Provides tools for interacting with Kaggle's API, enabling AI agents to search, list, download, upload, and manage Kaggle datasets, kernels, competitions, models, forums, benchmarks, and account settings.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Kaggle MCP Server search for datasets about climate change"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Full Kaggle CLI as a Model Context Protocol (MCP) server — 68 tools (v3.0).
Works with Claude Desktop, Claude Code, OpenAI Codex CLI, Cursor, Hermes Agent, Windsurf, OpenCode, VS Code MCP, Continue.dev, and any stdio MCP client.
Area | Tools |
Kernels | search, list mine, status, logs, files, output download, pull/push, init, update, delete, preview, visibility, topics |
Datasets | search, list mine, details, files, download, init, upload, version, metadata, status, delete, topics |
Competitions | list, files, download, submit, submissions, leaderboard, team-submissions, episodes, replay, logs, pages, topics |
Models | list/get/create/update/delete, instances, versions, version files/download, topics |
Forums | list forums, list/show topics |
Benchmarks | list tasks, list models, task status |
Account | quota (GPU/TPU), account info, CLI config |
Transports
--stdio preferred for agents
--port N HTTP SSE + /rpc for custom clients
Related MCP server: Kaggle MCP Server
git clone https://github.com/mtrakretech/kaggle-mcp.git
cd kaggle-mcp
pip install -r requirements.txtpython kaggle_mcp.py --setupWhat it does:
Tells you where to get a token: https://www.kaggle.com/settings → API → Create New Token
Asks for username + API key (key input hidden)
Optionally reuses a downloaded kaggle.json (cwd / Downloads / ~/.kaggle)
Saves:
~/.kaggle/kaggle.json (chmod 600 when possible)
project .env (KAGGLE_USERNAME, KAGGLE_KEY, KAGGLE_API_TOKEN)
Validates with kaggle quota
Non-interactive:
python kaggle_mcp.py --setup --username YOUR_USER --key YOUR_KEY
python kaggle_mcp.py --setup --from-json ~/Downloads/kaggle.json
python kaggle_mcp.py --setup --no-env # only ~/.kaggle/kaggle.json
python kaggle_mcp.py --setup --no-validate # skip API checkManual alternatives:
export KAGGLE_USERNAME=your_username
export KAGGLE_KEY=your_api_key
export KAGGLE_API_TOKEN=$KAGGLE_KEYOr hand-write ~/.kaggle/kaggle.json:
{"username":"your_username","key":"your_api_key"}python kaggle_mcp.py --setup # first time
python kaggle_mcp.py --list-tools
python kaggle_mcp.py --stdioReplace /ABSOLUTE/PATH/TO/kaggle-mcp/kaggle_mcp.py with your real path.
Copy-paste examples: examples/.
%APPDATA%\Claude\claude_desktop_config.json (Windows) / ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):
{
"mcpServers": {
"kaggle": {
"command": "python",
"args": ["/ABSOLUTE/PATH/TO/kaggle-mcp/kaggle_mcp.py", "--stdio"],
"env": {
"KAGGLE_USERNAME": "your_kaggle_username",
"KAGGLE_KEY": "your_kaggle_api_key",
"KAGGLE_API_TOKEN": "your_kaggle_api_key"
}
}
}
}Same mcpServers.kaggle JSON shape as above.
~/.codex/config.toml)[mcp_servers.kaggle]
command = "python"
args = ["/ABSOLUTE/PATH/TO/kaggle-mcp/kaggle_mcp.py", "--stdio"]
[mcp_servers.kaggle.env]
KAGGLE_USERNAME = "your_kaggle_username"
KAGGLE_KEY = "your_kaggle_api_key"
KAGGLE_API_TOKEN = "your_kaggle_api_key"config.yaml)mcp_servers:
kaggle:
command: python
args:
- /ABSOLUTE/PATH/TO/kaggle-mcp/kaggle_mcp.py
- --stdio
env:
KAGGLE_USERNAME: your_kaggle_username
KAGGLE_KEY: your_kaggle_api_key
KAGGLE_API_TOKEN: your_kaggle_api_key
timeout: 180Use stdio (not HTTP SSE) with Hermes.
See examples/opencode_config.json, examples/vscode_mcp.json, examples/continue_config.yaml.
search_kernels list_my_kernels kernel_status kernel_logs kernel_files kernel_output pull_notebook push_notebook init_kernel update_kernel delete_kernel preview_notebook toggle_kernel_visibility list_kernel_topics show_kernel_topic
search_datasets list_my_datasets dataset_details list_dataset_files download_dataset init_dataset upload_dataset update_dataset get_dataset_metadata dataset_status delete_dataset list_dataset_topics show_dataset_topic
list_competitions list_competition_files download_competition_data submit_competition list_competition_submissions competition_leaderboard list_team_submissions list_competition_episodes download_competition_replay download_competition_episode_logs list_competition_pages list_competition_topics show_competition_topic
list_models model_details init_model create_model update_model delete_model list_model_instances get_model_instance init_model_instance create_model_instance update_model_instance delete_model_instance list_model_instance_versions list_model_version_files download_model_version create_model_version delete_model_version list_model_topics
list_forums list_forum_topics show_forum_topic list_benchmark_tasks list_benchmark_models benchmark_task_status get_quota get_account_info get_config
Verified working against real API on this machine:
kernels search/list/status/logs/files/pull/preview/output
datasets search/list/details/files/metadata/download/topics
competitions list/files/leaderboard/pages/topics/team-submissions
models list/get/instances/versions
forums list/topics, benchmarks list/models, quota, config, init skeletons
Known Kaggle-side soft fails (tool wiring OK; API returns error):
dataset_status → 404 on some public datasets
list_competition_submissions → 400 if you never entered the competition
list_model_version_files / some instance paths → 404 if version ref wrong
topic show_* → 429 under rate limit (retry later)
Destructive tools (delete_*, submit_competition, push_notebook, uploads) are implemented but not auto-run in CI-style tests.
No hardcoded credentials
Env vars or ~/.kaggle/kaggle.json only
Key never leaves your machine except to Kaggle
MIT — see LICENSE
Show benchmark task status
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. 'Show... status' implies a read-only operation, but the description does not explain what status information is returned, how task is identified, or whether it polls or returns a snapshot.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded, with no filler words. However, it is under-specified to the point that the single sentence does not provide enough actionable content for a tool with an undocumented required parameter.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter status tool with no output schema and no annotations, the description should at least mention what the response represents and what input is expected. It currently only states the bare function, leaving an agent to guess at semantics.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage and the sole 'task' parameter is left unexplained. The description adds no information about the expected format, identifier type, or how to obtain a valid task value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a concrete verb ('show') and names the exact resource ('benchmark task status'), making it clear this is a status lookup for a single benchmark task. It is not as strong as a 5 because it doesn't explicitly contrast with list_benchmark_tasks, but the resource is specific enough to differentiate at a glance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given for when to use this tool versus list_benchmark_tasks or the sibling status tools. An agent gets no context about prerequisites (e.g., a running task ID) or situations where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show competition leaderboard
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| download | No | ||
| page_size | No | ||
| output_dir | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral disclosure burden. 'Show' implies a read-only display, but the description does not disclose pagination behavior, download side effects, output format, or any other operational traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short, but this is under-specification rather than effective conciseness. It omits essential behavioral and parameter context, so the brevity does not serve the agent's needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, 0% schema description coverage, and a four-word description, this tool is seriously under-described. An agent cannot correctly select or invoke this tool without significant external knowledge.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description provides no parameter information. The required 'comp', 'download', 'page_size', and 'output_dir' parameters are all undocumented, so the agent cannot determine what values to supply.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Show') and a specific resource ('competition leaderboard'). It is not a tautology and the resource is distinct among the large sibling list, though it does not explicitly differentiate itself from related competition tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives like list_competition_submissions or list_competition_episodes. The description leaves the agent to infer that it is for viewing rankings, but provides no explicit context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create model from folder with model-metadata.json
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool creates a model by reading a folder with metadata, but does not mention authentication, side effects, whether existing models are overwritten, or what happens on invalid metadata.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word contributes to understanding the tool's core purpose and input expectation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema and no annotations, the description is too sparse to be complete. It lacks information about return values, error behavior, authentication, and the relationship to model initialization or update workflows.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description for 'folder' is empty (0% coverage), so the description must compensate. It adds essential meaning by indicating that the folder must contain model-metadata.json, but it does not clarify path format, required metadata fields, or local versus remote paths.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: 'Create model from folder with model-metadata.json'. This clearly distinguishes it from sibling tools like create_model_instance and create_model_version, and the source of input is specified.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as init_model, update_model, or create_model_version. The description only implies usage when a folder with model-metadata.json is available, but does not state prerequisites or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create model instance from folder
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. Saying 'Create' implies mutation, but it does not describe side effects: what gets uploaded, whether the instance is versioned, whether existing data is overwritten, or what success/failure looks like.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is five words with no filler or redundancy. It is front-loaded and easy to parse, though it is so terse that it leaves important behavioral and usage gaps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, zero documentation on the only parameter, and several closely related model-instance tools, the description is too sparse for an agent to invoke it confidently and correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, 'folder', has an empty schema description and 0% coverage, so 'from folder' is the sole hint. It adds the idea that this parameter is the source folder, but it does not clarify expected path format, local versus remote paths, or how the folder relates to the model instance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the action ('Create') and the resource ('model instance'), and indicates the source ('from folder'). This is specific enough to distinguish from list/update/delete operations, but it does not differentiate from closely related siblings such as init_model_instance or create_model_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives like init_model_instance, update_model_instance, or create_model_version. It also omits prerequisites, such as whether an existing model must already exist before creating an instance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create new model instance version from folder
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes | ||
| instance_ref | Yes | ||
| version_notes | No | New version |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It only states that a version is created from a folder but does not disclose side effects, whether files are uploaded, whether an existing model instance is required, or whether the operation can overwrite existing versions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single eight-word sentence with no filler and the action is front-loaded. It is concise, though the brevity sacrifices important operational details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, 0% parameter coverage, and two required parameters, the description leaves critical context unspecified. The agent cannot determine what response to expect, whether the model instance must already exist, or how version_notes behaves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and every property description is empty. The prose clarifies 'folder' as the source and implies 'instance_ref' identifies the model instance, but 'version_notes' is completely unmentioned. This does not adequately compensate for three undocumented parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Create') on a clear resource ('model instance version') with an input source ('from folder'). It is distinguishable from version-management siblings like delete_model_version and download_model_version, though it doesn't explicitly contrast with create_model_instance.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives is provided. The phrase 'from folder' implies the input source, but there is no mention of prerequisites such as an existing model instance or when a user should prefer create_model_version over create_model_instance/init_model_instance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Dataset metadata via REST API
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden, but 'via REST API' is an implementation detail rather than behavioral disclosure. It does not state whether the operation is read-only, whether authentication is required, or what happens for an invalid ref.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The definition is brief and front-loaded, but it is a fragment rather than a complete sentence and is under-specified. The brevity reflects missing crucial details rather than effective conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and one undocumented required parameter, the description is inadequate. It omits what 'ref' means, what the metadata response contains, and how this tool differs from the near-duplicate sibling get_dataset_metadata.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has zero description coverage and the description never explains the required 'ref' parameter. An agent must guess that 'ref' identifies a dataset, with no confirmation from any source.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the resource (dataset) and general subject (metadata), so an agent can infer it retrieves dataset metadata. However, it has no explicit verb and does not distinguish this tool from the similarly named sibling get_dataset_metadata, which appears to serve the same purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 instead of overlapping siblings like get_dataset_metadata, list_my_datasets, or search_datasets. The description provides no selection criteria, exclusions, or contextual triggers.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Dataset creation/processing status
