Vetted Consumer

A free, hosted MCP server for local-LLM hardware decisions.

CommunityOnlineNo sign-inGlobalFreeRead-only

What it can do

  • 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.
  • 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.
  • Cheapest Hardware For Model: The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.

What data it sees

Do you need an account

No: the server works without sign-in

A free, hosted MCP server for local-LLM hardware decisions. Ask whether a model fits your GPU, Mac, or mini-PC, which GGUF quant to download, the cheapest machine that runs it, and the buy-vs-rent-vs-API cost, from a maintained catalog of models and machines plus a monthly used-GPU price feed. No install, no API key.

Tools: can_i_run_it, recommend_quant, cheapest_hardware_for_model, recommend_hardware, compare_hardware, cost_compare, get_used_gpu_prices, list_models, list_hardware.

Works with Claude, Cursor, Cline, and any MCP client.

How to connect

How to connect Vetted Consumer to Claude

The server address to paste into Claude settings:

https://vetted-consumer--hi-10f9.run.tools

  1. Open Claude (claude.ai or the desktop app).
  2. Go to Settings → Connectors.
  3. Click Add custom connector.
  4. Paste the server address copied below into Remote MCP server URL and click Add.
  5. In a chat, click + → Connectors and switch the new connector on.

Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

Authorization

After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

How to connect Vetted Consumer to ChatGPT

The server address to paste into ChatGPT settings:

https://vetted-consumer--hi-10f9.run.tools

  1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
  2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
  3. Open Settings → Connectors and click Create.
  4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
  5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
  6. In a new chat open + → Apps/Connectors and enable the connector.

OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

Authorization

On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

How to connect Vetted Consumer to Cursor

The server address to paste into Cursor settings:

https://vetted-consumer--hi-10f9.run.tools

Fastest: click Open in Cursor below and confirm the prompt.

Manually:

  1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
  2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
  3. Save the file. The server appears in the MCP list; authorize it there if asked.

Authorization

If the service needs sign-in, an authorize button appears next to the server in the MCP list.

Server tool list (9)

Raw names from tools/list. Only developers need these.

can_i_run_itWill 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.
recommend_quantWhich 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.
cheapest_hardware_for_modelThe cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.
list_modelsList the local LLM model classes the tools know about (params, dense/MoE, native context).
list_hardwareList the machines the tools know about (memory, bandwidth, price, buy link).
cost_compareBuy 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/.
recommend_hardwareRanked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.
get_used_gpu_pricesCurrent typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
compare_hardwareSide-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines.