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Cut LLM token costs: count tokens, estimate cost, slim prompts, and pick the cheapest capable model.
Cut LLM token costs: count tokens, estimate cost, slim prompts, and pick the cheapest capable model.
Valid MCP server (4 strong, 4 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.
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Add this to your MCP configuration file:
{
"mcpServers": {
"io-github-rccola990-cloud-mcp-token-optimizer": {
"args": [
"-y",
"mcp-token-optimizer"
],
"command": "npx"
}
}
}From the project's GitHub README.
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.
| 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.
Add to your MCP client config:
{
"mcpServers": {
"token-optimizer": {
"command": "npx",
"args": ["-y", "mcp-token-optimizer"]
}
}
}
claude_desktop_config.json..cursor/mcp.json.mcp-token-optimizer (stdio transport).Then ask your assistant things like:
npm install
npm test
npx mcp-token-optimizer # starts the stdio server
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