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gguf-mcp

gguf-mcp

by koleharv
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Local

Inspect a local model file

inspect_model

Summarize local GGUF or safetensors models: architecture, parameter count, quantization, context length, file size, tensor count. Reads only headers, so inspecting a 70GB model is instant.

Instructions

Summarize a local GGUF or safetensors model: architecture, parameter count, quantization, context length, file size, tensor count. Reads only headers — inspecting a 70 GB model is instant. Works on extension-less GGUF blobs (e.g. Ollama's).

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": "inspect_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesPath to a .gguf or .safetensors file, a *.safetensors.index.json, or a model directory

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4.4/5.0
Behavior4/5

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

The description explicitly discloses a key behavioral trait: it only reads headers, making it fast and implying a read-only, non-destructive action. This is especially important because no annotations are provided. It also signals that it handles extension-less files, adding transparency about compatibility. It does not detail all possible edge cases, but the critical behavior is well covered.

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 sentences with no fluff. It front-loads the primary purpose, then provides the speed benefit, and ends with a compatibility note. Every sentence contributes meaningful information, and the structure is easy to parse.

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?

The tool is simple (one parameter, no output schema), and the description lists the output fields (architecture, parameter count, quantization, context length, file size, tensor count), giving the agent a clear idea of what to expect. It lacks an explicit return format or error handling notes, but those are less critical for a straightforward inspection tool. The description provides sufficient context for effective selection and invocation.

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 fully documents the single 'path' parameter, so the baseline is 3. The description adds value by mentioning that extension-less GGUF blobs are supported, which goes beyond the schema's list of accepted file types. This extra nuance directly informs the user about valid path values, warranting a score above the baseline.

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 clearly states the tool summarizes local GGUF or safetensors models and lists the specific attributes returned (architecture, parameter count, quantization, context length, file size, tensor count). This distinguishes it from sibling tools like 'list_tensors' and 'estimate_vram' by focusing on a holistic summary rather than low-level details.

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?

Provides clear usage context: 'Reads only headers — inspecting a 70 GB model is instant' indicates it is ideal for quick inspections without loading the full model. It also notes compatibility with extension-less GGUF blobs (e.g., Ollama's), giving a concrete scenario. However, it does not explicitly contrast with sibling tools like 'get_metadata' or state when not to use this tool.

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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