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索引已观测

Model Ruler — AI Cost Calculators

Read-only AI/LLM cost tools with current MCP discovery and attributable Registry transport.

我该使用它吗

质量与安全性

A
描述质量
93%
模式完整度
100%
命名质量
50%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

发现（15）

  • LOWTool 'self-host-breakeven-calculator' description lacks action verb在 self-host-breakeven-calculator 中
  • LOWTool 'fine-tune-roi-calculator' description lacks action verb在 fine-tune-roi-calculator 中
  • LOWTool 'agent-loop-cost-calculator' description lacks action verb在 agent-loop-cost-calculator 中
  • LOWTool 'token-counter' doesn't follow camelCase/snake_case在 token-counter 中
  • LOWTool 'provider-cost-calculator' doesn't follow camelCase/snake_case在 provider-cost-calculator 中
  • LOWTool 'self-host-breakeven-calculator' doesn't follow camelCase/snake_case在 self-host-breakeven-calculator 中
  • LOWTool 'context-window-planner' doesn't follow camelCase/snake_case在 context-window-planner 中
  • LOWTool 'quantization-calculator' doesn't follow camelCase/snake_case在 quantization-calculator 中
  • LOWTool 'fine-tune-roi-calculator' doesn't follow camelCase/snake_case在 fine-tune-roi-calculator 中
  • LOWTool 'eval-cost-calculator' doesn't follow camelCase/snake_case在 eval-cost-calculator 中

基于对工具定义和协议合规性的自动分析。

上下文开销

~3,333token 数（工具定义）
~2.2 KB典型响应大小
对注意力有显著影响（占 128k 上下文窗口的 2.60%）

这是每次将服务器的工具加载到模型上下文窗口时所消耗的大致 token 数。数值越高，可用于其他任务的注意力就越少。

安装

一键安装

将以下内容添加到你的 `claude_desktop_config.json` 文件中：

{
  "mcpServers": {
    "model-ruler": {
      "url": "https://modelruler.dev/mcp"
    }
  }
}

远程端点

https://modelruler.dev/mcpstreamable-http
https://modelruler.dev/mcp?dist=model_everywhere_registry_native_official_registrystreamable-http

它能做什么

工具清单

工具（12）

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🟢token-counter(text, tokenizer, expected_out_tokens)

Use when a user asks how many tokens a given text will consume, or needs to estimate prompt size before pricing a workload. Given text and tokenizer family, returns low/high token range and byte-level measurements.

输入模式

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "Text content to count tokens for"
    },
    "tokenizer": {
      "type": "string",
      "enum": [
        "cl100k_base",
        "o200k_base",
        "claude",
        "gemini",
        "llama3",
        "mistral",
        "default"
      ],
      "description": "Tokenizer family (default: default)"
    },
    "expected_out_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Expected output token budget (optional)"
    }
  },
  "required": [
    "text"
  ],
  "examples": [
    {
      "text": "Summarize the Q3 board deck into five concise bullet points for the exec team."
    }
  ]
}
🟢provider-cost-calculator(tokens_in, tokens_out, calls_per_month, provider, model, ...)

Use when a user asks what an LLM workload costs on a specific provider/model, or wants to compare cost across providers. Given tokens per call and call volume, returns monthly cost plus a tier comparison table.

输入模式

{
  "type": "object",
  "properties": {
    "tokens_in": {
      "type": "number",
      "minimum": 0,
      "description": "Input tokens per call"
    },
    "tokens_out": {
      "type": "number",
      "minimum": 0,
      "description": "Output tokens per call"
    },
    "calls_per_month": {
      "type": "number",
      "minimum": 1,
      "description": "Monthly call volume"
    },
    "provider": {
      "type": "string",
      "description": "Target provider (e.g. anthropic, openai, together)"
    },
    "model": {
      "type": "string",
      "description": "Target model (e.g. claude-sonnet-4-6)"
    },
    "include_comparison": {
      "type": "boolean",
      "description": "Include tier comparison table (default true)"
    }
  },
  "required": [
    "tokens_in",
    "tokens_out"
  ],
  "examples": [
    {
      "tokens_in": 1200,
      "tokens_out": 400,
      "calls_per_month": 100000
    }
  ]
}
🟢self-host-breakeven-calculator(monthly_tokens, api_cost_per_1m_out, gpu_provider, gpu_type, utilization_pct, ...)

Use when a user is deciding between API usage and self-hosted GPU inference at a given volume. Returns breakeven token volume, monthly cost comparison, and go/no-go recommendation.

