MCP Token Optimizer runs on your own machine — the client starts it, so there is no endpoint to ping. 25 installs a week from npm.
Cut LLM token costs: count tokens, estimate cost, slim prompts, and pick the cheapest capable model.
The linked repository no longer exists on GitHub — it was deleted or made private.
Quiet is not dead — but it is worth knowing when it wakes up, or when someone else takes it over. We watch the repository and tell you either way.
This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.
claude mcp add mcp-token-optimizer -- npx -y mcp-token-optimizer
{
"mcpServers": {
"mcp-token-optimizer": {
"args": [
"-y",
"mcp-token-optimizer"
],
"command": "npx"
}
}
}
[mcp_servers.mcp-token-optimizer]
command = "npx"
args = ["-y", "mcp-token-optimizer"]
{
"mcpServers": {
"mcp-token-optimizer": {
"args": [
"-y",
"mcp-token-optimizer"
],
"command": "npx"
}
}
}
{
"mcpServers": {
"mcp-token-optimizer": {
"args": [
"-y",
"mcp-token-optimizer"
],
"command": "npx"
}
}
}
Offline MCP: predict LLM call cost and pick the cheapest capable model. No keys, no network.
Sourced AI-model pricing and capability data — compare and route to the cheapest capable model.
Measure a program's CPU energy and LLM tokens, then refactor it cheaper. Measure, never estimate.
Local LLM cost proxy: forecasts each call and recommends the cheapest model that clears the task.
Analyze LLM API costs: token waste detection, caching savings estimates, model comparison
MCP server and proxy that compresses LLM prompts, tool output, and replies to cut token cost.
Token counting & multi-model LLM cost estimates: GPT-4o, Claude, Gemini, 25+. No API key.
Token-budget-aware text handling: count, truncate, and chunk for LLM prompts.
Answers built from our own checks of this server.