codex-local-mcp
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- AlicenseNot gradedqualityBmaintenanceEnables ChatGPT conversations to directly inspect local workspaces and delegate coding tasks to a local Codex agent, with controlled concurrency, reusable sessions, and task/usage tracking.587 npmMIT
- AlicenseAqualityCmaintenanceEnables MCP-compatible coding agents to execute the local Codex CLI as a coding agent, including code reviews, session resume/fork, and CLI diagnostics.93 npmMIT
- AlicenseCqualityFmaintenanceConnects AI assistants like Claude to the Codex CLI for code analysis, editing, and execution. Supports file references with @ syntax, sandboxed code execution with approval workflows, and structured code changes for automated refactoring and documentation.893 npm178MIT
- FlicenseNot gradedqualityBmaintenanceEnables external AI (ChatGPT, Claude) to securely access a local workspace via Cloudflare Tunnel, providing file operations, shell commands, Git operations, and more with bearer token authentication and sandbox isolation.1-
- AlicenseAqualityCmaintenanceEnables Codex to delegate coding tasks to an OpenCode CLI locally, returning structured results such as exit codes, session summaries, tool calls, and git diffs.2MIT
- FlicenseBqualityDmaintenanceConnects AI assistants to a local Codex engine for performing deep, project-level code reviews and automated refactoring. It enables context-aware bug fixes and multi-file analysis through a standardized bridge between modern AI clients and local development environments.42-
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: running a coding task, generating images, polling async job results, and reading workspace artifacts. There is no overlap or ambiguity between them.
All tools share the codex_ prefix, but the pattern is inconsistent: codex_run and codex_generate_image are verb-led, codex_read_artifact is verb_noun, while codex_job_result is noun_noun. The naming is readable but not uniform.
Four tools is a compact but reasonable set for a local Codex CLI integration. Each tool serves a necessary function in the workflow, though the surface is slightly minimal.
The core lifecycle of submitting a task, polling for results, and reading generated artifacts is covered. Minor gaps exist, such as no way to cancel a running job or list available workspace files, but these are not critical.