{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_alperenkocyigit-call-for-papers-mcp","slug":"alperenkocyigit-call-for-papers-mcp","name":"call-for-papers-mcp","type":"mcp","url":"https://github.com/alperenkocyigit/call-for-papers-mcp","page_url":"https://unfragile.ai/alperenkocyigit-call-for-papers-mcp","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","smithery:alperenkocyigit/call-for-papers-mcp"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_alperenkocyigit-call-for-papers-mcp__cap_0","uri":"capability://tool.use.integration.call.for.papers.discovery.via.mcp.protocol","name":"call-for-papers discovery via mcp protocol","description":"Exposes academic conference and journal call-for-papers (CFP) data through the Model Context Protocol, allowing Claude and other MCP-compatible clients to query, filter, and retrieve structured CFP metadata without direct API calls. Implements MCP resource and tool handlers that translate natural language queries into CFP database lookups, returning standardized JSON with submission deadlines, conference dates, and venue details.","intents":["I want Claude to help me find relevant conferences to submit my research paper to","I need to search for open calls matching my research area and timeline","I want to integrate CFP discovery into my research workflow without leaving my AI assistant"],"best_for":["academic researchers and PhD students managing paper submission pipelines","AI agents that need to autonomously discover publication venues","research teams building custom submission workflow tools"],"limitations":["CFP data freshness depends on upstream source updates — no real-time scraping guarantee","Query filtering limited to whatever metadata fields the upstream CFP source provides","No built-in deduplication across multiple CFP aggregators if multiple sources are indexed"],"requires":["MCP client compatible with the MCP specification (Claude Desktop, custom MCP host, or compatible agent framework)","Network access to the MCP server endpoint","Access to the underlying CFP data source (likely a public CFP aggregator or database)"],"input_types":["natural language queries (e.g., 'machine learning conferences in 2025')","structured filter parameters (deadline date range, research area, venue type)"],"output_types":["JSON-structured CFP records with fields: conference name, submission deadline, conference date, venue, research areas, submission URL","filtered lists of matching calls ranked by relevance or deadline proximity"],"categories":["tool-use-integration","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alperenkocyigit-call-for-papers-mcp__cap_1","uri":"capability://data.processing.analysis.structured.cfp.metadata.extraction.and.normalization","name":"structured cfp metadata extraction and normalization","description":"Parses and normalizes heterogeneous call-for-papers data from upstream sources into a consistent schema with standardized field mappings (deadline, conference date, venue, research areas, submission requirements). Uses schema validation to ensure all returned CFP records conform to a predictable structure, enabling reliable downstream filtering and ranking by MCP tools.","intents":["I want to filter conferences by submission deadline and research area without parsing inconsistent data formats","I need to rank CFPs by deadline urgency or relevance to my research","I want to export CFP data to a spreadsheet or calendar with consistent field names"],"best_for":["researchers building custom submission tracking systems","research groups standardizing CFP data across multiple aggregator sources","AI agents that need predictable, schema-validated data for downstream decision-making"],"limitations":["Normalization quality depends on upstream source data completeness — missing fields in source data cannot be inferred","Custom or non-standard submission requirements may be lost during normalization to a fixed schema","No automatic detection of duplicate CFPs across multiple sources"],"requires":["Access to raw CFP data from one or more upstream sources (e.g., WikiCFP, OpenReview, conference websites)","Schema definition file (likely JSON Schema or similar) defining the normalized CFP structure"],"input_types":["raw CFP records from heterogeneous sources (HTML, JSON, CSV, RSS feeds)","schema definition specifying required and optional fields"],"output_types":["normalized JSON objects conforming to the defined CFP schema","validation error reports for records that fail schema conformance"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alperenkocyigit-call-for-papers-mcp__cap_2","uri":"capability://search.retrieval.deadline.aware.cfp.filtering.and.ranking","name":"deadline-aware cfp filtering and ranking","description":"Implements temporal and relevance-based filtering logic that ranks CFPs by submission deadline proximity, conference date, and match to user research interests. Uses date arithmetic and keyword matching against research area tags to surface the most actionable calls first, enabling researchers to prioritize submissions by urgency and fit.","intents":["Show me the conferences with the closest submission deadlines in my research area","Filter out conferences that have already passed their deadline","Rank CFPs by how well they match my research interests and timeline"],"best_for":["individual researchers managing multiple paper submissions across venues","research groups coordinating submissions and allocating effort by deadline","AI agents autonomously identifying high-priority submission opportunities"],"limitations":["Ranking is based on deadline and keyword matching only — does not account for conference prestige, acceptance rates, or citation impact","Research area matching relies on keyword overlap; may miss semantically related but differently-named research areas","No personalization based on user's past submissions or publication history"],"requires":["normalized CFP records with deadline and conference date fields","user research interests or keywords to filter against","current date/time for deadline proximity calculations"],"input_types":["normalized CFP records (JSON)","user research area keywords or tags (string array)","optional deadline filter range (e.g., 'within 60 days')"],"output_types":["ranked list of CFP records sorted by deadline proximity and relevance score","metadata including days-until-deadline and relevance score for each result"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alperenkocyigit-call-for-papers-mcp__cap_3","uri":"capability://tool.use.integration.mcp.tool.and.resource.handler.implementation","name":"mcp tool and resource handler