webgpt MCP

webgpt MCP

Local MCP server for Codex to send prompts to ChatGPT Web Pro extension and manage repository tasks safely.

Category
Visit Server

README

codex-gptpro: call ChatGPT Pro from Codex

日本語 | English

codex-gptpro is a local MCP server that lets Codex ask ChatGPT Web Pro for planning, review, and long-running reasoning, then receive the result back inside Codex.

It is designed for a local, Codex-led workflow. ChatGPT Web does not directly call Codex, and this MCP does not expose arbitrary shell execution, patch application, git commits, git pushes, file deletion, or a remote Codex runner.

Canonical names:

  • GitHub repo: codex-gptpro
  • package name: codex-gptpro
  • MCP server name: codex_gptpro
  • Chrome profile: codex-gptpro

日本語

これは何か

codex-gptpro は、Codex から ChatGPT Web の Pro 拡張へpromptを送り、回答をCodexへ返すためのローカルMCPサーバーです。

主な用途は、Codexが実装中にChatGPT Proへ重い計画、設計レビュー、調査、長時間の推論を依頼し、その出力をローカルの .ai/pro-outputs に保存して作業に戻すことです。

アーキテクチャ

Codex
  -> MCP server: codex_gptpro
  -> http://127.0.0.1:8788/mcp-codex
  -> run_pro_prompt
  -> Google Chrome profile: codex-gptpro
  -> ChatGPT Web Pro
  <- Pro output saved under .ai/pro-outputs

任意で /mcp もありますが、通常の主経路では使いません。CodexからChatGPT Proを呼び出すだけなら、外部公開やCloudflare Tunnelは不要です。

必要条件

  • Node.js 20+
  • pnpm
  • Google Chrome
  • Codex CLI または Codex IDE

Chromiumや専用Chromeアプリのインストールは不要です。既存のGoogle Chromeアプリに、MCP用profile codex-gptpro を作って使います。

セットアップ

git clone https://github.com/tomato-ga/codex-gptpro.git
cd codex-gptpro
pnpm install
pnpm build
pnpm run codex:configure

開発起動:

pnpm dev

本番寄りのローカル起動:

pnpm start

デフォルトURL:

http://127.0.0.1:8788

Codex設定

pnpm run codex:configure~/.codex/config.toml に次のMCP設定を追加します。

[mcp_servers.codex_gptpro]
url = "http://127.0.0.1:8788/mcp-codex"
startup_timeout_sec = 20
tool_timeout_sec = 1200
enabled = true
default_tools_approval_mode = "prompt"

MCP_CODEX_TOKEN を設定している場合だけ、次の行も追加されます。

bearer_token_env_var = "MCP_CODEX_TOKEN"

古い [mcp_servers.webgpt] がある場合は、codex:configure が新しい codex_gptpro 設定へ置き換えます。

使い方

Codexでこう依頼します。

Use codex_gptpro MCP.
Register the current repository automatically if needed.
Ask ChatGPT Pro to create an implementation plan for the current task.
Save the output and summarize the next steps.

初回だけChrome profileのログインが必要な場合があります。

Use codex_gptpro MCP.
Call prepare_pro_browser with keepBrowserOpen: true.

開いたChrome profile codex-gptpro でChatGPTにログインしてください。その後、run_pro_prompt が使えます。

主なtool

  • register_project: 現在のrepoをMCPへ登録
  • list_projects: 登録済みrepoを一覧
  • repo_tree: repo構造を安全に取得
  • read_file: 許可されたtext fileを読む
  • grep_repo: repo内検索
  • git_diff: bounded diffを取得
  • pro_browser_status: Chrome/Pro連携状態を確認
  • prepare_pro_browser: ChatGPTログイン用にChrome profileを開く
  • run_pro_prompt: ChatGPT Proへpromptを送り、回答を返す
  • list_tasks, read_task, claim_task, write_result, read_result: .ai task handoff
  • run_check: typecheck, lint, test, build のallowlisted scriptだけ実行

Chrome profile

デフォルト:

MCP_PRO_CHROME_AUTOMATION_USER_DATA_DIR=~/.codex-gptpro/chrome-user-data
MCP_PRO_CHROME_PROFILE_DIRECTORY=codex-gptpro

これは既存のGoogle Chromeアプリを使います。Chrome 136+では通常のChrome data directoryに対するremote debuggingが制限されるため、MCP専用のuser data dirを使います。

任意の環境変数

.env は必須ではありません。必要な場合だけ作成します。

cp .env.example .env
chmod 600 .env

よく使う値:

MCP_PORT=8788
MCP_CODEX_TOKEN=<long-random-token>
MCP_PUBLIC_PIN=<short-lived-pin>
MCP_PRO_MODEL_LABEL=Pro 拡張
MCP_PRO_CHROME_PROFILE_DIRECTORY=codex-gptpro

セキュリティ

public repoへ入れてはいけないものは .gitignore で除外しています。

  • .env, .env.*
  • projects.local.json
  • .ai/
  • .codex/
  • node_modules/, dist/, coverage/
  • local launchd plist
  • Chrome profile data
  • local memory files

MCP toolsも意図的に制限しています。

  • arbitrary shell executionなし
  • patch applicationなし
  • file deletionなし
  • git commit/pushなし
  • direct Codex invocationなし

検証

pnpm build
pnpm test
pnpm lint
pnpm run healthcheck

macOSで常駐化する場合:

pnpm run launchd:install
launchctl print "gui/$(id -u)/local.codex-gptpro"

English

What It Does

codex-gptpro is a local MCP server that lets Codex send prompts to ChatGPT Web Pro and receive the answer back in Codex.

