MCPサーバー
vetted-consumer
io.github.TheBaronofAI/vetted-consumer
このMCPでできること
Assesses local LLM hardware compatibility, quantization choices, performance, and the cost of buying, renting, or using APIs.
機能
ツール
9
ツール
can_i_run_it
Can I run it?
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.
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'model': {'type': 'string', 'description': "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."}, 'mxfp4': {'type': 'boolean', 'description': 'True if the model ships natively in MXFP4 (e.g. gpt-oss)'}, 'context': {'type': 'number', 'description': 'Context window in tokens (default 8192)'}, 'total_b': {'type': 'number', 'description': 'For an unlisted model: total parameters in billions'}, 'unified': {'type': 'boolean', 'description': 'True for unified-memory machines (Macs, Strix Halo, CPU+RAM)'}, 'vram_gb': {'type': 'number', 'description': 'For custom hardware: VRAM or unified memory in GB'}, 'active_b': {'type': 'number', 'description': 'For an unlisted model: active params in billions (= total for dense, less for MoE)'}, 'hardware': {'type': 'string', 'description': "Hardware name/id, e.g. 'rtx-3090', 'Mac 128GB', 'Strix Halo'. Use list_hardware to see known ones."}, 'kv_precision': {'enum': ['f16', 'q8', 'q4'], 'type': 'string', 'description': 'KV cache precision (default f16)'}, 'bandwidth_gbps': {'type': 'number', 'description': 'For custom hardware: memory bandwidth in GB/s'}}, 'additionalProperties': False}
cheapest_hardware_for_model
Cheapest hardware for a model
The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'model': {'type': 'string', 'description': "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."}, '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)'}, '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)'}}, 'additionalProperties': False}
compare_hardware
Compare hardware
Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'required': ['hardware'], 'properties': {'model': {'type': 'string', 'description': "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."}, 'mxfp4': {'type': 'boolean', 'description': 'True if the model ships natively in MXFP4 (e.g. gpt-oss)'}, 'context': {'type': 'number', 'description': 'Context window in tokens (default 8192)'}, 'total_b': {'type': 'number', 'description': 'For an unlisted model: total parameters in billions'}, 'active_b': {'type': 'number', 'description': 'For an unlisted model: active params in billions (= total for dense, less for MoE)'}, 'hardware': {'type': 'string', 'description': '2 to 4 hardware names/ids, comma-separated'}, 'kv_precision': {'enum': ['f16', 'q8', 'q4'], 'type': 'string', 'description': 'KV cache precision (default f16)'}}, 'additionalProperties': False}
cost_compare
Cost: buy vs rent vs API
Buy vs rent vs API cost to run a model locally: monthly/1y/3y totals, break-even months, and the energy cost per 1M tokens. Same math as /cost-calculator/.
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'api': {'type': 'number', 'description': 'API $/million tokens (default 1.0)'}, 'kwh': {'type': 'number', 'description': 'Electricity $/kWh (default 0.16)'}, 'rent': {'type': 'number', 'description': 'Cloud GPU $/hour (default 0.59)'}, 'hours': {'type': 'number', 'description': 'Active hours per day (default 3)'}, 'tdp_w': {'type': 'number', 'description': 'For custom hardware: board power draw in watts'}, 'tokens': {'type': 'number', 'description': 'Tokens generated per day, for the API comparison (default 300000)'}, 'hardware': {'type': 'string', 'description': "Catalogued hardware name/id (see list_hardware), e.g. 'rtx-3090-used'"}, 'price_usd': {'type': 'number', 'description': 'For custom hardware: price in USD'}}, 'additionalProperties': False}
get_used_gpu_prices
Used GPU prices
Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'gpu': {'type': 'string', 'description': 'Optional name/id filter, e.g. "3090"'}}, 'additionalProperties': False}
list_hardware
List hardware
List the machines the tools know about (memory, bandwidth, price, buy link).
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {}}
list_models
List models
List the local LLM model classes the tools know about (params, dense/MoE, native context).
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {}}
recommend_hardware
Recommend hardware
Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'model': {'type': 'string', 'description': "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."}, 'mxfp4': {'type': 'boolean', 'description': 'True if the model ships natively in MXFP4 (e.g. gpt-oss)'}, 'budget': {'type': 'number', 'description': 'Optional max price in USD'}, 'context': {'type': 'number', 'description': 'Context window in tokens (default 8192)'}, 'total_b': {'type': 'number', 'description': 'For an unlisted model: total parameters in billions'}, 'active_b': {'type': 'number', 'description': 'For an unlisted model: active params in billions (= total for dense, less for MoE)'}, 'kv_precision': {'enum': ['f16', 'q8', 'q4'], 'type': 'string', 'description': 'KV cache precision (default f16)'}}, 'additionalProperties': False}
recommend_quant
Recommend a quant
Which GGUF quantization to download for a model on given hardware: the full quant ladder with file size, max context, and tok/s for each, plus the recommended pick.
入力スキーマ
{'type': 'object', '$schema': 'http://json-schema.org/draft-07/schema#', 'properties': {'model': {'type': 'string', 'description': "Model name, e.g. 'Llama 70B', 'gpt-oss-120B', 'Qwen 32B'. Use list_models to see known names."}, '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)'}, 'total_b': {'type': 'number', 'description': 'For an unlisted model: total parameters in billions'}, 'unified': {'type': 'boolean', 'description': 'True for unified-memory machines (Macs, Strix Halo, CPU+RAM)'}, 'vram_gb': {'type': 'number', 'description': 'For custom hardware: VRAM or unified memory in GB'}, 'active_b': {'type': 'number', 'description': 'For an unlisted model: active params in billions (= total for dense, less for MoE)'}, 'hardware': {'type': 'string', 'description': "Hardware name/id, e.g. 'rtx-3090', 'Mac 128GB', 'Strix Halo'. Use list_hardware to see known ones."}, 'kv_precision': {'enum': ['f16', 'q8', 'q4'], 'type': 'string', 'description': 'KV cache precision (default f16)'}, 'bandwidth_gbps': {'type': 'number', 'description': 'For custom hardware: memory bandwidth in GB/s'}}, 'additionalProperties': False}
履歴
最近のツール変更
追加
compare_hardware
2026年9月17日12:52
追加
get_used_gpu_prices
2026年9月17日12:52
追加
recommend_hardware
2026年9月17日12:52
追加
cost_compare
2026年9月17日12:52
追加
list_hardware
2026年9月17日12:52
追加
list_models
2026年9月17日12:52
追加
cheapest_hardware_for_model
2026年9月17日12:52
追加
recommend_quant
2026年9月17日12:52
追加
can_i_run_it
2026年9月17日12:52
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