输入模式

{
  "type": "object",
  "properties": {
    "monthly_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Monthly output token volume"
    },
    "api_cost_per_1m_out": {
      "type": "number",
      "minimum": 0,
      "description": "Current API output cost per 1M tokens"
    },
    "gpu_provider": {
      "type": "string",
      "description": "GPU provider (e.g. runpod, modal)"
    },
    "gpu_type": {
      "type": "string",
      "description": "GPU type (e.g. h100, a100-80gb)"
    },
    "utilization_pct": {
      "type": "number",
      "minimum": 1,
      "maximum": 100,
      "description": "Expected GPU utilization % (default 60)"
    },
    "operational_overhead_pct": {
      "type": "number",
      "minimum": 0,
      "description": "Ops overhead % on GPU cost (default 40)"
    }
  },
  "required": [
    "monthly_tokens"
  ],
  "examples": [
    {
      "monthly_tokens": 750000000
    }
  ]
}
🟢context-window-planner(doc_tokens, overhead_tokens, expected_out_tokens, model_context_window, strategy_hint)

Use when a user needs to know whether a document plus prompt plus output fits within a model's context window, or wants a strategy recommendation (truncate/summarize/rag/chunk).

输入模式

{
  "type": "object",
  "properties": {
    "doc_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Primary document/content tokens"
    },
    "overhead_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "System prompt + few-shot + history (default 2000)"
    },
    "expected_out_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Reserved output budget (default 1000)"
    },
    "model_context_window": {
      "type": "number",
      "minimum": 1,
      "description": "Target model context size"
    },
    "strategy_hint": {
      "type": "string",
      "enum": [
        "truncate",
        "summarize",
        "rag",
        "chunk"
      ],
      "description": "Preferred strategy (optional)"
    }
  },
  "required": [
    "doc_tokens",
    "model_context_window"
  ],
  "examples": [
    {
      "doc_tokens": 18000,
      "model_context_window": 200000
    }
  ]
}
🟢quantization-calculator(params_billions, precision_from, precision_to, kv_cache_tokens, batch_size)

Use when a user is planning to quantize an LLM to fit on smaller hardware. Given parameter count and precision transition, returns VRAM requirement, speedup estimate, and approximate quality delta.

输入模式

{
  "type": "object",
  "properties": {
    "params_billions": {
      "type": "number",
      "minimum": 0.1,
      "description": "Model parameter count in billions"
    },
    "precision_from": {
      "type": "string",
      "enum": [
        "fp32",
        "fp16",
        "bf16",
        "fp8",
        "int8",
        "int6",
        "int5",
        "int4",
        "int3",
        "int2",
        "int1"
      ],
      "description": "Starting precision (default bf16)"
    },
    "precision_to": {
      "type": "string",
      "enum": [
        "fp32",
        "fp16",
        "bf16",
        "fp8",
        "int8",
        "int6",
        "int5",
        "int4",
        "int3",
        "int2",
        "int1"
      ],
      "description": "Target precision (default int4)"
    },
    "kv_cache_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Max KV cache tokens (default 8192)"
    },
    "batch_size": {
      "type": "number",
      "minimum": 1,
      "description": "Serving batch size (default 1)"
    }
  },
  "required": [
    "params_billions"
  ],
  "examples": [
    {
      "params_billions": 8
    }
  ]
}
🟢fine-tune-roi-calculator(train_tokens, train_cost_per_1m, base_inference_cost_1m, finetuned_inference_cost_1m, monthly_inference_tokens, ...)

Use when a user is considering fine-tuning vs prompt engineering. Returns training cost, monthly inference savings, months-to-ROI, and breakeven volume.

输入模式

{
  "type": "object",
  "properties": {
    "train_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Training tokens (dataset × epochs)"
    },
    "train_cost_per_1m": {
      "type": "number",
      "minimum": 0,
      "description": "Training cost per 1M tokens"
    },
    "base_inference_cost_1m": {
      "type": "number",
      "minimum": 0,
      "description": "Baseline API output cost per 1M"
    },
    "finetuned_inference_cost_1m": {
      "type": "number",
      "minimum": 0,
      "description": "Fine-tuned inference cost per 1M"
    },
    "monthly_inference_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Expected monthly inference volume (output tokens)"
    },
    "prompt_reduction_pct": {
      "type": "number",
      "minimum": 0,
      "maximum": 100,
      "description": "Prompt size reduction % from eliminating few-shot (default 0)"
    }
  },
  "required": [
    "train_tokens",
    "monthly_inference_tokens"
  ],
  "examples": [
    {
      "train_tokens": 50000000,
      "monthly_inference_tokens": 300000000
    }
  ]
}
🟢eval-cost-calculator(samples, models, trials_per_sample, avg_tokens_in, avg_tokens_out, ...)

