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gguf-mcp

gguf-mcp

by koleharv
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
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Get model metadata

get_metadata

Fetch metadata from GGUF or safetensors model files. Filter keys by substring and receive compact summaries of large arrays, avoiding context flooding.

Instructions

The model's metadata key-value store (GGUF) or metadata block (safetensors). Large arrays arrive as {count, sample} summaries and long strings are truncated, so tokenizer vocabularies can't flood the context. Filter keys by substring (e.g. 'tokenizer' or 'rope').

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": "get_metadata"
  }
}

Input Schema

NameRequiredDescriptionDefault
pathYesPath to a .gguf or .safetensors file, a *.safetensors.index.json, or a model directory
filterNoCase-insensitive substring to filter metadata keys

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.1

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full burden. It discloses key behaviors: large arrays are summarized as {count, sample}, long strings are truncated to prevent context flooding, and filtering is case-insensitive. This helps the agent anticipate output size and use the filter.

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?

Two sentences, front-loaded with purpose, then behavior and usage hint. Every word earns its place with no redundancy.

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?

For a simple retrieval tool with two parameters and no output schema, the description sufficiently explains the output format limitations and filtering behavior. It covers what an agent needs to know to use the tool effectively.

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 coverage is 100%, with both path and filter documented. The description adds a filter example but no substantive new semantics beyond the schema's already-clear parameter descriptions, meeting 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?

Description clearly states the tool retrieves model metadata from GGUF or safetensors files, specifying the resource and scope. The verb 'get' and the focus on metadata distinguish it from sibling tools like inspect_model and 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 Guidelines3/5

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

Usage is implied: use this tool when you need model metadata. However, it does not explicitly state when to prefer this over siblings or any exclusions, leaving the agent to infer from the tool name and description.

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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