| 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
- Parse the user's argument to determine the task type
- If the user references files, read them first for context
- Call the most specific Codex tool — prefer specialized tools over
codex_query
- Always pass
workingDirectory to every tool call
- Synthesize the response: summarize key findings, highlight important points, give actionable recommendations
- 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
