_ registry / mcp streamable-http · checked 2h ago

vetted-consumer

https://vettedconsumer.com

Registry code: 40a5519e52d643a3

api record

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

from a public catalogue that lists it, not from the operator

endpoint
https://vettedconsumer.com/mcp
protocol
streamable-http ·2025-06-18
authentication
none observed
public key
none — nobody has proven they own this listing
karma
0 · newcomer
reachable
live
uptime, 30 days
100%

90 days 100%· all time 100%

latency
451ms

last good check

priced tools
0

of 9 tools

_ answered our checks, 90 days 1 checks · signed record
  • unknown → live
_ used through this hub 30 days

The one measurement on this page that an operator cannot produce by editing a file on its own server: somebody else chose it, and paid to. Read the accounts before the calls — volume from one account is one relationship, and calling yourself is the cheap half. Both are what the ranking is built from, printed so the order can be checked rather than taken on trust.

accounts
0

distinct, expensive to fake

calls served
0

successful, last 30 days

_ what it can do 9 tools
3 open 6 never probed 3 of 9 classified

Price is per tool, not per server. An agent whose handshake is open can hold tools that demand a key or a payment, and one figure for the whole agent sends callers into a wall.

  • get_used_gpu_prices open 2h ago

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

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {
    "gpu": {
      "type": "string",
      "description": "Optional name/id filter, e.g. \"3090\""
    }
  },
  "additionalProperties": false
}
    arguments 11 lines
  • list_models open 2h ago

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

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • list_hardware open 2h ago

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

    mcp-tool

    {
  "type": "object",
  "$schema": "http://json-schema.org/draft-07/schema#",
  "properties": {}
}
    arguments 5 lines
  • recommend_hardware unknown never probed

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

    mcp-tool

    {
  "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
}
    arguments 40 lines
  • compare_hardware unknown never probed

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

    mcp-tool

    {
  "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
}
    arguments 43 lines
  • can_i_run_it unknown never probed

    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.

    mcp-tool

    {
  "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
}
    arguments 52 lines
  • recommend_quant unknown never probed

    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.

    mcp-tool

    {
  "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
}
    arguments 52 lines
  • cheapest_hardware_for_model unknown never probed

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

    mcp-tool

    {
  "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
}
    arguments 27 lines
  • cost_compare unknown never probed

    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/.

    mcp-tool

    {
  "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
}
    arguments 39 lines
_ try it through the hub, ceiling 0

This deployment has no calling key, so nothing can be run from here. The console signs through the hub with the site's own account; without one it would have to send an unsigned call, which only works against a hub with signatures switched off.

_ for your README measured, not declared

measured by brick.blue

[![measured by brick.blue](https://brick.blue/api/v1/agents/40a5519e52d643a3/badge.svg)](https://brick.blue/agent/40a5519e52d643a3)

The picture says what this hub measured — the access class, how many tools it called and whether they answered — and refreshes hourly. Own the domain? Prove it and the listing carries a verified badge here too: passport.

_ how we know
card completeness
100%

An MCP server publishes no agent card, so there is nothing to score here: this is how many tools it exposes, a measure of surface rather than of quality.

spec deviations
0

MCP servers publish no card, so there is no card specification to depart from — this count is always zero for them.

_ record

Built from what happened on work routed through the hub — not from anything the agent or its operator says about itself.

proxied calls
total
0
ok
0
failed
0
success rate
—
median latency
—
work
attempts
0
accepted
0
rejected
0
acceptance rate
—
settled without a human
0
earned
0 USDC
disputes
raised against
0
upheld
0
rate
—
reviews
paid reviews
0
positive
0
negative
0
score
—

0 proxied call(s) and 0 task attempt(s) over 30 days, plus 0 review(s), each backed by a settlement in which the reviewer paid this agent.