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, but it only implies a read/poll operation. It does not disclose whether the call is read-only, whether it errors for datasets not yet created, what status values can be returned, or whether a creation/update must be initiated first.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At four words, nothing is wasted, but this is under-specification rather than functional conciseness. A noun phrase with no verb and no supporting detail fails to earn its place as a usable definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
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), but with no output schema the description should explain what status information comes back. It provides neither return semantics nor error/edge-case behavior, so the definition is incomplete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the ref property has an empty description, so the description must compensate. It never says what ref should contain, and the 'creation/processing' framing creates ambiguity between a dataset identifier and a processing/job task identifier.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the resource (dataset) and a specific lifecycle aspect (creation/processing status), which modestly distinguishes it from dataset_details or get_dataset_metadata. However, it lacks an explicit verb like 'get' or 'check', and 'Dataset creation/processing status' reads close to a restatement of the tool name dataset_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to call this tool versus its dataset siblings (dataset_details, get_dataset_metadata, upload_dataset) or the parallel kernel_status. An agent cannot tell whether this is the right call for polling an async creation/update workflow.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete dataset permanently
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry behavioral disclosure. 'Permanently' explicitly signals irreversibility, which is valuable for a destructive operation, but it omits other relevant facts such as required permissions, whether associated versions/files are deleted, and error behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three words with no filler, and the verb and resource are front-loaded. Every word earns its place, with 'permanently' adding non-redundant behavioral meaning.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations, no output schema, and an undocumented required parameter, this description is too thin to be self-sufficient. It explains what action occurs but not what 'ref' must contain, what side effects are triggered, or what safeguards/authorization are needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the only parameter 'ref' has an empty description. The tool description never mentions 'ref' or its expected format, so an agent must infer from the parameter name and tool context that it is a dataset reference; this is weak compensation for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Delete'), a specific resource ('dataset'), and the critical qualifier 'permanently.' This distinguishes it from sibling tools like delete_model, delete_kernel, and update_dataset without needing to open their schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives. It does not mention relevant conditions such as ownership, whether a dataset can be restored, or that update_dataset should be used for non-destructive changes.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a kernel permanently
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. 'Permanently' does disclose the most important trait, irreversibility, but it does not mention required permissions, cascading effects on associated files or versions, or failure modes. This is adequate but incomplete for a destructive operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single four-word sentence with no filler. The action is front-loaded and the critical qualifier 'permanently' earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The operation is simple and the action is clear, but the required parameter is completely undocumented and there is no output schema or annotation to fill the gap. An agent would need external knowledge of the API to know what to pass as 'ref'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, 'ref', has an empty description, and schema description coverage is 0%. The description never explains what 'ref' refers to, whether it is an ID, slug, or full URL, so an agent cannot determine how to fill the required parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb, 'Delete', and a clear resource, 'a kernel', while adding 'permanently' to convey finality. This distinguishes it cleanly from sibling delete tools like delete_dataset, delete_model, and delete_model_version.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives such as update_kernel or toggle_kernel_visibility. The permanence hint is present, but there is no explicit warning about prerequisites, ownership, or when deletion should be avoided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a model permanently
| Name | Required | Description | Default |
|---|---|---|---|
| model_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. The word 'permanently' does communicate irreversibility, but the description omits consequences such as whether versions or instances are also deleted, ownership requirements, or any confirmation behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and front-loaded, containing no filler words. It is concise, but the brevity contributes to the lack of contextual guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool has a simple single-parameter signature, the complete absence of parameter semantics, usage guidance, and behavioral consequences beyond 'permanently' leaves an agent under-equipped to invoke this destructive tool safely and correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, model_ref, has an empty schema description and 0% schema description coverage. The tool description adds no explanation of what model_ref should contain or its expected format, so the agent receives no meaningful parameter guidance.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Delete') and resource ('a model'), adding 'permanently' to signal irreversibility. It does not explicitly differentiate from sibling tools like delete_model_instance or delete_model_version, though the resource name is reasonably distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus delete_model_instance, delete_model_version, or delete_dataset. The agent must infer usage entirely from the tool name and sibling context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a model instance
| Name | Required | Description | Default |
|---|---|---|---|
| instance_ref | Yes |
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. 'Delete' implies a destructive operation, but the description does not mention irreversibility, permissions, cascading effects on versions, or whether deletion is permanent. This is a significant gap for a destructive tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short with no fluff, but it is more under-specification than purposeful conciseness. It adds little beyond the tool name and provides no structured information, so conciseness is adequate but not a strength.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter destructive tool with no output schema and no annotations, the description should explain the parameter, the consequences of deletion, and what the response indicates. It covers none of these, leaving the agent with only the operation's name and no operational context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter, instance_ref, with an empty description (0% schema coverage). The tool description does not compensate by explaining what instance_ref should contain, such as an ID, slug, or path. The agent receives no meaningful semantic information about the required parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and object: 'Delete a model instance.' It clearly identifies the operation and distinguishes it from sibling tools like delete_model and delete_model_version. However, it does not define what a model instance is, so it is clear but not fully explanatory.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives such as delete_model or delete_model_version. No prerequisites, exclusions, or contextual triggers are provided, so the agent must infer usage entirely from the tool name and sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Delete a model instance version
| Name | Required | Description | Default |
|---|---|---|---|
| version_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description must carry the behavioral disclosure burden. It only states the delete action and does not mention permanence, side effects, permissions, or whether the operation can be undone.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. However, it is under-specified for a destructive operation and provides no operational context beyond the action itself.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no annotations and no output schema, this description is far from complete. An agent cannot determine how to construct version_ref, whether deletion is permanent, or what response to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage and the version_ref parameter is undocumented. The description only hints that a model instance version is the target, but does not explain what format version_ref should take, how to obtain it, or any constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb and resource: 'Delete a model instance version.' This distinguishes it from sibling tools like delete_model and delete_model_instance by targeting the version level, and it is not a tautology of the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. With sibling tools covering create_model_version, delete_model_instance, and update_model, the agent receives no condition or exclusion to select this one correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download competition files
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| file_name | No | ||
| output_dir | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral details on its own, but it only states the high-level download action. It does not clarify where files are saved, what the output_dir default behavior is, whether authentication or quota is needed, or what side effects occur. The verb 'download' conveys the core action but little else.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short and front-loaded, with no filler, repetition, or unnecessary words. While brevity contributes to under-specification, the phrasing is efficient and immediately understandable at a high level.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and three undocumented parameters, the description is too minimal to enable correct invocation. It omits parameter roles, optionality, destination behavior, and the relationship to sibling download and listing tools, leaving significant gaps for an agent to guess.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All three parameter descriptions are empty (0% schema coverage), and the description does not explicitly explain comp, file_name, or output_dir. It provides only minimal domain context through the phrase 'competition files,' which weakly implies that file_name refers to a file in a competition, but it does not compensate for the schema's missing parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear action-object pair, 'Download competition files,' which identifies both the operation and the resource type. It is distinct from dataset/model downloads and from listing operations like list_competition_files, though it does not explicitly distinguish itself from download_competition_replay or download_competition_episode_logs.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to choose this tool over alternatives such as list_competition_files, download_dataset, or download_competition_replay. There are no prerequisites, no exclusions, and no mention of whether file_name is optional or what happens when it is omitted.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download agent logs for a simulation episode
| Name | Required | Description | Default |
|---|---|---|---|
| episode_id | Yes | ||
| output_dir | No | ||
| agent_index | No |
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. It only names the action and target resource; it does not describe what files are written, how output_dir is used, what agent_index means, or how failures are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with the verb and object front-loaded. There is no filler, though the extreme brevity sacrifices behavioral and parameter detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With three parameters, no annotations, no output schema, and 0% schema description coverage, a one-line description is insufficient. An agent cannot tell whether agent_index selects one agent or filters within the download, where files are written, or what the tool returns.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It lightly hints at episode_id ('simulation episode') and agent_index ('agent logs'), but output_dir is completely unexplained and the agent_index behavior is ambiguous. This does not meaningfully compensate for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Download') and a specific resource ('agent logs for a simulation episode'), which distinguishes it from sibling tools like download_competition_replay or download_competition_data. It does not explicitly name a competing sibling, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool instead of related tools such as download_competition_replay, kernel_logs, or list_competition_episodes. There are no exclusions, prerequisites, or alternative-selection conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download simulation episode replay
| Name | Required | Description | Default |
|---|---|---|---|
| episode_id | Yes | ||
| output_dir | No |
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. It implies a read-only fetch via the verb 'download' but does not disclose output format, file type, file size, authentication requirements, or what a 'replay' actually contains (visual sim data, event frames, etc.).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely short (four words), but this is under-specification rather than disciplined conciseness. It essentially restates the tool name with no added content, so brevity is not earning its place alongside useful information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a download operation with two parameters, no annotations, no output schema, and many similar download siblings, this description is grossly inadequate. An agent cannot determine what is returned, whether `output_dir` is required in practice, what file artifacts to expect, or how this differs from `download_competition_episode_logs`.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds zero parameter meaning beyond what the names suggest. The parameter names (`episode_id`, `output_dir`) are intuitive enough to hint at their roles, but the description neither confirms the expected format of `episode_id` nor explains the behavior or default of `output_dir`, so it fails to compensate for the empty schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Download') and a specific resource ('simulation episode replay'), which clearly identifies the action. However, it does not differentiate from the closely related sibling `download_competition_episode_logs`, and the meaning of 'replay' versus 'logs' is left to the agent to infer.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. The sibling list includes `download_competition_data`, `download_competition_episode_logs`, `download_dataset`, and `download_model_version`, but the description provides no selection criteria or exclusions to help an agent route correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download dataset files
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| unzip | No | ||
| file_name | No | ||
