mcp-token-optimizer

An MCP server that cuts your LLM token costs. Give your AI assistant (Claude, Cursor, or any MCP client) the ability to measure, price, and shrink prompts before they cost you money.

Teams routinely overspend 60–80% on LLM tokens through bloated prompts and using a pricier model than the task needs. This server adds four tools that make those savings one call away — no LLM call required for the optimization itself, so it's free and instant.

Tools

| Tool | What it does | |---|---| | count_tokens | Token count for any text + input cost across common models (or one model). | | estimate_cost | Per-call and monthly/yearly spend for a prompt, model, output size, and call volume. | | slim_prompt | Safely compresses a prompt (shortens verbose phrases, drops filler, dedupes lines, normalizes whitespace) and reports tokens and dollars saved. | | compare_model_costs | Costs the same prompt across many models and recommends the cheapest one. |

Token counts use gpt-tokenizer (exact for OpenAI; a close estimate for Claude/Gemini). Prices are an editable mid-2026 snapshot in lib.js — verify against each provider's pricing page.

Install

Add to your MCP client config:

{
  "mcpServers": {
    "token-optimizer": {
      "command": "npx",
      "args": ["-y", "mcp-token-optimizer"]
    }
  }
}

• Claude Desktop: add the block above to claude_desktop_config.json.
• Cursor: add it to .cursor/mcp.json.
• Any MCP host: run the binary mcp-token-optimizer (stdio transport).

Then ask your assistant things like:

• "Count the tokens in this prompt and tell me the cost on gpt-4o vs gpt-4o-mini."
• "Slim this system prompt and show me what I'd save at 50,000 calls a month."
• "Which model is cheapest for this prompt with ~400 tokens of output?"

Run locally

npm install
npm test
npx mcp-token-optimizer   # starts the stdio server

License

MIT