codex

name	codex
description	Ask OpenAI Codex for a second opinion — code reviews, explanations, plan critiques, performance analysis, or general questions
argument-hint	<task or question>
allowed-tools	Read, Glob, Grep, Bash, mcp__codex__codex_query, mcp__codex__codex_review_code, mcp__codex__codex_review_plan, mcp__codex__codex_explain_code, mcp__codex__codex_plan_perf, mcp__codex__codex_implement

You are invoking Codex to get a second opinion. Route the user's request to the most appropriate Codex MCP tool.

Tool Selection

Pick the best tool based on the user's request:

Request Type	Tool	Key Parameters
Code review, diff review	mcp__codex__codex_review_code	target (diff range or file), focusAreas
Plan critique	mcp__codex__codex_review_plan	plan, codebasePath
Explain code	mcp__codex__codex_explain_code	target (file/function), depth
Performance analysis	mcp__codex__codex_plan_perf	target, metrics
Implement/fix (writes code)	mcp__codex__codex_implement	task
General question	mcp__codex__codex_query	prompt

Instructions

1. Parse the user's argument to determine the task type
2. If the user references files, read them first for context
3. Call the most specific Codex tool — prefer specialized tools over codex_query
4. Always pass workingDirectory to every tool call
5. Synthesize the response: summarize key findings, highlight important points, give actionable recommendations
6. Only use codex_implement if the user explicitly asks Codex to make changes

Examples

• /codex review my recent changes → codex_review_code with target "HEAD~1..HEAD"
• /codex explain src/lib/exec.ts → codex_explain_code with target "src/lib/exec.ts"
• /codex is my approach to caching correct? → codex_query with the question
• /codex optimize the response parsing → codex_plan_perf with target
• /codex implement error handling for timeouts → codex_implement with task