MCP MeasureMCP Measure

索引已观测

vetted-consumer

Will a local LLM run on your hardware? GGUF quant, buy-vs-rent-vs-API cost, used-GPU prices.

我该使用它吗

质量与安全性

A
描述质量
91%
模式完整度
78%
命名质量
87%
投毒风险
100%
权限匹配度
100%
协议合规性
100%

发现（2）

  • LOWTool 'get_used_gpu_prices' description lacks action verb在 get_used_gpu_prices 中
  • LOWTool 'compare_hardware' description lacks action verb在 compare_hardware 中

基于对工具定义和协议合规性的自动分析。

上下文开销

~1,957token 数（工具定义）
~1.9 KB典型响应大小
对注意力有中等影响（占 128k 上下文窗口的 1.53%）

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": {
    "vetted-consumer": {
      "url": "https://vettedconsumer.com/mcp"
    }
  }
}

远程端点

https://vettedconsumer.com/mcpstreamable-http

它能做什么

工具清单

工具（9）

🟢 只读🟡 写入🔴 删除⚪ 未知
⚪can_i_run_it(model, total_b, active_b, mxfp4, hardware, ...)

Will a given local LLM run on given hardware? Returns fit, the best quant that fits, theoretical tok/s, and real owner-measured tok/s where available.

输入模式

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."
    },
    "total_b": {
      "type": "number",
      "description": "For an unlisted model: total parameters in billions"
    },
    "active_b": {
      "type": "number",
      "description": "For an unlisted model: active params in billions (= total for dense, less for MoE)"
    },
    "mxfp4": {
      "type": "boolean",
      "description": "True if the model ships natively in MXFP4 (e.g. gpt-oss)"
    },
    "hardware": {
      "type": "string",
      "description": "Hardware name/id, e.g. 'rtx-3090', 'Mac 128GB', 'Strix Halo'. Use list_hardware to see known ones."
    },
    "vram_gb": {
      "type": "number",
      "description": "For custom hardware: VRAM or unified memory in GB"
    },
    "bandwidth_gbps": {
      "type": "number",
      "description": "For custom hardware: memory bandwidth in GB/s"
    },
    "unified": {
      "type": "boolean",
      "description": "True for unified-memory machines (Macs, Strix Halo, CPU+RAM)"
    },
    "context": {
      "type": "number",
      "description": "Context window in tokens (default 8192)"
    },
    "kv_precision": {
      "type": "string",
      "enum": [
        "f16",
        "q8",
        "q4"
      ],
      "description": "KV cache precision (default f16)"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢recommend_quant(model, total_b, active_b, mxfp4, hardware, ...)

Which GGUF quantization to download for a model on given hardware: the full quant ladder with file size, max context, and tok/s for each, plus the recommended pick.

输入模式

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."
    },
    "total_b": {
      "type": "number",
      "description": "For an unlisted model: total parameters in billions"
    },
    "active_b": {
      "type": "number",
      "description": "For an unlisted model: active params in billions (= total for dense, less for MoE)"
    },
    "mxfp4": {
      "type": "boolean",
      "description": "True if the model ships natively in MXFP4 (e.g. gpt-oss)"
    },
    "hardware": {
      "type": "string",
      "description": "Hardware name/id, e.g. 'rtx-3090', 'Mac 128GB', 'Strix Halo'. Use list_hardware to see known ones."
    },
    "vram_gb": {
      "type": "number",
      "description": "For custom hardware: VRAM or unified memory in GB"
    },
    "bandwidth_gbps": {
      "type": "number",
      "description": "For custom hardware: memory bandwidth in GB/s"
    },
    "unified": {
      "type": "boolean",
      "description": "True for unified-memory machines (Macs, Strix Halo, CPU+RAM)"
    },
    "context": {
      "type": "number",
      "description": "Context window in tokens (default 8192)"
    },
    "kv_precision": {
      "type": "string",
      "enum": [
        "f16",
        "q8",
        "q4"
      ],
      "description": "KV cache precision (default f16)"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
⚪cheapest_hardware_for_model(model, total_b, active_b, mxfp4, context)

The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.

