Skip to main content
Glama
Sign In

Nodegrove VRAM: can I run it?

by io.nodegrove
OverviewDiscussionsAdmin

What fits my GPU?

what_fits
Read-onlyIdempotent

Which open-weight LLMs fit this GPU: every model in list_models checked at one quantisation and context, with a recommended everyday model (the biggest class that fits with room for context at conversational speed), the largest that fits, the best at Q8 and the first out of reach. GPU: a name or id from list_gpus, or vram_gb for any other card.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "what_fits"
  }
}

Input Schema

NameRequiredDescriptionDefault
gpuNoA GPU from list_gpus (id or name, e.g. "rtx-4090", "4090" or "M4 Max").
quantNoWeight quantisation: fp16 (FP16 / BF16), q8 (Q8_0), q6 (Q6_K), q5 (Q5_K_M), q4 (Q4_K_M), q3 (Q3_K_M). q4 is the common default.q4
contextNoTokens held in context: prompt plus conversation.
vram_gbNoMemory of a card not in list_gpus, GB. For a Mac, its unified memory with apple_silicon: true.
apple_siliconNovram_gb is Apple unified memory; the GPU can use about 75% of it by default.
bandwidth_gb_sNoMemory bandwidth from the maker's spec, GB/s, for a speed ceiling.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so safety is covered structurally. The description adds genuinely useful behavioral context beyond that: it defines what 'recommended everyday model' means operationally (biggest class that fits with room for context at conversational speed) and frames the output as a comparison set. It does not discuss latency, cost, or model of computation, but the added output semantics are substantive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A compact two-sentence block that front-loads the purpose and the ranked outputs before the input guidance. Dense but every clause carries information; no filler or repetition of the title. It is close to the upper bound for a description of this complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return-value burden and does reasonably well by naming the four model classes reported. It omits that no parameter is required and does not mention defaults (q4, 8192 tokens), though the schema covers those. For a 6-param analytical tool with zero required inputs, the description is largely complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema, including the quant enum, context defaults and the Apple unified-memory caveat. The description only restates the gpu/vram_gb input choice and adds no format, syntax, or interaction detail beyond the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Which open-weight LLMs fit this GPU') and enumerates the exact outputs returned: a recommended everyday model, the largest that fits, the best at Q8, and the first out of reach. An agent immediately knows what the tool computes. It stops short of naming or contrasting sibling alternatives like can_i_run or estimate_vram, so it is clear but not sibling-differentiating.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It tells the agent where GPU inputs come from ('a name or id from list_gpus, or vram_gb for any other card'), which is useful routing guidance. However, it never states when this tool is preferable to siblings such as estimate_vram or can_i_run, nor any exclusions or prerequisites. Usage is implied rather than specified.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Nodegrove VRAM: can I run it? toolsInspect
Try in Browser
Claude Code, inside the Godot 4 editorFlockbay puts an AI agent in the real Godot 4 editor: it writes GDScript, runs your game and fixes what breaks. Free, or bring your own Claude Code or Codex.flockbay.comAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources