AI tech lead for coding agents with validation and impact analysis
{
"mcpServers": {
"io-github-n0zer0d4y-athena-protocol": {
"command": "<see-readme>",
"args": []
}
}
}No install config available. Check the server's README for setup instructions.
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AI tech lead for coding agents with validation and impact analysis
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Last commit 94 days ago. 6 stars.
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Transport: stdio. Works with Claude Desktop, Cursor, Claude Code, and most MCP clients.
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An intelligent MCP server that acts as an AI tech lead for coding agents—providing expert validation, impact analysis, and strategic guidance before code changes are made. Like a senior engineer reviewing your approach, Athena Protocol helps AI agents catch critical issues early, validate assumptions against the actual codebase, and optimize their problem-solving strategies. The result: higher quality code, fewer regressions, and more thoughtful architectural decisions.
Key Feature: Precision file analysis with analysisTargets - achieve 70-85% token reduction and 3-4× faster performance with precision-targeted code analysis. See Enhanced File Analysis for details
Imagine LLMs working with context so refined and targeted that they eliminate guesswork, reduce errors by 80%, and deliver code with the precision of seasoned architects—transforming how AI agents understand and enhance complex codebases.
This server handles API keys for multiple LLM providers. Ensure your .env file is properly secured and never committed to version control. The server validates all API keys on startup and provides detailed error messages for configuration issues.
The Athena Protocol MCP Server provides systematic thinking validation for AI coding agents. It supports 14 LLM providers and offers various validation tools including thinking validation, impact analysis, assumption checking, dependency mapping, and thinking optimization.
Key features:
This module depends upon a knowledge of Node.js and npm.
npm install
npm run build
The Athena Protocol uses 100% environment-driven configuration - no hardcoded provider values or defaults. Configure everything through your .env file:
cp .env.example .env
Edit .env and configure your provider:
DEFAULT_LLM_PROVIDER (e.g., openai, anthropic, google)Validate and test:
npm install
npm run build
npm run validate-config # Validates your .env configurat