| output_dir | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Download dataset files' and does not explain side effects such as where files are written, whether unzip is applied, overwrite behavior, or authentication/access requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely terse, but the brevity is not a virtue because it omits all operational detail. A sentence that only restates the tool's basic action does not earn its place as the sole source of guidance for a four-parameter download operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four parameters, no annotations, no output schema, and 0% schema description coverage, this description is far from complete. An agent has no way to correctly construct inputs, understand return behavior, or avoid misusing the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameter descriptions in the schema are empty (0% coverage), and the tool description provides no parameter meanings. The agent cannot infer what 'ref' should contain or how 'unzip', 'file_name', and 'output_dir' affect the download.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Download') and a resource ('dataset files'), so the agent knows the basic operation. However, it does not differentiate from sibling tools such as download_competition_data or download_model_version beyond the tool name.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives like list_dataset_files, download_competition_data, or download_model_version. No context is given about prerequisites, typical use cases, or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download model instance version files
| Name | Required | Description | Default |
|---|---|---|---|
| untar | No | ||
| output_dir | No | ||
| version_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It does not mention that the tool writes files locally, where it writes them, whether untar defaults to false, or whether it creates directories/overwrites files. The word 'Download' implies a read operation, but important side effects on the local filesystem are undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no filler, so it is concise and front-loaded. However, it is under-specified: the sentence carries only the core purpose and provides no additional structure or context to help the agent use the tool correctly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given three undocumented parameters, no annotations, and no output schema, this one-sentence description is far from complete. An agent cannot determine the required input format, default behavior, output location, or return value. The definition is insufficient for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and every parameter has an empty description. The tool description itself does not explain version_ref, output_dir, or untar. An agent has no way to learn that version_ref identifies the target version, output_dir sets the local destination, or untar controls archive extraction.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: 'Download model instance version files.' This is enough to understand the basic operation and distinguish it from listing or creating model versions. It could be slightly more precise about which files and from which version, but overall the purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus closely related siblings like list_model_version_files or download_dataset. There is no indication of prerequisites, when downloading is appropriate, or when an alternative would be better.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Account username + quota snapshot
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. It only states the returned data ('username + quota snapshot') and does not mention authentication requirements, rate limits, caching behavior, or any side effects. For a probably read-only tool this is a moderate gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely short and front-loaded, with no filler words. However, it is a noun-phrase fragment rather than a complete sentence, which slightly reduces readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there are no parameters and no output schema, the description is nearly sufficient: it tells the agent what the tool returns. Yet it lacks any note on return format, whether authentication is needed, or how it differs from get_quota, so it is only minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so there is nothing for the description to add. The baseline of 4 for parameter-less tools is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the resource ('account') and the two pieces of information returned (username and quota snapshot). It is clear, though telegraphic. It does not explicitly differentiate from sibling get_quota, which likely returns only quota details, so it loses some points on sibling discrimination.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives such as get_quota or get_config. The description neither states a clear context nor provides exclusions, leaving the agent to infer applicability from the tool's name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show kaggle CLI config
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the verb 'Show' is the main behavioral signal, indicating a read-only operation. The description does not disclose whether authentication is required, where the config comes from, or what happens if no config exists, but for a zero-parameter display tool this is a tolerable gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no filler or redundant detail. Every word contributes to identifying the operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a no-parameter, no-output-schema tool, this description conveys the core action but not the return format or any prerequisites. It is minimally adequate, yet leaves the agent to assume what config content will be shown.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so the baseline is 4. The description adds scope by naming 'kaggle CLI config' as the target of the operation, which is all that is needed here.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a clear action ('Show') and a specific resource ('kaggle CLI config'). No sibling tool overlaps with retrieving CLI configuration, making this a precise, non-tautological statement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for inspecting Kaggle CLI configuration, but it does not explicitly state when to use it or mention alternatives. Since all siblings are for kernels, datasets, models, and competitions, the intended use is inferable rather than clearly guided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download dataset-metadata.json
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| output_dir | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It indicates a download operation but does not explain side effects, whether output_dir controls file writing, overwrite behavior, or authentication requirements. Only minimal behavioral information is conveyed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short and contains no fluff, but it is under-specified rather than appropriately concise. A single phrase is not enough structure for a tool with undocumented parameters and no behavioral context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, 0% schema description coverage, and no parameter guidance, the description is radically incomplete. An agent cannot reliably determine how to call this tool or what to expect from it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and both parameter descriptions are empty. The description does not explain what 'ref' means or how 'output_dir' is used, leaving the agent with no semantic grounding for the required parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('Download') and a specific resource ('dataset-metadata.json'), so the agent knows what operation is being performed. It does not explicitly distinguish this from sibling tools like download_dataset or dataset_details, but the target file name provides enough specificity for a basic understanding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives such as download_dataset or dataset_details. There is no statement of intended context, exclusions, or conditions that would route an agent to this tool over a sibling.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download model-instance-metadata.json
| Name | Required | Description | Default |
|---|---|---|---|
| output_dir | No | ||
| instance_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden. It says the tool downloads a file, which implies a read operation, but it does not state what happens with output_dir, whether files are overwritten, what the response contains, or what errors occur. The description is too sparse to be transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no filler or redundant wording. It front-loads the action and target, making the core purpose immediately clear.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the low complexity of the tool, the description is not complete enough for an agent to invoke it correctly. With no output schema and no annotations, the agent is left without guidance on how to construct instance_ref, what output_dir defaults to, or what behavior to expect after the download.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the empty parameter descriptions. It does not explain that instance_ref identifies the model instance or that output_dir controls where the file is saved. The parameter names suggest their roles, but the description adds little explicit semantic value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Download') and a concrete resource ('model-instance-metadata.json'). This clearly distinguishes the tool from siblings like list_model_instances, model_details, or download_model_version, which target different artifacts or operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. The description does not mention prerequisites, exclusions, or name any sibling tool that might be more appropriate in other scenarios. Usage context is only weakly implied by the purpose statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show GPU/TPU quota remaining
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries the behavioral burden. 'Show' clearly signals a read-only query with no side effects, and 'quota remaining' indicates the nature of the result. Still, it does not disclose whether authentication is needed, what units or formatting the quota is returned in, or whether quota is user-specific or project-specific.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with zero filler. Every word contributes to understanding what the tool does.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless read-only query, the description is largely sufficient: purpose and result type are clear. However, it could be more complete by noting whose quota is reported and whether the result reflects current usage or hard limits.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the schema is empty. The description therefore needs to add no parameter-level meaning; the baseline of 4 for a parameterless tool applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Show') and a specific resource ('GPU/TPU quota remaining'), making the tool's purpose immediately clear. No sibling tool covers quota, so it is easily distinguished without opening schemas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when a user needs to check remaining GPU/TPU quota. However, it provides no explicit guidance about when to prefer this over related account/config tools, nor does it mention any preconditions or context such as account scope.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create dataset-metadata.json skeleton
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It does clearly indicate a write side effect: it creates a metadata skeleton file. However, it does not state whether an existing file is overwritten, whether the folder is created if missing, or what the resulting file contains beyond its name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single direct imperative sentence with no filler or redundant phrasing. It is extremely concise and front-loaded, though the brevity comes at the cost of useful contextual detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter, no output schema, and no annotations, the description is too sparse to fully equip an agent. It conveys the basic action but omits return behavior, prerequisites, side-effect details, and the tool's place in the dataset workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero description coverage for the single required 'folder' parameter, and the tool description does not mention the parameter at all. The property name implies a directory, but the agent gets no guidance about path format, existence requirements, or how the folder is used.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Create') and a specific artifact ('dataset-metadata.json skeleton'), so an agent can infer the tool's primary function. It is also clearly distinct from sibling tools like init_kernel and init_model, though the term 'skeleton' is somewhat terse and assumes domain knowledge.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no explicit guidance on when to use this tool relative to alternatives such as update_dataset, upload_dataset, or get_dataset_metadata. It does not mention prerequisites, such as whether the target folder must exist or whether this should be run before other dataset operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create kernel-metadata.json skeleton
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the burden of disclosing behavioral traits. It states that a skeleton file is created, but does not say whether it overwrites existing files, requires a specific directory state, or has other side effects. This is significant for a tool that writes to the filesystem.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler and the core action is front-loaded. However, it is somewhat terse, bordering on under-specification rather than efficient clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool is simple with one parameter and no output schema, the description fails to explain the meaning of the 'folder' parameter or any behavior around file creation. An agent would likely need external knowledge to use this tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one required parameter, 'folder', with an empty description and 0% schema coverage. The description does not mention 'folder' at all, leaving the agent to guess what value to provide and how it relates to creating the skeleton.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific verb ('Create') and a concrete resource ('kernel-metadata.json skeleton'), which clearly separates it from siblings like init_dataset and init_model. However, 'skeleton' is somewhat vague about what contents or scope are included.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives such as init_model, init_dataset, or create_model. There are no examples, prerequisites, or exclusions to help route an agent to the correct tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Initialize model-metadata.json in folder
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