implementation","description":"Implements the MCP server specification with tool handlers for querying CFPs and resource handlers for exposing CFP metadata as discoverable resources. Uses MCP's request-response protocol to translate Claude's natural language tool calls into structured CFP queries, with proper error handling and response formatting that conforms to MCP's JSON-RPC message structure.","intents":["I want Claude to call CFP search functions as native tools without custom API wrappers","I need MCP resources that expose CFP data in a format Claude can browse and reference","I want to build a multi-tool agent that combines CFP discovery with other research tools"],"best_for":["developers building MCP servers for academic research workflows","teams integrating CFP discovery into larger Claude-based agent systems","researchers using Claude Desktop with custom MCP server configurations"],"limitations":["MCP protocol overhead adds latency compared to direct API calls — typical round-trip ~100-500ms per query","Tool definitions must be manually maintained in sync with backend CFP query capabilities","No built-in caching of CFP results — each query hits the upstream source unless explicitly cached"],"requires":["MCP SDK for the implementation language (Python, TypeScript, Rust, etc.)","MCP client compatible with the server (Claude Desktop, custom host, or agent framework)","proper JSON-RPC 2.0 message handling and error response formatting"],"input_types":["MCP tool call requests with parameters (research area, deadline range, etc.)","MCP resource requests for CFP metadata URIs"],"output_types":["MCP tool response JSON with CFP results","MCP resource responses with CFP metadata and content","MCP error responses with proper error codes and messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_alperenkocyigit-call-for-papers-mcp__cap_4","uri":"capability://data.processing.analysis.multi.source.cfp.aggregation.and.deduplication","name":"multi-source cfp aggregation and deduplication","description":"Aggregates call-for-papers data from multiple upstream sources (e.g., WikiCFP, OpenReview, conference websites) and deduplicates records based on conference name, deadline, and venue matching. Uses fuzzy matching or exact field comparison to identify duplicate CFPs across sources, returning a unified view of available calls without redundant entries.","intents":["I want to search across all major CFP sources without manually checking each one","I need a single deduplicated list of conferences without seeing the same call multiple times","I want to know which sources have the most up-to-date CFP information for a given conference"],"best_for":["research groups that need comprehensive CFP coverage across multiple aggregators","researchers building custom CFP databases that combine multiple sources","AI agents that need a unified CFP index for discovery tasks"],"limitations":["Deduplication accuracy depends on data quality from upstream sources — inconsistent conference naming reduces match rate","No automatic conflict resolution when different sources have conflicting deadline or venue information","Aggregation latency scales with number of sources — fetching from 5+ sources may add 1-2 seconds per query","No built-in source reliability scoring — treats all sources equally regardless of update frequency or accuracy"],"requires":["connectors or adapters for each upstream CFP source (API, web scraping, RSS feed parsing)","deduplication logic with configurable matching thresholds (exact match, fuzzy match, etc.)","unified schema that can accommodate fields from all sources"],"input_types":["raw CFP records from multiple heterogeneous sources","deduplication configuration (matching thresholds, priority order)"],"output_types":["deduplicated CFP records with source attribution (which sources provided this call)","deduplication report showing matched duplicates and merge decisions"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":26,"verified":false,"data_access_risk":"high","permissions":["MCP client compatible with the MCP specification (Claude Desktop, custom MCP host, or compatible agent framework)","Network access to the MCP server endpoint","Access to the underlying CFP data source (likely a public CFP aggregator or database)","Access to raw CFP data from one or more upstream sources (e.g., WikiCFP, OpenReview, conference websites)","Schema definition file (likely JSON Schema or similar) defining the normalized CFP structure","normalized CFP records with deadline and conference date fields","user research interests or keywords to filter against","current date/time for deadline proximity calculations","MCP SDK for the implementation language (Python, TypeScript, Rust, etc.)","MCP client compatible with the server (Claude Desktop, custom host, or agent framework)"],"failure_modes":["CFP data freshness depends on upstream source updates — no real-time scraping guarantee","Query filtering limited to whatever metadata fields the upstream CFP source provides","No built-in deduplication across multiple CFP aggregators if multiple sources are indexed","Normalization quality depends on upstream source data completeness — missing fields in source data cannot be inferred","Custom or non-standard submission requirements may be lost during normalization to a fixed schema","No automatic detection of duplicate CFPs across multiple sources","Ranking is based on deadline and keyword matching only — does not account for conference prestige, acceptance rates, or citation impact","Research area matching relies on keyword overlap; may miss semantically related but differently-named research areas","No personalization based on user's past submissions or publication history","MCP protocol overhead adds latency compared to direct API calls — typical round-trip ~100-500ms per query","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.2,"ecosystem":0.48999999999999994,"match_graph":0.25,"freshness":0.52,"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":"2026-05-03T15:19:25.720Z","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=alperenkocyigit-call-for-papers-mcp","compare_url":"https://unfragile.ai/compare?artifact=alperenkocyigit-call-for-papers-mcp"}},"signature":"OULsRs7V2TMrsSl+Asok3m71zxfBwgy+T9JSJSsBOPABOv3i+oxBRp6O0e2Y9poFWwD+M6LDFywFX1dljjIzBA==","signedAt":"2026-06-22T11:25:26.643Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/alperenkocyigit-call-for-papers-mcp","artifact":"https://unfragile.ai/alperenkocyigit-call-for-papers-mcp","verify":"https://unfragile.ai/api/v1/verify?slug=alperenkocyigit-call-for-papers-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"}}