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Professor Ratings

by lulz botUpdated May 4, 2026

Provides MCP access to professor ratings and student review data from universities. Query by professor name, institution, or department to retrieve average quality scores, difficulty ratings, review counts, and feedback summaries. Used by students for course selection, academic researchers for teaching analysis, and developers building university apps.

professor-ratings
student-reviews
education-data
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Overview

The @lulzasaur9192/mcp-professor-ratings server integrates with the Model Context Protocol (MCP) to deliver structured access to aggregated professor ratings data. Sourced from student review platforms, it enables precise queries on faculty performance metrics across thousands of universities. This allows AI models and applications to fetch real-time ratings without scraping, supporting data-driven decisions in education.

Key Capabilities

  • Professor lookup by name, school, or department to identify matching faculty entries.
  • Retrieval of rating summaries including overall score (out of 5), difficulty level, and would-take-again percentage.
  • Access to review aggregates like total reviews, recent trends, and quality breakdowns (clarity, helpfulness).
  • Filtered searches for specific courses or semesters to narrow rating data.

These functions operate via standard MCP tool calls, returning JSON with numeric metrics and text excerpts.

Use Cases

  • Course selection tool: Query professor_ratings for a department at a university to rank professors by average score before enrollment.
  • Academic research dashboard: Pull ratings_trends across institutions to analyze correlations between difficulty ratings and student outcomes.
  • University app integration: Use search_professors in a mobile app to display ratings alongside class schedules.
  • Advisor recommendation system: Fetch review_summaries to suggest professors based on past student feedback for specific majors.

Who This Is For

Students evaluating classes, developers creating edtech apps, academic researchers studying teaching effectiveness, and advisors building personalized recommendations. Requires basic MCP client setup for integration.