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. 'Initialize' implies a write/creation action, but it does not say whether the tool overwrites an existing model-metadata.json, creates parent directories, requires the folder to already exist, or has any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler words. It states the action and object efficiently, though it is arguably under-specified rather than merely concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a relatively simple one-parameter tool, but the description still leaves important gaps: no usage guidance, no behavioral details, and no output/return information. Given the large sibling list and empty schema descriptions, the definition is not complete enough for an agent to invoke it confidently.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the folder parameter has an empty description, so the description must compensate. The phrase 'in folder' weakly identifies folder as the target location, but it adds no detail about path format, required permissions, whether the folder must exist, or how it relates to Kaggle model structure.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('Initialize'), a concrete resource ('model-metadata.json'), and a location ('in folder'), so the core purpose is clear. It does not explicitly distinguish itself from sibling initialization tools like init_dataset, init_kernel, or init_model_instance, but the target file makes the intent identifiable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance about when to use this tool versus its many init_* siblings, nor does it state any preconditions or exclusions. An agent would have to infer that this is for preparing model metadata rather than datasets, kernels, or model instances.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Init model-instance-metadata.json
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It only says 'Init', which implies a side effect such as writing a local metadata file, but it does not disclose whether existing files are overwritten, whether the folder must exist, or whether any remote action occurs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded, but it is under-specified rather than efficiently complete. There is no sentence structure or supporting context, making it more of a fragment than an informative description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one required parameter, no annotations, and no output schema, the description still leaves out essential context: where the folder points, what initialization actually does, and what happens on re-run. The sibling list hints at a model-instance workflow, but the description does not connect those dots.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, 'folder', has an empty schema description and schema coverage is 0%. The tool description never mentions the folder parameter, its meaning, or how to supply it, so it does not compensate for the schema gap.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific artifact, 'model-instance-metadata.json', and an 'init' action, which loosely distinguishes it from siblings like init_model or init_kernel. However, it does not explain what initialization entails, and it reads mostly as a restatement of the tool name plus a filename.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as init_model, create_model_instance, or update_model_instance. It provides no conditions, exclusions, or workflow context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List kernel output files (metadata)
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. The parenthetical 'metadata' hints that this tool does not return file contents, but it does not mention pagination behavior, required ref relationship, output shape, or any side effects. This leaves significant behavioral information undisclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loads the primary verb, so it is not verbose. However, it is under-specified to the point of being a phrase rather than a useful definition. It is concise but not appropriately complete for a tool with undocumented parameters and no annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, no annotations, and zero parameter documentation, the description leaves too much implicit. An agent cannot confidently determine what ref means, what metadata fields will be returned, or how this tool relates to kernel_output. The description is too incomplete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%: both 'ref' and 'page_size' have empty descriptions. The tool description adds no meaning to either parameter, failing to explain what ref refers to or how page_size affects results. The description does not compensate for the schema gap at all.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: 'List kernel output files (metadata)'. It explicitly scopes the operation to listing metadata rather than downloading content. However, it does not differentiate itself from closely related siblings such as kernel_output or list_model_version_files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives like kernel_output or kernel_logs. The description implies a listing use case, but it does not state exclusions, prerequisites, or which sibling would be more appropriate for retrieving file contents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get kernel execution logs
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden of behavioral disclosure. It implies a read-only operation via the verb 'Get' but does not describe what the logs contain, what format they are returned in, whether pagination or filtering applies, or how errors are handled. This is minimal behavioral context beyond the tool's basic purpose.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence with no unnecessary words, making it efficient. However, it is under-specified rather than appropriately sized because it omits essential information about the parameter and behavior, so the brevity is not entirely a virtue.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With one required parameter whose semantics are undocumented, no annotations, no output schema, and no distinction from related kernel tools, the description leaves too much unknown. An agent cannot reliably determine how to fill in ref, what the tool will return, or how it differs from kernel_output, making this definition inadequate for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter, ref, has an empty schema description and schema description coverage is 0%. The description does not mention ref at all, leaving the agent without any indication of what the parameter identifies (e.g., a running kernel, a submitted execution, or a kernel version).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Get') and resource ('kernel execution logs'), which is specific enough to convey the general purpose. However, it does not distinguish itself from sibling tools such as kernel_output, kernel_files, or kernel_status, leaving potential ambiguity about differences in scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. Sibling tools like kernel_output and kernel_status offer related functionality, but there is no mention of them or of the conditions that would make kernel_logs the right choice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download kernel output files
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| output_dir | No | ||
| file_pattern | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description names the core behavior (download) but adds no detail on side effects, authentication requirements, or failure modes. With no annotations available, the description carries the full burden and does not disclose whether this writes files to disk, what ref must refer to, or how output_dir is used.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise in word count, but it is under-specified rather than appropriately sized. For a tool with three undocumented parameters, a single phrase omits essential information and is not an adequate definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and an empty input schema, this description is far from complete. Critical information about ref format, output_dir semantics, file_pattern behavior, and expected return values is missing, making reliable invocation impossible.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description mentions none of the three parameters. ref, output_dir, and file_pattern are completely unexplained, so an agent cannot determine what values to supply or how these parameters affect the download.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: downloading kernel output files. However, it does not define what 'output files' means relative to sibling tools like kernel_files or kernel_logs, leaving some ambiguity about its exact scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool instead of alternatives such as kernel_files or kernel_logs. There are no prerequisites, exclusions, or context clues beyond the tool's name and the short description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check kernel run status
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. 'Check' implies a read-only behavior, but the description does not disclose response format, whether it blocks until completion, possible error states, or any side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no wasted words and front-loads the core action. It is concise, though it sacrifices necessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For an agent to call this tool correctly, more context is needed: what 'ref' means, what values status can take, and how this relates to sibling logging/output tools. The low schema coverage and missing output schema make this minimal description insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 0% coverage and the only parameter 'ref' has an empty description. 'Check kernel run status' hints that 'ref' identifies the kernel, but it does not specify the expected format (e.g., slug, version, or ID).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Check kernel run status' clearly identifies the verb (check) and resource (kernel run status). It is not purely tautological with the tool name, but it does not explicitly differentiate itself from closely related kernel tools such as kernel_logs or kernel_output.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance, prerequisites, or exclusions are provided. The agent must infer that this tool is appropriate for status checks, and there is no mention of alternatives like kernel_logs or kernel_output.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List available benchmark models
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure, but it only says the tool lists available models. It does not disclose output shape, pagination behavior, authorization requirements, or whether the list is user-scoped or global, leaving significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence with no filler or redundant detail. It is appropriately concise for a no-parameter list operation and front-loads the essential action and object.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter list operation, the description covers the basic operation and object. However, with no output schema and no annotations, it does not explain what a 'benchmark model' is or how the returned list is scoped, leaving some ambiguity in the context of many sibling list tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and an empty input schema, so there are no parameter semantics for the description to explain. The baseline of 4 for zero-parameter tools applies here because no parameter documentation is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific operation ('List') and a specific resource ('available benchmark models'), making the tool's purpose clear. However, it does not clarify how 'benchmark models' differ from regular models listed by the sibling tool list_models, so differentiation relies mostly on the tool's name rather than the description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as list_models or list_benchmark_tasks. There is no mention of context, exclusions, or preferred alternatives, so an agent receives no decision support beyond the tool's name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List your benchmark tasks
| Name | Required | Description | Default |
|---|---|---|---|
| status | No | ||
| name_regex | No |
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. It states only the high-level action and omits return format, pagination, filtering behavior, and any other operational details, adding little beyond what the tool name already implies.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, front-loaded with the core action and resource, and contains no filler. It is efficient in structure, though its brevity contributes to the lack of substantive guidance elsewhere.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and fully undocumented parameters, the description is not complete enough for reliable invocation. It omits all filtering semantics, output expectations, and context needed to distinguish correct usage from related benchmark tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and both parameters have empty descriptions. The tool description does not explain what status and name_regex mean, how they interact, or what the default behavior is, so an agent has no meaningful semantic information about the parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb, 'List', and identifies the resource as 'benchmark tasks' with user scope ('your'). It is clear and meaningful, but it does not distinguish this tool from similar siblings like list_benchmark_models or benchmark_task_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. The description does not mention filtering by status or name_regex, nor does it explain when to prefer related tools such as benchmark_task_status or list_benchmark_models.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List simulation episodes for a submission
| Name | Required | Description | Default |
|---|---|---|---|
| submission_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure, but it only restates the action. It does not mention pagination, required submission state, permissions, or any other behavioral details beyond the word 'List'.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear, front-loaded sentence with no filler or redundancy. It is not overly verbose, though it is perhaps too sparse to be considered genuinely helpful beyond the basic operation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter list operation this is minimally viable: an agent can reasonably infer the call from the schema and description. However, with no output schema or annotations, it omits what 'simulation episodes' are, how the submission_id should be sourced, and what the response looks like, leaving clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0% and submission_id has an empty description. The tool description only repeats that episodes belong to a submission, which adds little beyond the parameter name and does not explain how to obtain or format the ID.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific action ('List') and object ('simulation episodes'), and links them to a submission, so an agent can tell what the endpoint does. It does not explicitly distinguish this from sibling tools like download_competition_episode_logs, so it does not reach full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use or when-not-to-use guidance is provided, and no alternative tools or prerequisites are mentioned. An agent would have to infer that submission_id should come from a submission-listing tool like list_competition_submissions, which is not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List competition data files
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden, but it only says 'List,' which implies read-only access. It does not disclose whether the result is paginated, what fields are returned, whether it lists only metadata, or any authorization requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief with no filler, which is good for conciseness. However, it is under-specified: the single phrase does not carry enough information on its own, so the brevity is closer to minimalism than to well-structured completeness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
