NSAF MCP Server provides AI assistants with access to the Neuro-Symbolic Autonomy Framework, focusing on Self-Constructing Meta-Agents (SCMA) that can evolve and optimize their neural network architectures through generations. The server exposes tools for running evolutionary processes with customizable parameters and comparing different agent architectures, enabling AI assistants to leverage advanced neural architecture search capabilities without requiring deep expertise in evolutionary algorithms. Built with TensorFlow and designed to work seamlessly with Claude Desktop and Cline, this implementation is particularly valuable for researchers and developers exploring autonomous AI systems that can self-design and adapt to different problem domains.
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Highlight AIRun NSAF evolution with specified parameters. Parameters: population_size (integer, default: 20), generations (integer, default: 10), mutation_rate (float in range 0.0-1.0, default: 0.2), crossover_rate (float in range 0.0-1.0, default: 0.7), architecture_complexity (string: 'simple', 'medium', 'complex', default: 'medium')
Compare different NSAF agent architectures. Parameters: architectures (array of strings, default: ['simple', 'medium', 'complex'])