Agentic MCP — Local Coding Tools for ChatGPT Developer Mode

Connect compatible ChatGPT Web accounts to local coding tools through MCP Developer Mode.

Agentic MCP connects ChatGPT Web Developer Mode to your local repositories through a secure Model Context Protocol (MCP) server. It supports structured file editing, shell execution, Git worktrees, semantic navigation and checkpoints.

ChatGPT Web → Developer Mode → HTTPS Tunnel → Agentic MCP → Local Repository

🌐 Official MCP Registry

Agentic MCP is published in the Official MCP Registry as: io.github.hugolsramos01-bit/mcp-agentic-server

Install and run locally for Claude Desktop, Cursor, or other local MCP clients:

npx -y mcp-agentic-server@latest stdio

Related MCP server: codex-web-bridge

⚡ Quickstart: ChatGPT Plus Setup in 3 Minutes

Turn ChatGPT Web into your primary local coding agent using your existing Web plan:

1. Initialize and start the server:

   npx -y mcp-agentic-server@latest init
   npx -y mcp-agentic-server@latest serve

2. Expose the local port (7676) via an HTTPS tunnel:

   ngrok http 7676

3. Enable Developer Mode in ChatGPT Web:

   • Go to chatgpt.com > Settings > Apps / Developer Mode (or Plugins / Apps).

   • Enable Developer Mode.

4. Connect the Custom MCP App:

   • Add a new Custom MCP App.

   • Enter your public ngrok HTTPS URL ending with /mcp:

     https://YOUR-SUBDOMAIN.ngrok.app/mcp

5. Complete OAuth Authentication:

   • Complete the local browser OAuth authorization prompt when presented.

6. Start Coding:

   • In a new ChatGPT Web chat, enable your custom MCP app and prompt:

     "Open workspace C:/path/to/project using open_workspace and summarize the architecture."

🙋 Frequently Asked Questions (FAQ)

Does this work with ChatGPT Plus?

Tested by the maintainer with ChatGPT Plus via Developer Mode and a public HTTPS tunnel (ngrok, Cloudflare Tunnel, etc.). Availability may vary by account, region, plan, and rollout.

How does this affect my limits and billing?

When connected directly through ChatGPT Developer Mode, model usage is handled by the user's ChatGPT account rather than by an OpenAI Platform API key. Applicable limits depend on the user's plan and OpenAI policies.

Does it require an OpenAI API key?

No OpenAI model API key is required. Authentication for the MCP server and tunnel is separate from model API billing.

Is this an alternative to Codex or Claude Code?

Yes. It gives ChatGPT Web full local coding capabilities: file inspection, structured line-precise editing, shell execution, Git worktrees, AST navigation, and checkpoints.

🔥 Key Innovations

1. Smart Context Anti-Bloat

Stop melting your LLM context windows and preserve your ChatGPT Web message limits.

• workspace_summary: Instead of a massive unified file dump, returns a compact, high-level map of the workspace (git status, package manager, key scripts, and schemas).

• Hardened File Reads: The read tool forces offset/limit paginations and strict line-range constraints. The model gets exactly what it needs, down to the line, preventing catastrophic token bloat on large files.

2. True Git Worktree Sandboxing

Running experimental code, tests, or destructive LLM edits in your main directory can break your Hot Module Replacement (HMR), trigger infinite loops in Next.js/Vite, or mess up your node_modules.

• open_workspace (mode="worktree"): Automatically spins up an isolated, detached git worktree outside your project's main directory (e.g., ~/.agentic/worktrees/).

• The LLM can break things, install new dependencies via worktree_install_deps (with allowLifecycleScripts safely controlled), or sync uncommitted changes via worktree_sync_changes, all without touching your live development environment.

3. Semantic AST Navigation

The server parses TypeScript and JavaScript dynamically (with an in-memory LRU cache to save CPU) to provide the model with semantic maps of your architecture.

• next_route_map: Maps out Next.js App Router and Pages Router dynamically, showing roles (api, page, layout) and dynamic segments.