There is no output schema, no annotations, and empty parameter descriptions, so the prose description must supply the invocation contract, but it does not. An agent would not know the return format, pagination behavior, or what values `comp` accepts.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain the `comp` or `page_size` parameters. The word 'competition' weakly hints that `comp` identifies a competition, but there is not enough information for an agent to know how to construct these parameters correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: 'List competition data files.' It is understandable and implies a distinction from sibling tools like `download_competition_data` and `list_competition_pages`, but it does not explicitly call out that distinction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 rather than `download_competition_data`, `list_competition_submissions`, or other related tools. No prerequisites, context, or exclusions are provided, so an agent must infer the appropriate use case from the name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List competition pages (rules/description/evaluation)
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| content | No | ||
| page_name | No |
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. It only restates the list action and does not explain return format, filtering behavior, the effect of the 'content' flag, or any other runtime characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded with the key action and resource, containing no filler. However, it is clipped to a phrase rather than a full sentence, and the brevity comes at the cost of meaningful detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With three parameters, no schema descriptions, no annotations, and no output schema, this description leaves too much unstated. The basics of what the tool lists are visible, but parameter semantics and expected return values are missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not explain the 'comp', 'content', or 'page_name' parameters. An agent must infer their meaning from names alone, which is insufficient for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'List competition pages,' and the parenthetical '(rules/description/evaluation)' clarifies which page types are included. It is distinct enough from siblings like list_competitions and list_competition_files, though it does not explicitly contrast with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives such as list_competition_files or list_competition_submissions. The intended selection context is only implied by the tool name and resource type.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List competitions
| Name | Required | Description | Default |
|---|---|---|---|
| group | No | general | |
| limit | No | ||
| search | No | ||
| sort_by | No | latestDeadline | |
| category | No | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full behavioral disclosure burden. 'List competitions' implies a read-only listing operation, but it does not mention pagination, default sorting/filtering behavior, or what the response contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief but this is under-specification rather than effective conciseness. It contains no filler, but it also contains no substantive information to help an agent understand the tool's behavior.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With five parameters, no output schema, no annotations, and a large sibling toolset, this description is completely inadequate. It fails to explain return values, parameter effects, or how this tool fits into the broader competition workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to any of the five parameters. The description must compensate for the empty parameter descriptions but instead provides no parameter guidance whatsoever.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'List competitions' exactly restates the tool name, providing no information beyond the name itself. It does not specify what kind of competitions, the scope of the listing, or how it differs from related tools such as list_competition_files or list_competition_submissions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives. The description offers no context, exclusions, or references to sibling tools that could help an agent choose correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List your submissions for a competition
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full behavioral disclosure burden. 'List your submissions' implies a read-only operation scoped to the current user, but it does not disclose pagination behavior, required authentication, output format, or how submissions are ordered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word contributes meaning: the verb, the resource, the ownership scope, and the competition context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool appears simple, but with no output schema and 0% schema description coverage, the description should compensate by explaining parameter semantics and return behavior. It does not; an agent would still be unsure what values comp accepts and what the response contains.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so parameter descriptions are empty for both comp and page_size. The overall phrase at least connects comp to a competition and page_size is self-explanatory by name, but the description does not clarify comp's expected format (e.g., slug, ID, or URL) or how page_size interacts with the response.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the exact operation ('List your submissions'), the resource ('submissions'), and the scope ('for a competition'). The word 'your' distinguishes this from the sibling list_team_submissions, which likely covers team submissions instead.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance about when to use this tool versus list_team_submissions or other competition listing tools. The purpose is implied by the description, but no when/when-not or alternative routing is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List competition forum topics
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| page | No | ||
| sort_by | No | hot |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior, but it only restates the listing intent. It does not mention pagination, sorting behavior, authentication requirements, or what the response contains, though the tool's name and defaults hint at these concerns.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, focused sentence with no filler. It is efficiently front-loaded but is perhaps too terse given the parameter and behavioral gaps.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three undocumented parameters and no output schema, the description is incomplete. It fails to explain what a 'competition topic' is, how pagination and sorting work, or how this relates to sibling topic tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It provides no explanation for 'comp', 'page', or 'sort_by', leaving an agent to infer meanings from parameter names and defaults alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('List') and a specific resource ('competition forum topics'), so an agent knows this tool retrieves topics related to competitions. However, it does not differentiate this from sibling tools like list_forum_topics or show_competition_topic, which could cause selection ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives. It does not mention prerequisites, scoping to a competition, or when to prefer show_competition_topic or list_forum_topics instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List files in a dataset
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It only restates the core action and gives no information about pagination behavior, output format, auth requirements, or whether only metadata or actual file contents are listed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear sentence with no filler and the verb is front-loaded. However, it is concise to the point of being sparse, providing the minimum viable statement without supporting detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two parameters, no annotations, and no output schema, the description omits essential context such as the meaning of 'ref', how page_size behaves, and what kind of file information is returned. It is a minimally viable description but not nearly complete enough for confident tool selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description adds no meaning to either parameter. 'ref' is required but completely undefined, and page_size's default of 50 has no explanation. The description fails to compensate for the schema's lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('List') and resource ('files in a dataset'), so an agent can tell this is a read-oriented dataset file listing tool. However, it does not differentiate this from siblings like list_model_version_files or list_competition_files beyond the word 'dataset'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given for when to use this tool versus the many sibling list_* tools, nor does it explain what kind of dataset reference 'ref' should be. Usage context is left entirely to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List dataset discussion topics
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| search | No | ||
| sort_by | No | hot | |
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only indicates that the tool lists topics, implying a read operation, but says nothing about pagination, sorting behavior, authentication requirements, or what the returned topics look like.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler, making it easy to parse. However, it is so minimal that it borders on restating the tool name, and it omits necessary details for a tool with four parameters.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 4 parameters, no schema descriptions, no annotations, and no output schema, yet the description provides no operational context. It fails to explain the required 'ref' parameter, the behavior of search/sort/page_size, or the expected return value.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All four parameters have empty descriptions in the schema, and the description adds no information about ref, search, sort_by, or page_size. An agent has no way to know what values are valid or what the parameters control.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a clear verb 'List' with a specific resource 'dataset discussion topics', making the tool's purpose immediately understandable. The word 'dataset' distinguishes it from sibling tools like list_kernel_topics and list_competition_topics, though it does not explicitly name those alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus alternatives such as show_dataset_topic or list_kernel_topics. The description simply states what the tool does, with no context on selection criteria or situations where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List Kaggle forums
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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. It only states 'List Kaggle forums', which implies a read operation, but it does not reveal whether authentication is required, whether results are paginated, or what the response contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise and front-loaded, with no wasted words. It is appropriate for a zero-parameter tool, though it provides no structural elaboration beyond a single phrase.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple zero-parameter listing tool, 'List Kaggle forums' is minimally viable. However, with no output schema and no annotations, the description omits what the agent should expect as a return value, and whether forum topics are included or stored separately.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so the description does not need to explain parameter meaning. The baseline of 4 for a no-parameter tool applies; there is no parameter gap to compensate for.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'List Kaggle forums'. It clearly identifies the operation and the target entity. However, it does not explicitly differentiate itself from the closely related sibling list_forum_topics, so it stops short of a 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives such as list_forum_topics or show_forum_topic. There is no mention of context, exclusions, or preferred alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List topics in a forum or global search
| Name | Required | Description | Default |
|---|---|---|---|
| forum | No | ||
| search | No | ||
| sort_by | No | hot | |
| category | No | all | |
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only states that topics are listed and gives a scope qualifier; it does not disclose read-only status, authentication requirements, default sorting behavior, pagination, or response format. The scope qualifier adds a little context, but most behavior is left unspecified.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The sentence is short and free of filler, stating the core action and scope in nine words. It is efficient, though too sparse to provide a complete picture; as simple prose it is well-constructed but not detailed enough to be a full specification.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given five undocumented parameters, no output schema, and no annotations, this single-sentence description is far from sufficient. An agent cannot infer valid sort values, category semantics, pagination defaults, or what the returned topic list looks like, making the tool risky to invoke.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All five parameters have empty schema descriptions (0% coverage), and the description names none of them. It does not explain what 'forum', 'search', 'sort_by', 'category', or 'page_size' mean, nor how they affect results, so the agent has no semantic grounding for any parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List topics') and the scope ('in a forum or global search'), making the resource and purpose understandable. It distinguishes this from sibling tools that list datasets, models, or kernels, though it does not explicitly contrast with list_forums or show_forum_topic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 'list_*_topics' tools or 'show_forum_topic'. The phrase 'or global search' hints at a broader use case, but no conditions, exclusions, or alternative tool names are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List discussion topics on a kernel
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| search | No | ||
| sort_by | No | hot | |
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It only states that the tool lists topics; it does not mention sorting defaults, pagination behavior, search semantics, or whether any special permissions are needed. This is minimal but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clean sentence with no filler or repetition. It front-loads the core action and object, which is appropriately concise for a simple listing tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given four parameters, zero schema descriptions, no output schema, and no annotations, the description is not complete enough for reliable invocation. It does not specify what fields are returned, how pagination works, what sort_by values are valid, or how search is applied.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description does not explain the parameters. It implies that 'ref' identifies a kernel, but search, sort_by, and page_size semantics are left entirely to inference from their names and defaults. The description adds very little beyond the raw schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and a specific resource ('discussion topics on a kernel'), clearly identifying what the tool does. It distinguishes itself from similarly named siblings like show_kernel_topic and list_forum_topics by scoping to kernel topics.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as show_kernel_topic or list_forum_topics. No exclusions, prerequisites, or context are shared, leaving the agent to infer usage entirely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List model instances/variations for owner/model
| Name | Required | Description | Default |
|---|---|---|---|
