AI Token Cost Optimization — MCP Server

A production-ready Model Context Protocol (MCP) server that provides 4 tools to count tokens, estimate API costs, optimize prompts, and compare AI model pricing. Compatible with Cline, Claude Desktop, and any MCP-compatible client.

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Tools Overview

Tool	Description
count_tokens	Count tokens in any text for GPT-4o, GPT-4o-mini, Claude 3.5 Sonnet, Claude 3.5 Haiku, or Gemini 1.5 Flash. Includes cost estimation.
estimate_cost	Estimate the full API call cost: input cost + output cost. Supports USD and INR.
optimize_prompt	Analyze a prompt and get suggestions to reduce token usage. Also recommends the cheapest model for your text.
compare_models	Compare all 5 models side-by-side for your prompt, sorted from cheapest to most expensive.

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Project Structure

my-mcp-server/
├── src/
│   ├── index.ts          # MCP server (4 tools)
│   ├── types.ts          # TypeScript interfaces
│   ├── pricing.ts        # Model pricing data (USD/INR)
│   └── tokenizer.ts      # Token counting (gpt-tokenizer o200k_base)
├── package.json           # Dependencies and scripts
├── tsconfig.json          # TypeScript configuration
├── README.md              # This file
└── DOCUMENTATION.md       # Full process documentation

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Prerequisites

• Node.js v18 or higher
• npm (comes with Node.js)

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Quick Start

# Navigate to the project directory
cd C:\Users\ghosh\Documents\MSS\my-mcp-server

# Install dependencies
npm install

# Run in development mode
npm run dev

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Usage

Development Mode (hot-reload with tsx)

npm run dev

Development Mode (with auto-reload)

npm run dev:watch

Production Mode

npm run build    # Compile TypeScript to dist/
npm start        # Run compiled version

TypeScript Type-Check

npx tsc --noEmit

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Testing the Server

Option 1: MCP Inspector (Recommended)

npx @modelcontextprotocol/inspector npx tsx src/index.ts

Opens a web UI at http://localhost:5173 — browse tools, call them, see responses.

Option 2: Quick Command-Line Test

Test all 4 tools in one command:

cd C:\Users\ghosh\Documents\MSS\my-mcp-server

echo '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2024-11-05","capabilities":{},"clientInfo":{"name":"test","version":"1.0.0"}}}
{"jsonrpc":"2.0","method":"notifications/initialized"}
{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"count_tokens","arguments":{"text":"Hello World!","model":"gpt-4o"}}}
{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"compare_models","arguments":{"prompt":"Hello","estimated_output_tokens":100}}}' | npx tsx src/index.ts 2>/dev/null

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Cline Configuration

Add to your Cline MCP settings:

{
  "mcpServers": {
    "hello-world": {
      "command": "npx",
      "args": [
        "tsx",
        "C:\\Users\\ghosh\\Documents\\MSS\\my-mcp-server\\src\\index.ts"
      ]
    }
  }
}

macOS/Linux: Use forward slashes: "/Users/ghosh/Documents/MSS/my-mcp-server/src/index.ts"

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Tool Details

count_tokens

Parameters:

Field	Type	Required	Description
text	string	✅	The text to count tokens for
model	string	✅	gpt-4o, gpt-4o-mini, claude-3.5-sonnet, claude-3.5-haiku, gemini-1.5-flash
currency	string	❌	USD (default) or INR

Example response:

{
  "model": "gpt-4o",
  "token_count": 7,
  "character_count": 31,
  "tokenizer": "OpenAI o200k_base (via gpt-tokenizer) — exact",
  "estimated_input_cost": "$0.000018",
  "currency": "USD"
}

estimate_cost

Parameters:

Field	Type	Required	Description
prompt	string	✅	The input prompt text
estimated_output_tokens	number	❌	Default: 500
model	string	✅	Model identifier
currency	string	❌	USD (default) or INR

Example response:

{
  "model": "gpt-4o",
  "provider": "OpenAI",
  "input_tokens": 6,
  "output_tokens": 200,
  "input_cost": "$0.000015",
  "output_cost": "$0.002000",
  "total_cost": "$0.002015",
  "currency": "USD"
}

optimize_prompt

Parameters:

Field	Type	Required	Description
prompt	string	✅	The prompt to analyze
model	string	✅	Model identifier

Example response:

{
  "original_token_count": 15,
  "character_count": 62,
  "suggested_improvements": [
    { "suggestion": "Replace verbose phrases...", "estimated_savings_percent": 5 },
    { "suggestion": "Consider using GPT-4o-mini...", "estimated_savings_percent": 60 }
  ],
  "best_model_recommendation": "Switch to gpt-4o-mini for ~60% cost savings."
}

compare_models

Parameters:

Field	Type	Required	Description
prompt	string	✅	The input prompt text
estimated_output_tokens	number	❌	Default: 500
currency	string	❌	USD (default) or INR

Example response:

{
  "prompt_character_count": 11,
  "estimated_output_tokens": 100,
  "currency": "USD",
  "comparisons": [
    { "model": "gpt-4o-mini", "total_cost": "$0.000060" },
    { "model": "claude-3.5-haiku", "total_cost": "$0.000503" }
  ],
  "summary": {
    "cheapest": "gpt-4o-mini",
    "most_expensive": "claude-3.5-sonnet"
  }
}

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Supported Models & Pricing

Model	Provider	Input (per 1K tokens)	Output (per 1K tokens)
GPT-4o	OpenAI	$0.00250	$0.01000
GPT-4o-mini	OpenAI	$0.00015	$0.00060
Claude 3.5 Sonnet	Anthropic	$0.00300	$0.01500
Claude 3.5 Haiku	Anthropic	$0.00100	$0.00500
Gemini 1.5 Flash	Google	$0.00150	$0.00900

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Technical Details

Field	Value
SDK	@modelcontextprotocol/sdk ^1.29.0
Token Counting	gpt-tokenizer (o200k_base) for OpenAI; character-based for Claude/Gemini
Transport	StdioServerTransport
Module System	ES Modules ("type": "module")
Runtime	Node.js >= 18
Dev Runner	tsx for TypeScript execution

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Example Cline Prompts

After connecting the server, try asking Cline:

"Count the tokens in 'Hello World from my MCP Server!' using GPT-4o"

"Estimate the cost of sending a 100-token prompt with 500 output tokens on Claude 3.5 Sonnet in INR"

"Which is the cheapest model for this prompt: 'Write a poem about artificial intelligence' with 200 output tokens?"

"Analyze this prompt for optimization: 'I would like you to please write a detailed report'"

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License

MIT