AI App Architecture Complexity Scorer MCP Connector for Claude
A+Quantify the structural intricacy and risks of AI-driven application architectures.
This MCP server provides a suite of analytical tools to measure the complexity of AI architectures. It evaluates model dependencies, data pipeline sophistication, and inference patterns to generate a normalized complexity score. Use calculate_complexity_score to get a high-level overview, analyze_model_risk to identify vulnerabilities in model chains, evaluate_data_flow_efficiency to find data bottlenecks, and suggest_architectural_simplification to find ways to reduce system weight.
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