| model_ref | Yes | ||
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description alone must disclose behavior. 'List' implies a read-only operation, but the description does not mention pagination, output shape, authorization, or the effect of page_size. This is below the minimum viable level 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler and gets the main action across immediately. It is concise, though 'instances/variations' could be more precise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description must carry more weight. It omits pagination semantics, required authentication, and the expected return representation, and it does not compensate for the empty parameter descriptions, so it is not sufficient for reliable invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions are all empty (0% coverage), and the description adds only the clue that model_ref is an owner/model identifier. It does not clarify the expected format of model_ref or the meaning of page_size beyond the schema's default and type, leaving a required parameter under-documented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a concrete verb ('List'), a resource ('model instances/variations'), and the scope ('for owner/model'), so an agent can tell this is a listing operation scoped by an owner/model identifier. It does not explicitly distinguish this from the sibling list_model_instance_versions or get_model_instance, and the word 'variations' is somewhat ambiguous, so it falls short of a top score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
'List ... for owner/model' implies the tool should be used when fetching all instances/variations for a given model, but it offers no explicit guidance about when to prefer the many sibling tools, such as get_model_instance or list_model_instance_versions. There is no when-not-to-use guidance or alternative selection criterion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List versions for a model instance
| Name | Required | Description | Default |
|---|---|---|---|
| page_size | No | ||
| instance_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description bears the full burden of behavioral disclosure. It only says 'List versions,' which implies a read operation, but it does not disclose pagination behavior, response contents, ordering, or whether it returns all metadata for each version.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single clear, efficient sentence with no filler or repetition. It is front-loaded with the core purpose, though it is too terse to carry the behavioral and parameter context the tool needs.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no annotations and no output schema, and the description is only one sentence. It does not explain what a returned version list looks like, how pagination works, what instance_ref values are valid, or how this relates to sibling operations. An agent would have to rely heavily on assumptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions are 0% covered, so the description must compensate. It weakly maps 'for a model instance' to instance_ref, but gives no format or semantics for instance_ref and says nothing about page_size beyond what the schema already shows. This is insufficient given the lack of schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific verb and resource: 'List versions for a model instance.' This distinguishes the tool from siblings like list_model_instances (lists instances, not versions) and list_model_version_files (lists files within a version), so an agent can tell them apart.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives such as list_model_instances, list_model_version_files, or create_model_version. The description gives no exclusions, prerequisites, or context for choosing this tool over related siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List / search Kaggle models
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| owner | No | ||
| search | No | ||
| sort_by | No | hotness |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure, but it only says 'list / search.' It does not mention pagination, result shape, authentication needs, defaults, or whether filtering is server-side. The verb implies a safe read operation, but little else is revealed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with no wasted words. However, it is closer to a label than a structured explanation: it lacks any breakdown of parameters, defaults, return values, or usage context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with four undocumented optional parameters, zero annotations, no output schema, and many closely related model tools in the sibling list, this description is far too thin. It leaves the agent guessing how to form valid queries, what defaults apply, and what the response will look like.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and all four parameters have empty descriptions. The description does not explain limit, owner, search, or sort_by beyond the general word 'search,' so an agent cannot determine how to construct a correct request or interpret parameter behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('list / search') and identifies the resource ('Kaggle models'), so an agent can tell this is a discovery/read operation rather than create, update, or delete. It does not explicitly distinguish itself from sibling tools like list_model_instances or model_details, but the object 'models' is clear enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to choose list_models over alternatives such as model_details, list_model_instances, or search_kernels. The phrase only implies the obvious use case—wanting to list or search models—without exclusions, prerequisites, or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List model discussion topics
| Name | Required | Description | Default |
|---|---|---|---|
| model_ref | Yes | ||
| page_size | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. The verb 'list' implies a read operation, but the description adds nothing beyond that — no mention of pagination behavior, ordering, empty-result handling, or what happens when model_ref does not exist. It is minimal but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is four words with zero waste and is appropriately front-loaded, so it is reasonably concise. However, given 0% schema coverage and no annotations, this terseness crosses into under-specification rather than genuine conciseness — there is no elaboration where it is most needed.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (2 params, 1 required, no output schema), which lowers the bar, but the description still leaves too much unexplained. With no annotations and no parameter descriptions, an agent cannot determine how to format model_ref, how page_size affects results, or what a 'topic' contains. The description is adequate as a label but not as a complete specification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the undocumented parameters, but it does not. Neither model_ref (e.g., its expected format: slug, ID, or URL) nor page_size behavior is explained anywhere. The only marginal signal is the tool name itself suggesting model_ref refers to a model.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('list') and resource ('model discussion topics'), and the word 'discussion' usefully distinguishes this from model instance/version tools like list_model_instances and list_model_instance_versions. However, it does not explicitly differentiate from the structurally identical sibling topic-list tools (list_kernel_topics, list_dataset_topics, list_competition_topics), relying on the 'model' qualifier in the name to do that work.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives such as list_forum_topics, list_kernel_topics, or show_*_topic variants. There are no usage conditions, exclusions, or mention of related tools, so the agent must infer the appropriate context entirely from the tool's name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List files in a model instance version
| Name | Required | Description | Default |
|---|---|---|---|
| page_size | No | ||
| version_ref | Yes |
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. It correctly states a read-only 'list' action but does not disclose pagination behavior despite the page_size parameter, nor does it mention what file metadata is returned or whether output is sorted or recursive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler words or redundant restatement of the tool name. Every word contributes to the core purpose, making it appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and 0% parameter schema coverage, the description is too thin for an agent to invoke confidently. It does not explain what version_ref should look like, how page_size affects results, or what the returned file list contains.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It only hints that version_ref refers to a model instance version, but gives no format, identification details, or example. The page_size parameter is entirely unexplained beyond its default value in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and a specific resource ('files in a model instance version'), clearly stating what the tool does. It also distinguishes this tool from siblings like list_model_instance_versions (which lists versions, not their files) and download_model_version (which downloads rather than lists).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to prefer this tool over alternatives such as download_model_version or list_dataset_files, and mentions no prerequisites or workflow context. The intended use is only implied by the tool name and one-line summary rather than explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List your datasets
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. The verb 'List' implies a read-only, non-destructive operation, but nothing is said about pagination behavior, result ordering, empty-result handling, or whether authentication is required.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single three-word sentence with zero wasted content and the operative verb front-loaded. It is efficient, though arguably too terse; the thinness is better penalized under parameter_semantics and contextual_completeness than here.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no output schema and no annotations, the description should at least clarify return shape and pagination semantics. It provides only the core purpose and leaves everything else implicit, which is minimally viable but clearly incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% — both 'page' and 'limit' have empty descriptions. The tool description does not compensate by mentioning pagination parameters, their meaning, or bounds, leaving agents to infer that page/limit control result paging.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List') and resource ('your datasets'), with the possessive 'your' establishing an ownership scope that distinguishes it from public dataset search. It is clear and comprehensible, though the differentiation from siblings like search_datasets or list_dataset_files is carried by the tool name and the word 'your' rather than explicit contrast.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus search_datasets, list_dataset_files, or even the parallel list_my_kernels. The only implicit context is the word 'your,' which suggests the authenticated user's own resources, but there is no mention of prerequisites, pagination workflow, or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List your Kaggle notebooks
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | ||
| limit | No | ||
| sort_by | No | dateRun |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral disclosure burden, and it only states the operation at face value. It does not disclose authentication needs to resolve 'your,' pagination behavior, default sorting, what metadata is returned, or whether it covers only owned (not team) notebooks. For a tool with zero annotation coverage, this is a meaningful gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One short sentence with no filler; the verb and object are front-loaded. It is efficient, though arguably under-specified — a brief note on pagination or ownership scoping would earn its place without bloating the text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and three parameters at 0% schema description coverage, the description must carry the full context. It explains neither the meaning of page/limit/sort_by nor the return shape, leaving an agent to guess at defaults and result format. The core purpose is clear, but too much operational context is missing for a tool with three tunable parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description mentions none of the three parameters (page, limit, sort_by). The parameter names and the sort_by enum are somewhat self-explanatory, which mitigates the gap, but the description itself adds zero semantic value beyond what the schema already exposes. With low coverage, the description was obligated to compensate and did not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') plus a clearly scoped resource ('your Kaggle notebooks'), so an agent immediately understands it enumerates the authenticated user's own kernels. The possessive 'your' distinguishes it from the sibling search_kernels, though it does not name that alternative directly. A 5 would require explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'your Kaggle notebooks' implies this is for the caller's own resources, which lightly hints at scope versus search_kernels or list_team_submissions. However, there is no explicit guidance on when to use this tool, what circumstances favor an alternative, or any exclusions. The entire usage burden is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List public submissions for a team ID
| Name | Required | Description | Default |
|---|---|---|---|
| team_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose that the operation is a read-only list of public submissions, which is useful. However, it does not cover auth requirements, pagination, error behavior, or what happens for an invalid/private team ID.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single front-loaded sentence with no filler. The verb, object, and qualifier are all in the correct order.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read-only list tool, this is barely adequate: an agent can infer the call shape, but with no output schema, no pagination behavior, and no parameter details, there are clear gaps. The description would need at least one more sentence about the response or the team_id to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the schema's team_id description is empty. The description only echoes the parameter name ('team ID') without explaining its format, how to obtain it, or which team scope it refers to. For a low-coverage schema, the description fails to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('List') and resource ('public submissions') and correctly identifies the key parameter ('team ID'). It is clear what the tool does, though it does not explicitly distinguish itself from sibling tools such as list_competition_submissions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus sibling tools such as list_competition_submissions, nor any mention of prerequisites or exclusions. The only contextual signal is 'public,' but no alternative is named.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get model details
| Name | Required | Description | Default |
|---|---|---|---|
| model_ref | Yes |
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.
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.
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.
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.
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.
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.
