MCP server

LLM Configurator

io.github.aikuba/llmconfigurator

Checked 26 Oct 2026, 08:29 UTC · Connected to it and listed its tools

Read-only: which local LLMs a GPU or Mac can run - VRAM fit, tokens/sec, model specs.

Website Source

At a glance

Type

MCP server

Publisher

Not verified

Status

Working at last check

Tools

5

Token cost

About 1,339 tokens per request

Sets up in

Claude Code, Claude Desktop, Codex, Gemini CLI, Cursor, VS Code, Antigravity, ChatGPT, OpenAI API, Grok

Last checked

26 Oct 2026, 08:29 UTC

Connect it to your agent

Add to CursorAdd to VS CodeVS Code Insiders

Opens the app, which shows the server and asks you to confirm. For other apps, copy the setup below.

Claude Code

claude mcp add --transport http llmconfigurator https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp

Claude Desktop

Settings → Connectors → Add custom connector, and paste the URL.

Codex (config.toml)

[mcp_servers.llmconfigurator]
url = "https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp"

Gemini CLI

{"mcpServers":{"llmconfigurator":{"httpUrl":"https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp"}}}

Cursor

{"mcpServers":{"llmconfigurator":{"url":"https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp"}}}

VS Code

{"servers":{"llmconfigurator":{"type":"http","url":"https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp"}}}

Antigravity

agy mcp add llmconfigurator https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp

ChatGPT

Settings → Apps & Connectors → Advanced settings: turn on Developer mode, then Create a connector and paste the URL. Which plans can do this varies.

OpenAI API (Responses tools)

{"type":"mcp","server_label":"llmconfigurator","server_url":"https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp","require_approval":"always"}

Grok (xAI API tools)

{"type":"mcp","server_label":"llmconfigurator","server_url":"https://elgadmisijrfowuujcmu.supabase.co/functions/v1/mcp"}

Tools

• check_hardware_fit ~351 tokens
  Use this when the user asks whether one specific model runs on one specific machine, at what memory cost, and roughly how fast. Needs a hardware id and a model id from search_catalog. Returns a fit verdict, the memory arithmetic, and a decode-speed range with a confidence label. Memory is sized from
• compare_hardware ~190 tokens
  Use this when the user compares two to four machines. Needs hardware ids from search_catalog. Returns memory, bandwidth, memory type, release year, status and a dated price where one is recorded, side by side. A price of null means none is on record. It does not check any model against the machines;
• get_model_specs ~168 tokens
  Use this when the user asks about one model's size, architecture, context window or licence. Needs a model id from search_catalog. Returns total and active parameters, weight size per quantisation, context length, licence, release date and sources. It does not say whether the model fits any machine;
• models_for_hardware ~395 tokens
  Use this when the user asks what models a given machine can run. Needs a hardware id from search_catalog. Returns up to 20 catalogue models ranked for that machine, each with a fit verdict and a speed range, optionally filtered to one use such as coding. It covers only models in the catalogue, at on
• search_catalog ~235 tokens
  Use this first when the user names a GPU, Mac, mini PC or language model and you need its id for the other tools. Matches names and common aliases in a fixed catalogue of local-LLM hardware and models. It does not return specs, fit or speed, and it does not search the web.

Browse

• GitHub tools
• GitHub and code hosting
• Calling other AI models

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