MCP Code Editor ("The Typist")

A custom MCP (Model Context Protocol) Server that acts as a "code writing assistant" to save your primary AI tokens and expenses.

With this setup, your primary AI (such as Claude-3.5-Sonnet or Gemini-1.5-Pro) in Antigravity/Cursor acts purely as a Logic Architect that designs program flows and system architectures, while the heavy lifting of writing and editing long files is delegated to a cheaper third-party LLM (such as Gemini-1.5-Flash, Llama-3, or DeepSeek) via MCP Tools.

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🚀 Architecture Overview

The Typist server decouples high-level reasoning from low-level code generation, optimizing cost and speed.

sequenceDiagram
    participant LA as Logic Architect (Primary AI)
    participant MCP as MCP Code Editor (Server)
    participant C as Cheap Coder LLM
    participant FS as File System

    LA->>MCP: Call Tool (e.g., create_file, edit_file)
    Note over MCP: Validates request, formats prompt
    MCP->>C: Send Code Generation Request (Instruction + Context)
    C-->>MCP: Return Generated Code
    alt create_file or edit_file
        MCP->>FS: Write/Overwrite File
        FS-->>MCP: Success Confirmation
    end
    MCP-->>LA: Tool Execution Result (Success/Code Block)
    LA->>LA: Summarize changes, continue logic flow

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✨ Core Features

This MCP Server registers 4 essential tools for file manipulation and testing:

1. write_code: Generates new code based on logical instructions and returns the code block directly in the chat without writing it to disk (useful for quick review).
2. create_file: Generates code based on instructions and writes it directly as a new file at the specified path.
3. edit_file: Reads an existing file, sends its contents and edit instructions to the cheap LLM, then overwrites the file completely with the modified code.
4. check_connection: Tests connectivity to the configured cheap LLM provider, printing API latency and the raw response to help verify settings.

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⚙️ Installation & Setup

Follow these steps to get your Typist server running locally.

Option A: Local Execution (For Development/Testing)

Step 1: Project Setup

Clone or open this folder in your terminal.

git clone https://github.com/yourusername/mcp-code-editor.git
cd mcp-code-editor

Step 2: Install Dependencies

npm install

Step 3: Configure Environment Variables

Copy the example file and fill in your API Key and endpoint details.

cp .env.example .env

Edit the newly created .env file:

Note: This server uses a standard OpenAI-compatible client, so you can easily point it to OpenRouter, DeepSeek, Groq, or even a local Ollama instance by changing the configuration below.

# .env configuration example
CODER_API_URL=https://api.cometapi.com/v1/chat/completions
CODER_API_KEY=your_api_key_here
CODER_MODEL=gpt-4o-mini

Option B: Global Installation (Recommended for Daily Use)

To run the server from any directory as a global command:

Step 1: Install Globally

Navigate to the project directory (if not already there) and run:

npm install -g .
# OR use npm link if you plan on making local edits
# npm link

Step 2: Configure Environment Variables

Ensure your .env file is configured as described in Option A, Step 3. If running globally, the server will look for a configuration file named .mcp-code-editor.env in your user's home directory (~/.mcp-code-editor.env). Copy the contents of your local .env file into this global configuration file for persistent access.

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🔌 Registering to Antigravity / Cursor

You must inform your primary AI environment (Antigravity/Cursor) where to find this server. Add the following configuration to your global MCP configuration file (e.g., mcp_config.json):

⚠️ Crucial: Choose the configuration method that matches your installation option above.

If using Local Execution (Option A): Adjust the absolute path to index.js based on where the repository is cloned on your system.

{
  "mcpServers": {
    "mcp-code-editor": {
      "command": "node",
      "args": ["/path/to/your/MCP-CodeEditor/index.js"]
    }
  }
}

If using Global Installation (Option B): Use the package name directly as the command.

{
  "mcpServers": {
    "mcp-code-editor": {
      "command": "mcp-code-editor"
    }
  }
}

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🧠 Agent Rules (.clauderules / .cursorrules / gemini.md)

To guide your primary AI to delegate coding tasks effectively, copy the following rules to a .clauderules or .cursorrules file in the root of the project you are working on:

These rules enforce the separation of concerns between the Architect and the Coder.

# Rule: Logic Architect & Code Writing Delegation

You act as the **Logic Architect**. You have access to the MCP Code Editor tools (`write_code`, `create_file`, `edit_file`).

## Core Rules:

1. **DO NOT Write Long Code Blocks Directly**: Whenever the user asks to create a new file (e.g. React component, Laravel Controller, Python script) or rewrite a long code block (>20 lines), you are **strictly forbidden** from generating it directly in the main chat conversation using your internal resources.
2. **Always Delegate to Tools**:
   - To create a new file, gather detailed logical requirements first, then call the **`create_file`** tool with the full specifications.
   - To modify or add features to an existing file, gather the modification logic, then call the **`edit_file`** tool.
   - To generate temporary code for review in the chat without writing to disk, call the **`write_code`** tool.
3. **Focus on Logic and Structure**: Your primary task in the chat is to think about the architecture, design the program flow, verify the conceptual correctness, and provide extremely specific and detailed instructions for the `instruction` parameter in the tools.
4. **After Tool Execution**: Simply provide a summary of the changes or confirmation that the file was successfully created/updated by the code writing assistant.