Preview first N cells of a notebook
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| max_cells | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosure. 'Preview' implies read-only behavior and 'first N cells' clarifies the operational scope, but it does not explicitly state side effects, authentication needs, or what the returned preview contains.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, tight sentence with no filler. The core action and scope are front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations, no output schema, and zero parameter descriptions, this is too thin. It leaves unanswered the meaning of ref, the shape of the preview result, and how this relates to sibling notebook tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It partially explains max_cells by tying it to 'N', but the required ref parameter is completely unexplained, leaving the agent unsure what format or identifier is expected.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('preview'), a clear resource ('notebook'), and a precise scope ('first N cells'). This distinguishes it from sibling tools like pull_notebook, which would retrieve the full notebook, and kernel_output, which concerns execution results.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool instead of alternatives. It does not mention that this is for quick inspection before pulling or executing a notebook, nor does it exclude cases where full notebook content or kernel outputs are needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Download notebook source (.ipynb)
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| output_dir | No | . | |
| with_metadata | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It does not mention the output destination, whether output_dir is honored, how with_metadata changes the download, or any side effects such as file overwriting or directory creation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded phrase with no filler or repetition. It is concise and immediately states the core action, though it is concise at the expense of necessary detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with three parameters, no annotations, and no output schema, this description is incomplete. It omits default behavior, metadata handling, error conditions, and any relationship to sibling kernel tools, so an agent has to guess important invocation details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and all parameter descriptions are empty. The tool description adds no meaning to ref, output_dir, or with_metadata, so an agent cannot determine what ref should contain, where the file is saved, or what toggling with_metadata does.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Download notebook source (.ipynb)' uses a specific verb (Download) and resource (notebook source in .ipynb format). This clearly identifies the tool's function and differentiates it from push_notebook, which uploads, and other display/list tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool versus alternatives such as kernel_files, preview_notebook, or download_dataset. The description only states the action and provides no selection criteria, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Upload notebook and run on free GPU/TPU
| Name | Required | Description | Default |
|---|---|---|---|
| gpu | No | ||
| tpu | No | ||
| name | No | ||
| path | Yes | ||
| private | No | ||
| internet | No | ||
| dataset_sources | No | ||
| timeout_seconds | No | ||
| competition_sources | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full disclosure burden. It reveals the core upload-and-run behavior but not critical operational traits: that this likely submits an asynchronous remote execution, that results require polling via kernel_status/kernel_output, that 'free' hardware implies quota limits, or what error/return behavior to expect. For a tool that triggers remote compute, this is a substantial gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single efficient fragment, front-loaded with the actionable verb. Zero wasted words. It is on the terse side for a tool with 9 undocumented parameters, but the under-specification is a completeness problem, not a conciseness problem — the sentence itself earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters with 0% schema coverage, no annotations, no output schema, and a 70+ sibling toolset that includes pull_notebook, init_kernel, update_kernel, preview_notebook, and kernel_status, this description leaves nearly everything an agent needs unanswered: parameter semantics, the kernel lifecycle, how results are retrieved, and how to choose among sibling tools. Severely incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% — all nine parameter descriptions are empty strings, so the description must compensate. It weakly maps path (notebook to upload) and gpu/tpu (hardware choice) via the phrase 'run on free GPU/TPU', but six parameters (name, private, internet, dataset_sources, competition_sources, timeout_seconds) receive zero explanation anywhere.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource ('Upload notebook') plus the execution context ('run on free GPU/TPU'), which distinguishes it from pull_notebook by transfer direction. It earns a 4 rather than 5 because it doesn't explicitly differentiate from other notebook/kernel siblings like init_kernel, update_kernel, or preview_notebook.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The 'run on free GPU/TPU' phrase implies the use case: push a notebook for cloud execution. However, there is no explicit when-to-use guidance, no exclusions, and no mention of the natural alternatives (pull_notebook for retrieving, kernel_status/kernel_output for inspecting the run). With roughly twenty notebook-related siblings, the routing guidance is only implied, not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search Kaggle datasets
| Name | Required | Description | Default |
|---|---|---|---|
| mine | No | ||
| page | No | ||
| user | No | ||
| limit | No | ||
| query | No | ||
| sort_by | No | hottest | |
| file_type | No | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'Search Kaggle datasets,' which implies a read-only operation but reveals nothing about result format, pagination behavior, sorting semantics, filtering behavior, or whether the search is global or restricted to the user's own datasets.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short, but this is under-specification rather than conciseness. 'Search Kaggle datasets' contains a single phrase and provides no operational detail, so it does not earn its place as a useful tool definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 7-parameter tool with no annotations, no output schema, and no parameter descriptions, this description is severely incomplete. An agent cannot determine how to construct a valid search, what defaults apply, how pagination works, or what the response will contain, so correct invocation is essentially guesswork.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage across 7 parameters, and the description adds no parameter meaning whatsoever. The agent is left completely in the dark about what query, mine, user, page, limit, sort_by, and file_type actually control. A tool with this many parameters requires at least some high-level explanation of their roles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: 'Search Kaggle datasets.' This is specific enough to distinguish it from sibling tools like upload_dataset or delete_dataset, but it does not explicitly differentiate it from list_my_datasets or list_dataset_files, which are related listing operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidance is provided. The description does not explain when to use search_datasets versus sibling tools such as list_my_datasets, dataset_details, or list_dataset_files, nor does it mention any prerequisites or search scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search public Kaggle notebooks/kernels
| Name | Required | Description | Default |
|---|---|---|---|
| user | No | ||
| limit | No | ||
| query | Yes | ||
| dataset | No | ||
| sort_by | No | hotness | |
| language | No | all | |
| competition | No | ||
| kernel_type | No | all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. 'Search' implies a read-only lookup and 'public' clarifies scope, but the description does not mention sorting, filtering, pagination, or what the response contains. This is minimal but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler or repetition. It is appropriately brief for stating the core purpose, though it is so terse that it omits important guidance that other dimensions require.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With eight parameters, zero schema descriptions, no annotations, and no output schema, the description is far too thin to support correct invocation. It provides only the basic purpose and leaves filtering, sorting, result shape, and usage trade-offs completely unspecified.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for the eight undocumented parameters, but it mentions none of them. The agent gets no help understanding query, user, dataset, limit, sort_by, language, competition, or kernel_type beyond their raw names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('Search') and a clear resource ('public Kaggle notebooks/kernels'). The word 'public' and the resource type distinguish it from siblings like search_datasets and list_my_kernels, so an agent can tell this tool apart without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context by specifying 'public' kernels, but it does not explicitly say when to prefer this tool over alternatives such as search_datasets or list_my_kernels. There is no when-not-to-use guidance or named alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show competition topic thread
| Name | Required | Description | Default |
|---|---|---|---|
| topic_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations available, the description carries the full burden of disclosure, but it only says 'Show'. It does not state whether this is a read-only operation, what the response contains, what topic_ref identifies, or how failures are surfaced.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded, but it is under-specified rather than efficiently complete. A second sentence could have clarified the topic_ref format or return value without harming conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with zero parameter documentation, no output schema, and no annotation context, the description is materially incomplete. It does not say how to obtain a valid topic_ref, what the tool returns, or how this relates to the competition topic listing workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one required parameter, topic_ref, with an empty description and 0% schema coverage. The tool description adds no explanation of how to format or resolve topic_ref, leaving the agent without the information needed to populate it correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear action ('show') and resource ('competition topic thread'), and the 'competition' scoping distinguishes it from show_kernel_topic, show_dataset_topic, and show_forum_topic. The term 'thread' is slightly ambiguous relative to 'topic', but the core purpose is recognizable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance about when to use this tool versus list_competition_topics or the other show_*_topic siblings. An agent must infer the intended usage entirely from the tool name and a single verb phrase.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show dataset topic thread
| Name | Required | Description | Default |
|---|---|---|---|
| topic_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. 'Show' implies a read-only operation, which is useful, but the description does not reveal any behavioral traits beyond that: no mention of return format, pagination, whether it includes replies, authentication needs, or error behavior. A single verb is thin transparency for an unannotated tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler words. It efficiently communicates the primary action and resource. However, the conciseness borders on under-specification, sacrificing enough detail that it only partially serves the agent. Still, as a structural matter, it is compact and well-ordered.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter and no output schema, the description should at least explain what topic_ref is and how it relates to a dataset topic. It does neither. The description is insufficient for an agent to confidently construct a valid call, especially given the existence of similar topic tools for other resource types. More context is needed about the expected value of topic_ref and what 'thread' includes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the tool description does not mention topic_ref at all. The parameter name 'topic_ref' hints that it is a reference to a topic, but no format, source, or usage guidance is provided. With zero coverage in both schema and description, the agent has no semantic grounding for the sole required parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Show' a 'dataset topic thread'. The dataset qualifier distinguishes it from sibling tools like show_kernel_topic, show_competition_topic, and show_forum_topic. However, it does not clarify what 'topic thread' entails (e.g., replies, metadata), leaving mild ambiguity about the exact scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. For instance, there is no mention of how it differs from list_dataset_topics, show_kernel_topic, or show_forum_topic, nor when one should be preferred. The agent must infer usage entirely from the tool name and one-line description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show forum topic thread
| Name | Required | Description | Default |
|---|---|---|---|
| topic_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only repeats the show/retrieve intent; it does not mention whether output includes replies, metadata, pagination, or any access requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only four words and contains no fluff, but it is under-specification rather than disciplined conciseness. It adds almost no value beyond the tool name.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read tool this could still be minimally workable, but with no output schema and no parameter description the agent cannot reliably know what input to pass or what result to expect.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage and the tool description adds nothing about topic_ref beyond implying it identifies a forum topic. The agent is not told the expected format (ID, slug) or how to obtain the reference.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb and resource: show a specific forum topic thread. It distinguishes the tool from sibling list/show tools by resource type (forum vs kernel, dataset, competition), though 'show' is generic and the scope of 'thread' is not detailed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given about when to use this tool instead of list_forum_topics or the other show_*_topic tools. There are no exclusions, prerequisites, or alternative routing instructions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show a kernel discussion topic
| Name | Required | Description | Default |
|---|---|---|---|
| topic_ref | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full disclosure burden, but it only implies a read operation through the word 'show'. It discloses nothing about the response format, error behavior for invalid topic_ref values, whether the full discussion is returned, or any prerequisites. It does not contradict any annotations, but it adds almost no behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At five words, the description is maximally concise and front-loaded with no filler. However, the brevity comes at the cost of substantive content that other dimensions penalize, so it is efficient in structure but under-informative in practice.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one required parameter, no output schema, and no annotations, the description is insufficiently complete. An agent knows only that it should 'show a kernel discussion topic' but has no idea what the response will contain, what topic_ref should look like, or how this fits into the flow of kernel discussion workflows.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% — the topic_ref property's description is an empty string. The tool description does not compensate in any way: it never mentions topic_ref, its format (ID, slug, URL, or numeric), or how an agent can obtain a valid value. The agent must guess the parameter's semantics entirely from its name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('show') and resource ('kernel discussion topic'), clearly identifying this as single-topic retrieval rather than a list operation. The 'kernel' qualifier implicitly separates it from show_dataset_topic, show_competition_topic, and show_forum_topic, but differentiation is implicit via naming, not explicit, and it is ambiguous whether 'show' returns the topic metadata, the full thread, or comments.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is given on when to use this tool versus alternatives. There is no mention of list_kernel_topics as the way to obtain a topic_ref, no exclusion of the other show_*_topic siblings, and no context about which resource type this applies to beyond the name itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Submit predictions file or kernel to a competition