• payload_schema_map: Analyzes Payload CMS collections to return a clean map of fields, hooks, and relationships.

• file_dependencies: Maps outward (what a file imports) and inward (who imports the file) dependencies instantly.

4. Safe Editing & Dry-Runs

• edit_dry_run: Simulates replacements to validate exact string matching and uniqueness, returning the surrounding context of the would-be edit without saving to disk.

• changed_files_summary: Fast, accurate summaries of modified and newly staged files using git status --porcelain.

5. Checkpoint System

For long autonomous sessions, the agent can save point-in-time snapshots of the workspace (checkpoint_save), list them (checkpoint_list), and roll back (checkpoint_restore) if it goes down a bad architectural path.

💻 Local & Desktop MCP Clients (Claude Desktop, Cursor, Roo Code)

If you prefer using desktop MCP hosts, configure your client configuration file to use the stdio transport:

{
  "mcpServers": {
    "agentic-mcp": {
      "command": "npx",
      "args": ["-y", "mcp-agentic-server@latest", "stdio"],
      "env": {
        "AGENTIC_ALLOWED_ROOTS": "C:/path/to/projects"
      }
    }
  }
}

🛠 Available Tools Reference

The tool surface depends on the AGENTIC_TOOL_MODE setting.

Note: The assistant mode is recommended for the full agentic coding experience. Set AGENTIC_TOOL_MODE=assistant in your .env or environment.

Core tools (always available)

Assistant tools (mode: assistant)

Verification & Risk (New in 1.4.0)

The risk and verification pipeline enforces strict, deterministic planning.

• task_context now produces a RiskProfile accounting for sensitive configuration, fan-out, test proximity, and analysis confidence.

• suggest_checks returns a VerificationPlan and is advisory only (commands are not executed). Mutating checks and non-existent scripts are excluded. Low confidence can elevate policyLevel without altering the intrinsic riskLevel.

Supported Inputs:

{
  workspaceId: string;
  changedPaths?: string[]; // Actual paths changed
  goal?: string;           // Optional goal for discovery
  taskType?: "auto" | "bug_fix" | "feature" | "refactor" | "security_review" | "migration" | "frontend" | "release";
  focusPaths?: string[];   // Specific files to focus on

  // Legacy compatibility transition (deprecated)
  paths?: string[];
  scope?: "changed" | "workspace";
  level?: "minimal" | "recommended" | "full";
}

Deprecated compatibility aliases are hidden by default and never appear in the model's standard workflow. Existing clients can temporarily opt in with AGENTIC_LEGACY_ALIASES=1; they will be removed in the next major release. New clients must use the canonical names above: edit_dry_run, next_route_map, payload_schema_map, changed_files_summary, and project_bootstrap.

🧠 Mental Model for Agents

This server is designed to act as the "hands and eyes" of a remote AGI. If you are building an autonomous agent or using Claude/ChatGPT for coding, instruct your agent to:

1. Always call semantic_pack, project_bootstrap, or workspace_summary first.

2. Use read_compressed for large files — expand blocks with expand_compressed_block.

3. Use context_budget to estimate token cost before reading multiple files.

4. Use mode="worktree" if the task involves running complex shell commands or destructive tests.

5. Use edit_dry_run before performing multi-line regex or exact string replacements.

Isolation boundaries

A managed worktree is an isolated Git checkout, not an operating-system sandbox. It keeps experiments out of the primary checkout, but it does not restrict network access, subprocesses, credentials, CPU/memory use, or paths that are otherwise allowed to the server. Treat scripts as real local commands and use the typed Git/script tools first. Git tools only operate when the opened workspace itself is the repository root; an ancestor repository is rejected rather than exposing sibling projects.

📝 License & Acknowledgements

Licensed under the MIT License. See LICENSE for more details.

This project uses components heavily inspired by internal security architectures, with thanks to standard best practices in the open source community.

We welcome contributions! Please see CONTRIBUTING.md for details on how to get started.