Skip to main content
Glama
Sign In
kaggle-mcp

kaggle-mcp

by parkseokjune
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

kaggle_download_model

Download model artifacts from Kaggle to a work directory by specifying a model version (owner/model/framework/variation/version).

Instructions

Download a specific model instance version's artifacts into a work dir. model_version is 'owner/model/framework/variation/version'. Returns paths.

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": {
      "model_version": "<model_version>"
    },
    "name": "kaggle_download_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
model_versionYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.2

TDQS

A4.3/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false (writes locally) and destructiveHint=false (no remote destruction). The description adds that it downloads to a work dir and returns paths, but does not detail overwrite behavior or permissions. Some behavioral context is provided, but more would be helpful.

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: first states purpose, second defines the parameter format. No extraneous information. Every word earns its place.

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?

Given the tool's simplicity (1 parameter, output schema exists), the description covers the core action and parameter format. It could mention default work dir or output structure, but is sufficient for an agent to use correctly.

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

Parameters5/5

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

The single parameter 'model_version' has 0% schema description coverage, making the description's format string essential. It provides an exact template ('owner/model/framework/variation/version'), which is highly valuable for correct invocation.

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 action 'Download' and the target 'specific model instance version's artifacts'. It defines the model_version format precisely, distinguishing this tool from siblings like kaggle_get_model or kaggle_list_models.

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 implies this tool is for downloading artifacts of a specific model version, but does not explicitly state when to use it versus alternatives (e.g., kaggle_get_model for metadata). However, the purpose is clear enough for an agent to select it correctly.

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

Deploy Server

Other Tools