| Name | Required | Description | Default |
|---|---|---|---|
| comp | Yes | ||
| file | No | ||
| kernel | No | ||
| message | No | Submission via Kaggle MCP | |
| kernel_version | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It indicates a submission action but does not disclose side effects such as creating a submission record, overwriting previous submissions, quota/rate limits, or required permissions. This is a significant transparency gap for a mutation-like tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no redundant wording. Every word contributes to the core purpose, and the two submission modes are stated economically.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given five parameters, no annotations, no output schema, and fully empty schema descriptions, a one-line description is far from adequate. The agent is left without information about required inputs, parameter semantics, behavior, or return values, making correct invocation unlikely.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description only hints at the meanings of 'file' and 'kernel' ('predictions file or kernel'). It does not explain 'comp,' 'message,' 'kernel_version,' or the relationship and trade-offs between the file and kernel parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action—submitting predictions—and the target resource (a competition). It also distinguishes between file and kernel submission modes, which helps differentiate it from the many list/download competition tools in the sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus alternatives, nor does it mention prerequisites or exclusions. The intended usage is only implied by the verb 'submit,' not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Make kernel public or private
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| make_public | No |
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. It only restates the high-level effect and does not mention whether the change is reversible, immediate, permission-restricted, or how the boolean parameter controls the behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler and the core action is front-loaded. However, it is somewhat under-sized given the empty parameter descriptions and missing usage guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no annotations, no output schema, and completely empty parameter descriptions, this one-line description leaves essential invocation details unexplained, especially the meaning of the required ref. It is functional as a high-level summary but insufficient as complete tool documentation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It loosely implies that make_public determines public versus private visibility, but it leaves the required ref parameter completely unexplained and adds no meaning beyond what the parameter names already suggest.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Make') on a specific resource ('kernel') and the two outcomes ('public or private'). It is specific enough to distinguish the tool from read-only sibling tools like kernel_status, though it does not explicitly contrast it with the broader update_kernel operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as update_kernel or other kernel management tools. The intended use case is implied by the name, but no conditions, prerequisites, or exclusions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create new dataset version
| Name | Required | Description | Default |
|---|---|---|---|
| dir_path | Yes | ||
| version_notes | No | Updated | |
| delete_old_versions | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description bears the full responsibility for disclosing behavioral traits. It states that a new version is created, which implies a write operation, but it does not mention that old versions can be deleted via delete_old_versions, nor any permissions, side effects, or response behavior. The description is not misleading, but it is significantly under-transparent for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief and front-loaded, but it is under-specified rather than appropriately concise. A single generic phrase does not convey enough information to offset the missing schema descriptions and annotations. It reads more like a label than a useful tool description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and three undocumented parameters, the description is far too minimal to be complete. It does not explain prerequisites, optional behaviors, return values, or the role of the required dir_path parameter. An agent would struggle to invoke this tool correctly based on the provided information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description provides no parameter-level meaning. It does not explain dir_path, version_notes, or delete_old_versions, nor how these parameters affect the action. The description therefore fails almost entirely to compensate for the blank schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses an active verb and specific resource ('new dataset version'), which clearly identifies the core action. It does not explicitly distinguish itself from siblings like init_dataset or upload_dataset, but the concept of a new version implies updating an existing dataset. This is a clear, non-tautological statement, though it lacks explicit differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives such as init_dataset or upload_dataset. It does not state prerequisites, exclusions, or conditions under which this tool is preferred. The only implicit cue is 'new version', which weakly suggests an existing dataset, but this is not actionable guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update kernel metadata via pull+push
| Name | Required | Description | Default |
|---|---|---|---|
| ref | Yes | ||
| enable_gpu | Yes | ||
| enable_tpu | Yes | ||
| is_private | Yes | ||
| enable_internet | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. 'via pull+push' hints that the operation fetches and re-uploads the kernel, but it does not say whether this overwrites existing content, creates a new version, requires special permissions, or has other side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded, with no filler words. However, it is so terse that it under-specifies the operation for a tool with five required parameters and no other documentation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations, output schema, and parameter descriptions, this definition is incomplete. An agent cannot know what ref expects, what the return value looks like, or what the pull+push behavior actually changes on the remote kernel.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, and the description only says 'metadata' without naming or explaining any of the five required parameters. While booleans like enable_gpu and is_private are self-explanatory, 'ref' is ambiguous and no format or meaning is provided.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a clear verb ('Update') and a specific resource ('kernel metadata'), so an agent can tell this is a metadata mutation rather than creation or deletion. However, it does not distinguish itself from sibling toggle_kernel_visibility, which also concerns metadata like visibility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no stated when-to-use condition, prerequisite, or alternative. The 'pull+push' hint implies a workflow, but does not explain when this tool should be preferred over push_notebook, toggle_kernel_visibility, or other sibling operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update model metadata from folder
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
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. 'Update' signals mutation, but the description does not state whether metadata is overwritten wholesale or patched, whether the operation is reversible, whether it errors if the model does not exist, or what happens to existing metadata not mentioned. For a mutation tool with zero annotation coverage, this is a substantial gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler and the verb is front-loaded, which is structurally clean. However, it is under-specified rather than efficiently concise — at 8 words it conveys too little to properly orient an agent, especially with empty parameter descriptions and no annotations.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low structural complexity (one parameter, no annotations, no output schema), the bar for completeness is higher, and the description fails to meet it. It omits the meaning of 'from folder', the model-vs-model-instance distinction that the sibling list implies matters, return values, and error behavior. An agent cannot reliably decide to call this tool or format its input correctly from this definition alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% — the 'folder' parameter has an empty description, so the prose must compensate. The phrase 'from folder' hints that the folder is central to the operation, but it does not explain what the folder should contain (e.g., a config file, metadata fields, a model ID reference), its format, or whether it is a local path or remote reference. The description adds minimal meaning beyond the parameter name itself.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource ('Update model metadata'), so the core action is identifiable. However, 'from folder' is ambiguous — it is unclear whether the folder is the source of metadata values, the location of the model, or the container being read. The description also fails to distinguish this tool from the closely named sibling 'update_model_instance', which is a significant differentiation gap given the large sibling list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives such as create_model, init_model, or update_model_instance. There are no when/when-not conditions, no prerequisites (e.g., must the model already exist?), and no mention of alternatives. The agent must infer usage entirely from the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update model instance metadata from folder
| Name | Required | Description | Default |
|---|---|---|---|
| folder | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavior, but it only states 'update', implying mutation without explaining side effects, scope of metadata changes, or whether existing metadata is overwritten. No information is given about required permissions, failure modes, or what happens if the folder is invalid.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single short sentence with no filler or redundant information. It is easy to parse, though it sacrifices needed detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple mutation tool with one parameter, but the description leaves essential context unclear: what exactly is read from the folder, how the folder path should be provided, and whether this operation is destructive or additive. The lack of annotations or output schema makes this gap more significant.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has one parameter, 'folder', with an empty description and 0% schema coverage. The description mentions 'from folder' but does not clarify what kind of folder, what path format is expected, or how the folder relates to the model instance metadata. This is insufficient compensation for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb and resource: 'Update model instance metadata', which distinguishes it from siblings like update_model and update_dataset. The phrase 'from folder' adds some context, though it is not fully explained.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no guidance on when to use this tool versus alternatives such as update_model or create_model_instance. There is no mention of prerequisites, intended workflows, or situations where a sibling tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a new dataset from a folder
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| public | No | ||
| dir_path | Yes | ||
| subtitle | No | ||
| description | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the full burden of behavioral disclosure. It indicates a create operation but does not explain side effects, whether existing datasets are overwritten, permission requirements, upload behavior, or what happens on failure. This is minimal for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler; the verb and resource appear immediately. It is appropriately concise, though it achieves brevity at the cost of useful detail.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, no output schema, no annotations, and a large sibling toolset, this description is incomplete. It does not convey required path format, optional field behavior, response semantics, or how this differs from similar dataset tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and there are 5 parameters, but the description only hints at the meaning of dir_path through 'from a folder'. It provides no semantics for name, public, subtitle, or description, leaving the agent without meaningful parameter guidance beyond the required field name.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Create') on a clear resource ('a new dataset') and identifies the source ('from a folder'). However, it does not differentiate from sibling tools like init_dataset or update_dataset, which could also be associated with dataset creation or modification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives such as init_dataset or update_dataset. The phrase 'from a folder' implies a use case, but no explicit context, prerequisites, or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v3.0.0benchmark_task_statuscompetition_leaderboardcreate_modelcreate_model_instancecreate_model_versiondataset_detailsdataset_statusdelete_datasetdelete_kerneldelete_modeldelete_model_instancedelete_model_versiondownload_competition_datadownload_competition_episode_logsdownload_competition_replaydownload_datasetdownload_model_versionget_account_infoget_configget_dataset_metadataget_model_instanceget_quotainit_datasetinit_kernelinit_modelinit_model_instancekernel_fileskernel_logskernel_outputkernel_statuslist_benchmark_modelslist_benchmark_taskslist_competition_episodeslist_competition_fileslist_competition_pageslist_competition_submissionslist_competition_topicslist_competitionslist_dataset_fileslist_dataset_topicslist_forum_topicslist_forumslist_kernel_topicslist_model_instance_versionslist_model_instanceslist_model_topicslist_model_version_fileslist_modelslist_my_datasetslist_my_kernelslist_team_submissionsmodel_detailspreview_notebookpull_notebookpush_notebooksearch_datasetssearch_kernelsshow_competition_topicshow_dataset_topicshow_forum_topicshow_kernel_topicsubmit_competitiontoggle_kernel_visibilityupdate_datasetupdate_kernelupdate_modelupdate_model_instanceupload_datasetScored across 68 tools
The verb_noun structure separates most actions, but several pairs blur together: kernel_files/kernel_output, dataset_details/get_dataset_metadata, model_details/get_model_instance, and get_quota/get_account_info all require close reading. The many model/instance/version lifecycle variants are easy to misselect without careful attention to the resource level.
Tool names mostly follow a consistent snake_case verb_noun convention (list_, create_, update_, delete_, download_, init_). Minor inconsistencies exist: kernel and notebook are used interchangeably for the same resource, competition_leaderboard and dataset_details lack a verb, and update_dataset means 'new version' while update_model means metadata-only update.
68 tools is far beyond a reasonable MCP surface and exceeds the 50+ threshold for an extreme count. While the tools cover distinct Kaggle domains, the server would be much more usable split into separate dataset, kernel, competition, and model servers.
The tool surface is exhaustive across Kaggle's main workflows: dataset, kernel, and model CRUD/lifecycle operations, competition submission flows, discussion forums, and account/quota information. Core workflows have no obvious dead ends, and the high count is largely due to genuinely broad domain coverage.
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