Skip to main content
Glama

model_details

Retrieve detailed information about a specific Kaggle model using its reference. Access model metadata and specifications directly to support your workflows.

Instructions

Get model details

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
model_refYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

C2.1/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Get' implies a read operation, but the description does not explain response shape, authentication requirements, error behavior, or whether the operation is safe. This is minimal coverage for a tool with no annotation support.

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

Conciseness2/5

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

The description is only one sentence, which is concise, but it is under-specified rather than efficiently complete. 'Get model details' adds almost no information beyond the tool name and does not earn its place with useful details, fitting the under-specification pattern.

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

Completeness2/5

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

Given the lack of annotations, output schema, and parameter descriptions, this tool needs more context to be complete. The description does not specify what model_ref refers to, what details are returned, or how this relates to the broader model management API, so an agent would struggle to invoke it correctly.

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

Parameters1/5

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

The only parameter, model_ref, has an empty description in the schema and the tool description does not explain its format, required values, or examples. With 0% schema coverage, the description must compensate but does not, leaving an agent unable to construct a valid invocation.

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

Purpose3/5

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

The description 'Get model details' states a clear action and a resource, but 'model details' is vague and does not specify what kind of model or which details are returned. It does not differentiate from sibling tools like get_model_instance or list_model_instances, so an agent cannot be certain this is the right call for a specific need.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the many sibling tools such as list_models, get_model_instance, or list_model_instance_versions. The description provides no context about prerequisites, use cases, or exclusions, leaving the agent to infer usage solely from the name.

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