MCPaeroedu
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AlicenseCqualityAmaintenanceEnables AI assistants to interact with the EduBase educational platform to create quizzes, upload questions, schedule exams, manage educational content, and analyze user results through natural language.200609 npm28MIT- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to interact with the Classavo education platform via natural language for course management, assignments, grading, attendance, polling, and discussions, with strict privacy controls for students.-
- -licenseNot gradedqualityNot gradedmaintenanceEnables AI assistants to interact with the MyWhoosh API using natural language, allowing users to create workouts, manage training sessions, schedule tasks, and adjust settings.-
- AlicenseAqualityCmaintenanceConnects AI assistants to the ADAS platform, enabling them to build, validate, and deploy multi-agent systems through natural language commands without manual configuration.412,040 npm1MIT

dSIPRouter MCP Serverofficial
AlicenseNot gradedqualityDmaintenanceEnables AI assistants to manage dSIPRouter operations such as endpoint groups, carrier groups, inbound mappings, and call data retrieval through natural language.Apache 2.0- FlicenseNot gradedqualityDmaintenanceEnables AI assistants to access educational domain tools including grading, cognitive diagnosis, knowledge tracing, learning path recommendations, and sentiment analysis through FastAPI-powered small models.2-
TDQS
Scored across 24 tools
Each tool targets a distinct function: course creation, content blocks, exams, knowledge graph, DaVinci editing, question bank, and student/report operations. Within each group, tools are clearly differentiated (e.g., block_add_lab vs block_add_pdf). No two tools appear to overlap in purpose.
All tools share the 'aeroedu_' prefix, but the structure after that is inconsistent. Some follow module_verb_noun (e.g., aeroedu_block_add_lab), others module_noun_verb (e.g., aeroedu_davinci_clip_import), and some use just verb or noun (e.g., aeroedu_kg_search, aeroedu_report_exam). Within the same module (e.g., kg), patterns vary (extract, node_detail, path_find, record_visit).
24 tools cover a wide array of educational tasks from course management to video editing. While the count is high, each tool serves a distinct purpose within the platform's scope. The number is slightly above typical but still appropriate for a comprehensive educational server.
The tool set covers major workflows (course creation, content delivery, exam management, knowledge graph, question bank, video editing), but lacks update and delete operations for courses, chapters, lessons, exams, and questions. This creates gaps in lifecycle management that agents may need to work around.