Use when a user needs to budget an LLM evaluation run. Given samples/models/trials, returns total cost, per-run cost, and parallel time estimate.

输入模式

{
  "type": "object",
  "properties": {
    "samples": {
      "type": "number",
      "minimum": 1,
      "description": "Number of eval samples"
    },
    "models": {
      "type": "number",
      "minimum": 1,
      "description": "Candidate models (default 1)"
    },
    "trials_per_sample": {
      "type": "number",
      "minimum": 1,
      "description": "Repeats per sample (default 1)"
    },
    "avg_tokens_in": {
      "type": "number",
      "minimum": 0,
      "description": "Avg input tokens per sample (default 2000)"
    },
    "avg_tokens_out": {
      "type": "number",
      "minimum": 0,
      "description": "Avg output tokens per sample (default 500)"
    },
    "provider": {
      "type": "string",
      "description": "Eval provider (default anthropic)"
    },
    "model": {
      "type": "string",
      "description": "Eval model (default claude-sonnet-4-6)"
    },
    "judge_enabled": {
      "type": "boolean",
      "description": "Enable LLM-as-judge second pass (default false)"
    },
    "judge_tokens_in": {
      "type": "number",
      "minimum": 0,
      "description": "Judge input tokens (default 1500)"
    },
    "judge_tokens_out": {
      "type": "number",
      "minimum": 0,
      "description": "Judge output tokens (default 200)"
    }
  },
  "required": [
    "samples"
  ],
  "examples": [
    {
      "samples": 2000
    }
  ]
}
🟢observability-cost-calculator(requests_per_day, avg_log_bytes, retention_days, provider)

Use when a user needs to budget LLM observability tooling. Returns monthly cost at given request volume with retention adjustment.

输入模式

{
  "type": "object",
  "properties": {
    "requests_per_day": {
      "type": "number",
      "minimum": 0,
      "description": "Average daily LLM requests"
    },
    "avg_log_bytes": {
      "type": "number",
      "minimum": 0,
      "description": "Avg payload bytes per traced request (default 4096)"
    },
    "retention_days": {
      "type": "number",
      "minimum": 1,
      "description": "Retention in days (default 30)"
    },
    "provider": {
      "type": "string",
      "enum": [
        "langsmith",
        "langfuse",
        "helicone",
        "portkey",
        "arize",
        "braintrust"
      ],
      "description": "Observability provider"
    }
  },
  "required": [
    "requests_per_day",
    "provider"
  ],
  "examples": [
    {
      "requests_per_day": 50000,
      "provider": "langsmith"
    }
  ]
}
🟢rag-pipeline-cost-calculator(queries_per_day, corpus_tokens, chunk_size_tokens, chunks_retrieved, embedding_provider, ...)

Use when a user needs end-to-end RAG cost estimation (embedding + vector store + generation). Returns monthly cost with breakdown and dominant-component identification.

输入模式

{
  "type": "object",
  "properties": {
    "queries_per_day": {
      "type": "number",
      "minimum": 0,
      "description": "User query volume per day"
    },
    "corpus_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Indexed corpus size in tokens"
    },
    "chunk_size_tokens": {
      "type": "number",
      "minimum": 64,
      "description": "Avg chunk size (default 512)"
    },
    "chunks_retrieved": {
      "type": "number",
      "minimum": 1,
      "description": "Chunks per query (default 5)"
    },
    "embedding_provider": {
      "type": "string",
      "enum": [
        "openai-3-small",
        "openai-3-large",
        "voyage-3",
        "voyage-3-lite",
        "cohere-embed-v3",
        "google-text-embed-004"
      ],
      "description": "Embedding model"
    },
    "vector_store": {
      "type": "string",
      "enum": [
        "pinecone",
        "weaviate",
        "qdrant",
        "chroma",
        "turbopuffer"
      ],
      "description": "Vector store"
    },
    "generator_provider": {
      "type": "string",
      "description": "Generator LLM provider (default anthropic)"
    },
    "generator_model": {
      "type": "string",
      "description": "Generator model (default claude-sonnet-4-6)"
    },
    "question_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Avg question tokens (default 100)"
    },
    "answer_tokens": {
      "type": "number",
      "minimum": 0,
      "description": "Avg answer tokens (default 400)"
    },
    "reindex_fraction_per_month": {
      "type": "number",
      "minimum": 0,
      "maximum": 1,
      "description": "Fraction of corpus re-embedded per month (default 0.1)"
    }
  },
  "required": [
    "queries_per_day",
    "corpus_tokens"
  ],
  "examples": [
    {
      "queries_per_day": 5000,
      "corpus_tokens": 25000000
    }
  ]
}
🟢automation-cost-calculator(workflow_runs_per_month, billable_steps_per_run, make_modules_per_run)

Use for generic or non-agent recurring workflow platform billing across Zapier task billing, Make credit billing, and n8n execution billing. Use agent-workflow-cost-calculator instead when the workflow is explicitly an AI agent with app actions or MCP tool calls. Use agent-loop-cost-calculator instead when the question is about LLM inference/reasoning cost per successful agent task.

