mcp server
vetted-consumer
Will a local LLM run on your hardware? GGUF quant, buy-vs-rent-vs-API cost, used-GPU prices.
Description as published by the maintainer. Source
- version 1.0.0
- active
active — Registry entry last updated 2026-06-22.
What this server can do
9 functions, named and described by the server itself. Parameter names are shown because they say more about what a function does than its name usually does.
can_i_run_it(model, mxfp4, context, total_b, unified, vram_gb, active_b, hardware, kv_precision, bandwidth_gbps)- 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.
cheapest_hardware_for_model(model, mxfp4, context, total_b, active_b)- The cheapest catalogued, buyable machine that runs a given model at Q4 with the requested context.
compare_hardware(model, mxfp4, context, total_b, active_b, hardware, kv_precision)- Side-by-side memory, bandwidth, price, and (with a model) fit + tok/s for 2 to 4 machines. Required: hardware.
cost_compare(api, kwh, rent, hours, tdp_w, tokens, hardware, price_usd)- 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/.
get_used_gpu_prices(gpu)- Current typical used-GPU prices for local-AI rigs (eBay Browse API median asking + hand-verified, monthly).
list_hardware- List the machines the tools know about (memory, bandwidth, price, buy link).
list_models- List the local LLM model classes the tools know about (params, dense/MoE, native context).
recommend_hardware(model, mxfp4, budget, context, total_b, active_b, kv_precision)- Ranked list of catalogued, buyable machines that run a model at the requested context, cheapest first, with an optional budget cap.
recommend_quant(model, mxfp4, context, total_b, unified, vram_gb, active_b, hardware, kv_precision, bandwidth_gbps)- 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.
Last successful function declaration observed on . Source: https://vettedconsumer.com/mcp. We list what the server declared; we do not call any of these functions.
Endpoint status observed on . Source: https://vettedconsumer.com/mcp.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| Latest published version | 1.0.0 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-06-22 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| First listed in the MCP Registry | 2026-06-22 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| mcp tools declared | 9 tools | Number of functions the server itself declared when asked to list them. This is what the server offers an agent, not a measure of how well any of them work. | as of probe | vettedconsumer.com | |
| mcp endpoint status | ok | The server listed 9 functions when asked. | as of probe | vettedconsumer.com |
Where to get it
This record as data
Every field on this page, with its source and observation date, is in the catalog JSON. Fetch the whole kind at once instead of parsing this HTML.
GET /api/v1/entries/mcp_server.json