输入模式

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."
    },
    "total_b": {
      "type": "number",
      "description": "For an unlisted model: total parameters in billions"
    },
    "active_b": {
      "type": "number",
      "description": "For an unlisted model: active params in billions (= total for dense, less for MoE)"
    },
    "mxfp4": {
      "type": "boolean",
      "description": "True if the model ships natively in MXFP4 (e.g. gpt-oss)"
    },
    "context": {
      "type": "number",
      "description": "Context window in tokens (default 8192)"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢list_models

List the local LLM model classes the tools know about (params, dense/MoE, native context).

输入模式

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢list_hardware

List the machines the tools know about (memory, bandwidth, price, buy link).

输入模式

{
  "type": "object",
  "properties": {},
  "$schema": "http://json-schema.org/draft-07/schema#"
}
⚪cost_compare(hardware, price_usd, tdp_w, hours, tokens, ...)

Buy vs rent vs API cost to run a model locally: monthly/1y/3y totals, break-even months, and the energy cost per 1M tokens. Same math as /cost-calculator/.

输入模式

{
  "type": "object",
  "properties": {
    "hardware": {
      "type": "string",
      "description": "Catalogued hardware name/id (see list_hardware), e.g. 'rtx-3090-used'"
    },
    "price_usd": {
      "type": "number",
      "description": "For custom hardware: price in USD"
    },
    "tdp_w": {
      "type": "number",
      "description": "For custom hardware: board power draw in watts"
    },
    "hours": {
      "type": "number",
      "description": "Active hours per day (default 3)"
    },
    "tokens": {
      "type": "number",
      "description": "Tokens generated per day, for the API comparison (default 300000)"
    },
    "kwh": {
      "type": "number",
      "description": "Electricity $/kWh (default 0.16)"
    },
    "rent": {
      "type": "number",
      "description": "Cloud GPU $/hour (default 0.59)"
    },
    "api": {
      "type": "number",
      "description": "API $/million tokens (default 1.0)"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢recommend_hardware(model, total_b, active_b, mxfp4, context, ...)

Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.

输入模式

{
  "type": "object",
  "properties": {
    "model": {
      "type": "string",
      "description": "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."
    },
    "total_b": {
      "type": "number",
      "description": "For an unlisted model: total parameters in billions"
    },
    "active_b": {
      "type": "number",
      "description": "For an unlisted model: active params in billions (= total for dense, less for MoE)"
    },
    "mxfp4": {
      "type": "boolean",
      "description": "True if the model ships natively in MXFP4 (e.g. gpt-oss)"
    },
    "context": {
      "type": "number",
      "description": "Context window in tokens (default 8192)"
    },
    "kv_precision": {
      "type": "string",
      "enum": [
        "f16",
        "q8",
        "q4"
      ],
      "description": "KV cache precision (default f16)"
    },
    "budget": {
      "type": "number",
      "description": "Optional max price in USD"
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
🟢get_used_gpu_prices(gpu)

Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).

输入模式

{
  "type": "object",
  "properties": {
    "gpu": {
      "type": "string",
      "description": "Optional name/id filter, e.g. \"3090\""
    }
  },
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}
⚪compare_hardware(hardware, model, total_b, active_b, mxfp4, ...)

Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.

输入模式

{
  "type": "object",
  "properties": {
    "hardware": {
      "type": "string",
      "description": "2 to 4 hardware names/ids, comma-separated"
    },
    "model": {
      "type": "string",
      "description": "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."
    },
    "total_b": {
      "type": "number",
      "description": "For an unlisted model: total parameters in billions"
    },
    "active_b": {
      "type": "number",
      "description": "For an unlisted model: active params in billions (= total for dense, less for MoE)"
    },
    "mxfp4": {
      "type": "boolean",
      "description": "True if the model ships natively in MXFP4 (e.g. gpt-oss)"
    },
    "context": {
      "type": "number",
      "description": "Context window in tokens (default 8192)"
    },
    "kv_precision": {
      "type": "string",
      "enum": [
        "f16",
        "q8",
        "q4"
      ],
      "description": "KV cache precision (default f16)"
    }
  },
  "required": [
    "hardware"
  ],
  "additionalProperties": false,
  "$schema": "http://json-schema.org/draft-07/schema#"
}

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已验证未记录版本9 个工具
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已验证未记录版本9 个工具