输入模式

{
  "type": "object",
  "properties": {
    "workflow_runs_per_month": {
      "type": "number",
      "minimum": 0,
      "description": "Workflow/scenario runs per month"
    },
    "billable_steps_per_run": {
      "type": "number",
      "minimum": 1,
      "description": "Billable actions/modules per run (default 5)"
    },
    "make_modules_per_run": {
      "type": "number",
      "minimum": 1,
      "description": "Make module actions per scenario run; defaults to billable_steps_per_run"
    }
  },
  "required": [
    "workflow_runs_per_month"
  ],
  "examples": [
    {
      "workflow_runs_per_month": 120000
    }
  ]
}
🟢agent-workflow-cost-calculator(agent_runs_per_month, app_actions_per_run, mcp_tool_calls_per_run, make_modules_per_run)

Use for automation-platform/iPaaS cost of an AI agent workflow, including app-action fan-out and MCP tool-call accounting, explicitly excluding LLM token spend. Use automation-cost-calculator for generic non-agent workflow billing. Use agent-loop-cost-calculator for LLM inference/reasoning cost per successful multi-step agent task.

输入模式

{
  "type": "object",
  "properties": {
    "agent_runs_per_month": {
      "type": "number",
      "minimum": 0,
      "description": "Agent workflow runs per month"
    },
    "app_actions_per_run": {
      "type": "number",
      "minimum": 0,
      "description": "Downstream app actions per agent run (default 3)"
    },
    "mcp_tool_calls_per_run": {
      "type": "number",
      "minimum": 0,
      "description": "MCP tool calls per agent run (default 1)"
    },
    "make_modules_per_run": {
      "type": "number",
      "minimum": 1,
      "description": "Make modules per run; defaults to app actions + MCP calls"
    }
  },
  "required": [
    "agent_runs_per_month"
  ],
  "examples": [
    {
      "agent_runs_per_month": 20000
    }
  ]
}
🟢agent-loop-cost-calculator(tasks_per_month, steps_per_task, tool_calls_per_step, avg_tokens_in_per_turn, avg_tokens_out_per_turn, ...)

Use for LLM inference/reasoning cost per successful multi-step agent task, including failure overhead and context growth across turns. Use agent-workflow-cost-calculator for iPaaS/app-action/MCP orchestration fees. Use automation-cost-calculator for generic Zapier/Make/n8n workflow billing.

输入模式

{
  "type": "object",
  "properties": {
    "tasks_per_month": {
      "type": "number",
      "minimum": 0,
      "description": "Tasks attempted per month"
    },
    "steps_per_task": {
      "type": "number",
      "minimum": 1,
      "description": "Avg reasoning steps per task (default 5)"
    },
    "tool_calls_per_step": {
      "type": "number",
      "minimum": 0,
      "description": "Avg tool invocations per step (default 2)"
    },
    "avg_tokens_in_per_turn": {
      "type": "number",
      "minimum": 0,
      "description": "Avg input tokens per turn (default 3000)"
    },
    "avg_tokens_out_per_turn": {
      "type": "number",
      "minimum": 0,
      "description": "Avg output tokens per turn (default 400)"
    },
    "success_rate": {
      "type": "number",
      "minimum": 0.01,
      "maximum": 1,
      "description": "Task success rate 0-1 (default 0.7)"
    },
    "provider": {
      "type": "string",
      "description": "LLM provider (default anthropic)"
    },
    "model": {
      "type": "string",
      "description": "LLM model (default claude-sonnet-4-6)"
    },
    "context_growth_factor": {
      "type": "number",
      "minimum": 1,
      "description": "Multiplier on input tokens as conversation grows (default 1.4)"
    }
  },
  "required": [
    "tasks_per_month"
  ],
  "examples": [
    {
      "tasks_per_month": 60000
    }
  ]
}

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最近观测

已验证未记录版本12 个工具
已验证未记录版本12 个工具