INDIVIDUAL MCP TOOL

recommend_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.

vettedconsumer.comnone authenticationAvailability not checked

Input schema

{}

Risk classification

Inferred read-only · medium confidence · heuristic, not a guarantee.

A tool name or description suggests retrieving external content.

Parent endpoint

vettedconsumer.com

CONNECT WITH APPROVAL

Client installation

Review this server and its permissions before adding it. Secret placeholders must be set locally.

Codex

~/.codex/config.toml

[mcp_servers.vetted-consumer]
url = "https://vettedconsumer.com/mcp"
enabled = true

Claude Code

.mcp.json

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

Claude Desktop

Settings → Connectors → Add custom connector

Name: vetted-consumer
Remote MCP URL: https://vettedconsumer.com/mcp

Add this remote URL as a custom connector in Claude Desktop. Availability depends on the user plan and workspace policy.

Cursor

.cursor/mcp.json

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

Visual Studio Code

.vscode/mcp.json

Add to Visual Studio Code

{
  "servers": {
    "vetted-consumer": {
      "type": "http",
      "url": "https://vettedconsumer.com/mcp"
    }
  }
}

Generic MCP

Client-specific MCP configuration

{
  "name": "vetted-consumer",
  "transport": "streamable-http",
  "url": "https://vettedconsumer.com/mcp"
}

MCP Inspector

Run the official MCP Inspector locally and enter the indexed Streamable HTTP endpoint.

Related tools

can_i_run_it — Will a given local LLM run on given hardware?
cheapest_hardware_for_model — The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.
list_models — List the local LLM model classes the tools know about (params, dense/MoE, native context).
list_hardware — List the machines the tools know about (memory, bandwidth, price, buy link).
cost_compare — 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.
recommend_hardware — Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.
get_used_gpu_prices — Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
compare_hardware — Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.