The main use case is letting Codex delegate heavyweight planning, design review, research, and long-running reasoning to ChatGPT Pro while keeping implementation and verification local.

Architecture

Codex
  -> MCP server: codex_gptpro
  -> http://127.0.0.1:8788/mcp-codex
  -> run_pro_prompt
  -> Google Chrome profile: codex-gptpro
  -> ChatGPT Web Pro
  <- Pro output saved under .ai/pro-outputs

The optional /mcp endpoint exists for safe planner/reviewer tools, but it is not required for the primary workflow. If all you want is Codex calling ChatGPT Pro, you do not need to expose any endpoint publicly.

Requirements

  • Node.js 20+
  • pnpm
  • Google Chrome
  • Codex CLI or Codex IDE

You do not need Chromium or a separate Chrome app. The server uses your existing Google Chrome app with a dedicated Chrome profile named codex-gptpro.

Setup

git clone https://github.com/tomato-ga/codex-gptpro.git
cd codex-gptpro
pnpm install
pnpm build
pnpm run codex:configure

Development server:

pnpm dev

Production-like local server:

pnpm start

Default URL:

http://127.0.0.1:8788

Codex Config

pnpm run codex:configure writes this MCP server block to ~/.codex/config.toml.

[mcp_servers.codex_gptpro]
url = "http://127.0.0.1:8788/mcp-codex"
startup_timeout_sec = 20
tool_timeout_sec = 1200
enabled = true
default_tools_approval_mode = "prompt"

If MCP_CODEX_TOKEN is set, it also adds:

bearer_token_env_var = "MCP_CODEX_TOKEN"

If an old [mcp_servers.webgpt] block exists, codex:configure replaces it with the new codex_gptpro server name.

Usage

Ask Codex:

Use codex_gptpro MCP.
Register the current repository automatically if needed.
Ask ChatGPT Pro to create an implementation plan for the current task.
Save the output and summarize the next steps.

On first use, the Chrome profile may need a ChatGPT login:

Use codex_gptpro MCP.
Call prepare_pro_browser with keepBrowserOpen: true.

Log in to ChatGPT in the opened codex-gptpro Chrome profile. After that, use run_pro_prompt.

Main Tools

  • register_project: register the current repository
  • list_projects: list registered repositories
  • repo_tree: inspect repository structure safely
  • read_file: read allowed text files
  • grep_repo: search repository text
  • git_diff: return bounded diff output
  • pro_browser_status: check Chrome/Pro automation status
  • prepare_pro_browser: open the Chrome profile for first-run login
  • run_pro_prompt: send a prompt to ChatGPT Pro and return the answer
  • list_tasks, read_task, claim_task, write_result, read_result: .ai task handoff
  • run_check: run only allowlisted package scripts: typecheck, lint, test, build

Chrome Profile

Defaults:

MCP_PRO_CHROME_AUTOMATION_USER_DATA_DIR=~/.codex-gptpro/chrome-user-data
MCP_PRO_CHROME_PROFILE_DIRECTORY=codex-gptpro

This uses your existing Google Chrome app. Chrome 136+ restricts remote debugging on the default Chrome data directory, so this server uses a dedicated user data directory for MCP automation.

Optional Environment Variables

.env is optional. Create one only when needed.

cp .env.example .env
chmod 600 .env

Common values:

MCP_PORT=8788
MCP_CODEX_TOKEN=<long-random-token>
MCP_PUBLIC_PIN=<short-lived-pin>
MCP_PRO_MODEL_LABEL=Pro 拡張
MCP_PRO_CHROME_PROFILE_DIRECTORY=codex-gptpro

Security

Local and sensitive files are ignored by default:

  • .env, .env.*
  • projects.local.json
  • .ai/
  • .codex/
  • node_modules/, dist/, coverage/
  • local launchd plists
  • Chrome profile data
  • local memory files

The MCP surface is intentionally limited:

  • no arbitrary shell execution
  • no patch application
  • no file deletion
  • no git commit/push
  • no direct Codex invocation

Verification

pnpm build
pnpm test
pnpm lint
pnpm run healthcheck

For macOS launchd:

pnpm run launchd:install
launchctl print "gui/$(id -u)/local.codex-gptpro"

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured