa-q1-crafter-mcp","slug":"zaeyasa-q1-crafter-mcp","name":"q1-crafter-mcp","type":"mcp","url":"https://github.com/zaeyasa/q1-crafter-mcp","page_url":"https://unfragile.ai/zaeyasa-q1-crafter-mcp","categories":["mcp-servers","testing-quality"],"tags":["mcp","model-context-protocol","smithery:zaeyasa/q1-crafter-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_zaeyasa-q1-crafter-mcp__cap_0","uri":"capability://search.retrieval.multi.source.academic.search","name":"multi-source academic search","description":"This capability enables querying across 18 academic databases simultaneously, utilizing a smart field-based routing mechanism that directs queries to the most relevant sources based on the subject area. It employs a modular architecture where each database has its own API client, allowing for efficient parallel processing and aggregation of results. The system is designed to handle various data formats and ensures a seamless user experience by abstracting the complexity of multiple API interactions.","intents":["I want to find recent papers on machine learning in drug discovery.","Can I search multiple academic databases at once for literature on climate change?","How do I query specific fields like medicine or computer science effectively?"],"best_for":["researchers needing comprehensive literature reviews"],"limitations":["Dependent on the availability of external APIs; if an API is down, results may be incomplete."],"requires":["Python 3.10+","API keys for paid sources if needed"],"input_types":["text"],"output_types":["structured data","JSON"],"categories":["search-retrieval","academic-research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_zaeyasa-q1-crafter-mcp__cap_1","uri":"capability://data.processing.analysis.intelligent.deduplication","name":"intelligent deduplication","description":"This capability implements a two-phase deduplication process that first checks for exact matches using DOI and then applies a fuzzy matching algorithm based on title similarity with a 92% Levenshtein threshold. This ensures that duplicate entries are effectively filtered out, providing cleaner and more relevant search results. The architecture leverages Pydantic models for data validation and consistency throughout the deduplication process.","intents":["How can I ensure my search results don't include duplicate papers?","I need to clean up my literature list to remove redundant entries.","Can I get a unique set of references from my search results?"],"best_for":["scholars compiling literature reviews"],"limitations":["Fuzzy matching may occasionally miss duplicates with minor title variations."],"requires":["Python 3.10+"],"input_types":["structured data"],"output_types":["structured data"],"categories":["data-processing-analysis","literature-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_zaeyasa-q1-crafter-mcp__cap_2","uri":"capability://data.processing.analysis.literature.analysis.and.gap.detection","name":"literature analysis and gap detection","description":"This capability analyzes the retrieved literature to identify research gaps, extract keywords using TF-IDF, and validate citations. It employs natural language processing techniques to assess the content of papers and generate insights about trends and themes. The architecture is designed to allow easy integration of various analysis tools, making it flexible for future enhancements.","intents":["Can I find gaps in the current research on renewable energy?","I need to extract keywords from my literature search results.","How do I validate the citations in my references?"],"best_for":["research teams conducting systematic reviews"],"limitations":["Analysis quality depends on the quality of the input data; poor quality papers may skew results."],"requires":["Python 3.10+"],"input_types":["structured data"],"output_types":["structured data","reports"],"categories":["data-processing-analysis","academic-research"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_zaeyasa-q1-crafter-mcp__cap_3","uri":"capability://data.processing.analysis.visualization.of.publication.trends","name":"visualization of publication trends","description":"This capability generates visual representations of publication trends, source distribution, and citation networks using libraries like Mermaid for diagram generation. It processes the analyzed data to create charts and graphs that help researchers visualize complex relationships and trends in their literature. The design allows for easy customization of visual outputs to meet specific user needs.","intents":["How can I visualize trends in my research area over the past decade?","Can I create a citation network for my key papers?","I need a chart showing the distribution of sources in my literature review."],"best_for":["data scientists and researchers needing visual insights"],"limitations":["Visualizations may require additional formatting for publication-ready quality."],"requires":["Python 3.10+","Mermaid library"],"input_types":["structured data"],"output_types":["charts","diagrams"],"categories":["data-processing-analysis","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_zaeyasa-q1-crafter-mcp__cap_4","uri":"capability://data.processing.analysis.apa.7.citation.formatting","name":"apa 7 citation formatting","description":"This capability formats citations and references according to APA 7th edition standards, handling complex rules for different author counts and DOI formatting. It uses a set of predefined templates and rules encoded in Pydantic models to ensure compliance with citation standards. The architecture allows for easy updates to citation rules as standards evolve.","intents":["How do I format my references in APA 7 style?","Can I automatically generate citations for my bibliography?","I need to ensure my in-text citations are correctly formatted."],"best_for":["students and researchers preparing manuscripts"],"limitations":["Limited to APA 7; does not support other citation styles without modification."],"requires":["Python 3.10+"],"input_types":["structured data"],"output_types":["formatted text","structured data"],"categories":["data-processing-analysis","citation-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_zaeyasa-q1-crafter-mcp__cap_5","uri":"capability://automation.workflow.docx.manuscript.generation","name":"docx manuscript generation","description":"This capability assembles all components of a research manuscript, including title pages, sections, and references, into a formatted .docx file. It leverages the Python-docx library to create structured documents that adhere to academic standards. The architecture is modular, allowing for easy updates and customization of document templates based on user preferences.","intents":["Can I generate a complete manuscript for submission?","How do I create a formatted .docx file from my research data?","I need to compile my findings into a structured document."],"best_for":["academics preparing submissions to journals"],"limitations":["Formatting may require manual adjustments for specific journal requirements."],"requires":["Python 3.10+","Python-docx library"],"input_types":["structured data"],"output_types":["DOCX file"],"categories":["automation-workflow","document-generation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":35,"verified":false,"data_access_risk":"high","permissions":["Python 3.10+","API keys for paid sources if needed","Mermaid library","Python-docx library"],"failure_modes":["Dependent on the availability of external APIs; if an API is down, results may be incomplete.","Fuzzy matching may occasionally miss duplicates with minor title variations.","Analysis quality depends on the quality of the input data; poor quality papers may skew results.","Visualizations may require additional formatting for publication-ready quality.","Limited to APA 7; does not support other citation styles without modification.","Formatting may require manual adjustments for specific journal requirements.","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"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":"2026-05-24T12:16:28.695Z","last_scraped_at":"2026-05-03T15:19:34.639Z","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=zaeyasa-q1-crafter-mcp","compare_url":"https://unfragile.ai/compare?artifact=zaeyasa-q1-crafter-mcp"}},"signature":"N7Qgiikx5chFtIjsPWgfFl6A9oJd2t027JmkFUYnqR+VpM6UAdDwlZ/oifH+3Mh4CT76NikXFQYmk5mUo7vlDQ==","signedAt":"2026-06-22T06:01:24.373Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/zaeyasa-q1-crafter-mcp","artifact":"https://unfragile.ai/zaeyasa-q1-crafter-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=zaeyasa-q1-crafter-mcp","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"}}