# Evolutionary AI - MCP Tool Specification

This document outlines the specification for tools within the Evolutionary AI module that are intended to be integrated with the Model Context Protocol (MCP).

## Current Status: No MCP Tools Defined

The Evolutionary AI module provides genetic algorithm components for internal use by other Codomyrmex modules, including genome representation, population management, selection operators (tournament, roulette, rank, elitism), crossover operators (single-point, two-point, uniform, blend), mutation operators (bit-flip, swap, Gaussian, scramble), and fitness evaluation. These functions are primarily for programmatic integration within the application lifecycle and are not suited for exposure as Model Context Protocol (MCP) tools.

MCP tools are typically designed for discrete, invocable actions or queries that an external agent (like an LLM) would trigger. The internal evolutionary algorithm mechanisms do not fit this paradigm.

If future enhancements to this module introduce features that are appropriate for MCP (e.g., running an evolutionary optimization with configurable parameters and returning the best solution, or querying population fitness statistics), this document will be updated accordingly.

For details on how to use the evolutionary_ai functionalities within your Python code, please refer to the module's `README.md` and `API_SPECIFICATION.md`.

## Navigation Links

- **Parent**: [Project Overview](../README.md)
- **Module Index**: [All Agents](../../AGENTS.md)
- **Documentation**: [Reference Guides](../../../docs/README.md)
- **Home**: [Root README](../../../README.md)
