MCP MeasureMCP Measure

Index beobachtet

vetted-consumer

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

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
91%
Vollständigkeit des Schemas
78%
Qualität der Benennung
87%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (2)

  • LOWTool 'get_used_gpu_prices' description lacks action verbin get_used_gpu_prices
  • LOWTool 'compare_hardware' description lacks action verbin compare_hardware

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,957Tokens (Tool-Definitionen)
~1.9 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.53% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

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

Remote-Endpunkte

https://vettedconsumer.com/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (9)

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⚪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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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).

Eingabe-Schema

{
  "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).

Eingabe-Schema

{
  "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/.

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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).

Eingabe-Schema

{
  "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.

Eingabe-Schema

{
  "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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