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gguf-mcp

gguf-mcp

by koleharv
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Estimate VRAM needed

estimate_vram

Estimates memory needed to run a GGUF or safetensors model, factoring in weights and a default KV cache for GGUF. Enter a model path to see if it fits your GPU.

Instructions

Will this model fit? Estimates memory as exact weights size plus a modeled fp16 KV cache (GGUF; context defaults to min(model context, 8192)). Safetensors models get a weights-only figure.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "path": "<path>"
    },
    "name": "estimate_vram"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesPath to a .gguf or .safetensors file, a *.safetensors.index.json, or a model directory
context_lengthNoContext window to budget the KV cache for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries full responsibility and discloses the estimation methodology (exact weights + modeled fp16 KV cache), the default context behavior, and the safetensors weights-only distinction. This is transparent about key assumptions, though it doesn't mention return format or error handling for unsupported files.

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

Conciseness5/5

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

The description is three short sentences, starting with a core question and immediately providing methodology and format details. It is front-loaded and has no redundant information.

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

Completeness3/5

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

The estimation logic is well explained, but the output format or return value is never described. Since there is no output schema, this leaves an important gap for an agent expecting a specific result type, such as a number or an object with a breakdown.

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

Parameters4/5

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

The schema already provides full descriptions for both parameters (100% coverage), but the tool description adds the default context length (min(model context, 8192)) and clarifies how the file type (GGUF vs safetensors) changes the estimate, adding value beyond the schema.

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

Purpose5/5

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

The description opens with the question 'Will this model fit?' and explains it estimates memory as weights plus KV cache for GGUF, with a weights-only figure for safetensors. This clearly specifies the tool's purpose and differentiates it from sibling tools like inspect_model or list_tensors.

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

Usage Guidelines4/5

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

The description clearly implies the use case—estimating VRAM for GGUF vs safetensors models—and gives context about default context length. However, it doesn't explicitly name alternatives or say when not to use this tool, but the context is clear enough for an agent to decide.

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

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