TokenZero
LLM cost optimization gateway, repo token-waste auditor & model arbitrage MCP server.
사용해야 할까요
품질 및 안전성
발견 사항 (1)
- LOWTool 'workspace_metrics' description lacks action verbworkspace_metrics에서
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"tokenzero": {
"url": "https://tokenzero.sh/mcp"
}
}
}원격 엔드포인트
https://tokenzero.sh/mcpstreamable-http할 수 있는 일
도구 목록
도구 (5)
🟡audit_repo_token_waste(repo, files)
Static-analyze a public GitHub repo (owner/name) or a set of source files for LLM token waste: uncached static prompts, frontier models on extraction tasks, hot loops, non-deterministic repeat calls. Returns findings with fixes and an estimated recoverable percentage.
입력 스키마
{
"type": "object",
"properties": {
"repo": {
"type": "string",
"description": "GitHub owner/name (public)"
},
"files": {
"type": "array",
"items": {
"type": "object",
"properties": {
"path": {
"type": "string"
},
"content": {
"type": "string"
}
},
"required": [
"path",
"content"
]
},
"description": "Alternative to repo: files to analyze"
}
}
}🟢optimize_prompt_payload(body, endpoint, strategy)
Rewrite an OpenAI Chat/Responses or Anthropic Messages request body so its static prefix is provider-cacheable (Anthropic cache_control breakpoints, OpenAI prompt_cache_key). Returns the optimized body, what changed, the token profile and the projected per-request saving. Never changes the tokens the model sees.
입력 스키마
{
"type": "object",
"properties": {
"body": {
"type": "object",
"description": "The request body"
},
"endpoint": {
"type": "string",
"enum": [
"/v1/chat/completions",
"/v1/responses",
"/v1/messages"
]
},
"strategy": {
"type": "string",
"enum": [
"static",
"conversation"
]
}
},
"required": [
"body"
]
}🟢calculate_model_arbitrage(model, input_tokens, output_tokens, requests_per_month, static_tokens, ...)
Price a workload at list prices across models: cost per request and per month for the requested model, its cheaper sibling, and the effect of prompt caching. Pricing table version 2026-10-26.
입력 스키마
{
"type": "object",
"properties": {
"model": {
"type": "string"
},
"input_tokens": {
"type": "number"
},
"output_tokens": {
"type": "number"
},
"requests_per_month": {
"type": "number"
},
"static_tokens": {
"type": "number",
"description": "portion of input_tokens that is a fixed prefix (system + tools)"
},
"compare_to": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"model",
"input_tokens",
"output_tokens"
]
}🟡provision_workspace(name)
Create a TokenZero workspace and key with no form. Returns the key once, the base URLs that route through it (OPENAI_BASE_URL / ANTHROPIC_BASE_URL), the one-click dashboard link and the badge URL. Free up to the monthly free tier; nothing is billed.
입력 스키마
{
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "workspace name (default mcp-agent)"
}
}
}🟢workspace_metrics(key, window, demo)
Live value scorecard for a TokenZero workspace: hard savings and net ROI per window, the four engines' contribution, speed, parity, availability and privacy posture, per-model breakdown and one-click recommendations. Needs the workspace key (tz_live_…); pass demo: true for a labelled sample.
입력 스키마
{
"type": "object",
"properties": {
"key": {
"type": "string",
"description": "workspace key (tz_live_…)"
},
"window": {
"type": "string",
"enum": [
"24h",
"7d",
"30d",
"all"
]
},
"demo": {
"type": "boolean",
"description": "return the sample scorecard instead"
}
}
}커뮤니티
증거