Chain of Draft Prompt Tool MCP server

The MCP Chain of Draft (CoD) Prompt Tool transforms standard prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning while reducing token usage. This powerful tool works by transforming your prompt, processing it through your chosen LLM, and delivering a clear, concise response with improved reasoning quality.

Installation Options

Python Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Configure API keys in .env file:

   ANTHROPIC_API_KEY=your_api_key_here
   

4. Run the server:

   python server.py
   

JavaScript/TypeScript Installation

1. Clone the repository
2. Install dependencies:

   npm install
   

3. Configure API keys in .env file:

   ANTHROPIC_API_KEY=your_api_key_here
   

4. Build and run the server:

   # Build TypeScript files using Nx
   npm run nx build
   
   # Start the server
   npm start
   

For development with auto-reload:

npm run dev

Single Executable Applications (SEA)

Build standalone executables that don't require Node.js on the target system:

# Build for all platforms
npm run build:sea

# Or build for specific platforms
npm run build:macos   # macOS
npm run build:linux   # Linux
npm run build:windows # Windows

Configuring Your LLM

The tool supports various LLM providers:

Cloud Services

# For Anthropic Claude
export ANTHROPIC_API_KEY=your_key_here

# For OpenAI
export OPENAI_API_KEY=your_key_here

# For Mistral AI
export MISTRAL_API_KEY=your_key_here

Local Models with Ollama

# First install Ollama
curl https://ollama.ai/install.sh | sh

# Pull your preferred model
ollama pull llama2
# or
ollama pull mistral

# Configure the tool to use Ollama
export MCP_LLM_PROVIDER=ollama
export MCP_OLLAMA_MODEL=llama2  # or your chosen model

Custom Local Models

# Point to your local model API
export MCP_LLM_PROVIDER=custom
export MCP_CUSTOM_LLM_ENDPOINT=http://localhost:your_port

Integration with Claude Desktop

1. Install Claude Desktop from claude.ai/download

2. Create or edit the Claude Desktop config file:

   ~/Library/Application Support/Claude/claude_desktop_config.json
   

3. Add the tool configuration (Python version):

   {
       "mcpServers": {
           "chain-of-draft-prompt-tool": {
               "command": "python3",
               "args": ["/absolute/path/to/cod/server.py"],
               "env": {
                   "ANTHROPIC_API_KEY": "your_api_key_here"
               }
           }
       }
   }
   

   Or for the JavaScript version:

   {
       "mcpServers": {
           "chain-of-draft-prompt-tool": {
               "command": "node",
               "args": ["/absolute/path/to/cod/index.js"],
               "env": {
                   "ANTHROPIC_API_KEY": "your_api_key_here"
               }
           }
       }
   }
   

4. Alternatively, use the Claude CLI:

   # For Python implementation
   claude mcp add chain-of-draft-prompt-tool -e ANTHROPIC_API_KEY="your_api_key_here" "python3 /absolute/path/to/cod/server.py"
   
   # For JavaScript implementation
   claude mcp add chain-of-draft-prompt-tool -e ANTHROPIC_API_KEY="your_api_key_here" "node /absolute/path/to/cod/index.js"
   

Integration with Dive GUI

1. Download and install Dive from their releases page

2. Configure the Chain of Draft tool in Dive's MCP settings:

   {
     "mcpServers": {
       "chain-of-draft-prompt-tool": {
         "command": "/path/to/mcp-chain-of-draft-prompt-tool",
         "enabled": true,
         "env": {
           "ANTHROPIC_API_KEY": "your_api_key_here"
         }
       }
     }
   }
   

   If using the non-SEA version:

   {
     "mcpServers": {
       "chain-of-draft-prompt-tool": {
         "command": "node",
         "args": ["/path/to/dist/index.js"],
         "enabled": true,
         "env": {
           "ANTHROPIC_API_KEY": "your_api_key_here"
         }
       }
     }
   }
   

Testing with MCP Inspector

Start the MCP Inspector for testing and debugging:

# Start the MCP Inspector with the tool
npm run test-inspector

# Or run it manually
npx @modelcontextprotocol/inspector -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY -- node dist/index.js

The Inspector will be available at http://localhost:5173 by default.

Available Tools

Programmatic Usage

Python Client

from client import ChainOfDraftClient

# Create client with specific LLM provider
cod_client = ChainOfDraftClient(
    llm_provider="ollama",  # or "anthropic", "openai", "mistral", "custom"
    model_name="llama2"     # specify your model
)

# Use directly
result = await cod_client.solve_with_reasoning(
    problem="Solve: 247 + 394 = ?",
    domain="math"
)

print(f"Answer: {result['final_answer']}")
print(f"Reasoning: {result['reasoning_steps']}")
print(f"Tokens used: {result['token_count']}")

JavaScript/TypeScript Client

import { ChainOfDraftClient } from './lib/chain-of-draft-client';

// Create client with your preferred LLM
const client = new ChainOfDraftClient({
  provider: 'ollama',           // or 'anthropic', 'openai', 'mistral', 'custom'
  model: 'llama2',             // your chosen model
  endpoint: 'http://localhost:11434'  // for custom endpoints
});

// Use the client
async function solveMathProblem() {
  const result = await client.solveWithReasoning({
    problem: "Solve: 247 + 394 = ?",
    domain: "math",
    max_words_per_step: 5
  });
  
  console.log(`Answer: ${result.final_answer}`);
  console.log(`Reasoning: ${result.reasoning_steps}`);
  console.log(`Tokens used: ${result.token_count}`);
}

solveMathProblem();

How to add this MCP server to Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > MCP and click "Add new global MCP server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "cursor-rules-mcp": {
            "command": "npx",
            "args": [
                "-y",
                "cursor-rules-mcp"
            ]
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explictly ask the agent to use the tool by mentioning the tool name and describing what the function does.