ayushwalunj1101/project-evaluator-mcp
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MCP Server for AI-powered project innovation evaluation is designed to assess and enhance project ideas using advanced AI technologies.
APEX Judge: An Agentic AI Framework for Comprehensive Project Evaluation Using Multi-Modal Context and Small Language Models
The Project Evaluator MCP Server provides AI-powered evaluation of projects, code, and ideas. It combines the Model Context Protocol (MCP) powered by Perplexity AI with a custom-trained Small Language Model (SLM) for problem-solution relevance analysis.
Features:
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Innovation & Novelty Evaluation – Assess individual projects, run batch checks, or compare projects with detailed scoring and recommendations.
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Code Evaluation – Analyze code repositories for quality, maintainability, security, and scalability.
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Patentability Check – Estimate novelty and potential for patentability, with insights into prior art risks.
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Problem-Solution Fit (Custom SLM) – Evaluate how relevant a solution is to a given problem, returning results such as Highly Relevant, Relevant, Less Relevant, or Irrelevant.
How It Works:
MCP + Perplexity → Handles innovation, code, and patentability evaluations.
Custom SLM → Handles problem-solution fit evaluations.