kaggle-mcp

Server Quality Checklist

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Latest release: v0.4.2
Disambiguation5/5
Each tool targets a distinct resource or action, with detailed descriptions that clearly differentiate them. For example, kaggle_get_competition, kaggle_competition_landscape, and kaggle_competition_leaderboard serve different purposes (details, triage, leaderboard). There is no ambiguity.
Naming Consistency4/5
All tools share the kaggle_ prefix and use underscore-separated names. Most follow a verb_noun pattern (e.g., kaggle_get_competition, kaggle_list_datasets), but some are noun-first (e.g., kaggle_competition_landscape, kaggle_competition_leaderboard). This minor inconsistency does not hinder readability.
Tool Count3/5
With 41 tools, the server covers a broad Kaggle domain (competitions, datasets, kernels, discussions, models). While each tool has a clear purpose, the count is at the high end considering the scope. A more streamlined set could reduce cognitive load.
Completeness5/5
The tool surface is remarkably complete for Kaggle workflows, covering competition lifecycle (list, get, download, EDA, submit, track), dataset management (search, create, version, delete), kernels (push/pull/status/output), discussions (search/get), models (list/download/delete), and admin (auth, audit, status). Obvious gaps like discussion posting are due to API limitations.
Average 4/5 across 41 of 41 tools scored. Lowest: 2.4/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 20 commits in the last 12 weeks
- Last stable release on June 16, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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{
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  ]
}
```
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'Read-only fetch,' but the annotation readOnlyHint is false, indicating a contradiction. Additionally, while it mentions 'Log tail is untrusted-wrapped and truncated,' this positive detail is undermined by the inconsistency with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at two sentences, front-loading the main action. It could be slightly more efficient by merging the second sentence, but overall it's well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, the description should clarify what is returned beyond the generic 'output files and logs.' It lacks details on return structure and pagination, leaving gaps for a tool with no schema descriptions.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It does not describe the 'kernel' or 'file_pattern' parameters, leaving the agent without guidance on how to specify them.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it downloads a completed kernel's output files and logs, and specifies it's a read-only fetch. This distinguishes it from sibling tools like kaggle_push_kernel or kaggle_kernel_status. However, it could be more specific about the exact output included.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide guidance on when to use this tool vs alternatives, nor when not to use it. It only states it downloads from a completed kernel, but lacks explicit context for selection among many sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds 'poll' and 'processing status', which provides some behavioral context beyond annotations, but does not detail polling frequency or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences, front-loading the purpose. However, it could be slightly more structured by including parameter hints, but remains minimally adequate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema, the description does not need to explain return values, but it omits guidance on interpreting status, error scenarios, and further actions. The description is incomplete for a polling tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'dataset' has no description in the schema (0% coverage), and the description fails to add any context about its format (e.g., slug or ID). The description does not compensate for the missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'poll' and clearly identifies the resource as 'processing status of a dataset create/version op', effectively differentiating from sibling tools like kaggle_get_dataset_metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use after create or version operations but does not explicitly state when to use or not use this tool, nor mention alternatives. No exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. Description adds the polling behavior and status values, which is helpful but doesn't disclose details like rate limits or side effects. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single concise sentence that front-loads the purpose and enumerates possible statuses. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Output schema exists, so return values are documented externally. Description explains polling behavior and statuses, but lacks details on polling intervals, error handling, or example usage. Adequate but minimal.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has one parameter 'kernel' with no description, and schema coverage is 0%. The description does not clarify what the kernel parameter represents (e.g., ID, name), adding no meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'poll', the resource 'kernel run status', and enumerates possible statuses (running, complete, error, cancelAcknowledged). It distinguishes well from sibling tools like kaggle_kernel_output by focusing on status polling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives (e.g., when to stop polling, or handling timeouts). Implies polling context but lacks explicit when/when-not rules.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description states 'Read-only,' which aligns with annotations (readOnlyHint true, destructiveHint false). It adds no further behavioral context beyond what annotations already provide, but does not contradict them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Efficiently conveys the action and read-only nature.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a simple list operation with 2 parameters and an output schema, the description is adequate but minimal. It covers the basic purpose and return data (status and scores) but omits parameter details and usage context (e.g., scoping to a competition).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description does not explain any parameters. It does not clarify that competition is required or that limit controls count. The description fails to compensate for the missing schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it lists submission history with status and scores, using a specific verb and resource. This distinguishes it from siblings like kaggle_get_submission_score (specific score) and kaggle_preview_submission (preview).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention exclusions or context for usage, leaving the agent to infer from the tool name and siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds that the deletion (if later executed) is irreversible, but also states 'no side effects', aligning with readOnlyHint=true and destructiveHint=false. However, it does not disclose the token's role or persistence. The description is clear but lacks depth on behavioral implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the core action and outcome. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a single required parameter and output schema present, the description explains the basic purpose but does not elaborate on the confirm_token usage or parameter semantics. Adequate but leaves gaps for a new user.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'model' is not described in the description. Schema coverage is 0%, so description must compensate but fails to provide any details about what value to pass or its format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool issues a confirm_token for an irreversible model delete with no side effects. It distinguishes the tool from sibling kaggle_delete_model by indicating this is a preview/confirmation step.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like kaggle_delete_model. The description does not explain the workflow (e.g., use this to get a token before performing a delete) 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds useful behavioral traits: returns a compact table instead of raw JSON, paginated, and limits to top ~10 results. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loads the core action and output format, and contains no unnecessary words. It is highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, 0% schema coverage, and an output schema, the description provides essential output details (columns) but lacks parameter documentation and guidance on when to use this tool relative to siblings. It is adequate but has clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It only implicitly references search and pagination (top ~10) but does not detail the purpose of page, sort_by, file_type, or page_size. The description falls short of compensating for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches datasets and returns a compact ranked table with specific columns, which is a specific verb+resource. However, it does not explicitly distinguish from sibling search tools like kaggle_search_discussions, so differentiation is implicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as when to prefer this over kaggle_get_dataset_metadata or other dataset tools. No exclusions or context are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that data will become world-visible, which is a key behavioral outcome beyond annotations. Aligns with readOnlyHint=true and destructiveHint=false by stating no side effects, but does not explain the token mechanism.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler. Front-loaded with the primary action and key behavioral fact. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter and an output schema, so the description covers the core purpose and a critical outcome. However, the lack of parameter documentation leaves a gap in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single required parameter 'folder' is completely undocumented in the description. With 0% schema description coverage, the description must compensate but fails to add any meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action: issuing a confirm_token to publish a dataset publicly. It clearly distinguishes from sibling tools like create, version, delete, and preview_delete by focusing on the publish preview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives. Does not mention prerequisites, context (e.g., use before actual publish), or exclusions among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context beyond annotations by explaining the cap (top-N, default 20) and the rationale ('to protect context'). Annotations already indicate read-only and non-destructive behavior, so the description complements that without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently communicates the core purpose and a key constraint. No unnecessary words, and the most critical information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, 1 required), the description is mostly adequate but lacks explanation of the required 'competition' parameter. The existence of an output schema mitigates the need for return value details, but parameter unclarity reduces completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 mentions the top_n parameter with its default (20) but does not explain the required 'competition' parameter or any format/range. The competition parameter is completely unspecified.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it returns the public leaderboard capped to top-N (default 20). The verb is implicit but the resource is well-defined. It does not explicitly differentiate from siblings like kaggle_competition_landscape or kaggle_leaderboard_track, but the cap is a distinguishing feature.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for retrieving a limited leaderboard to avoid large context, but does not explicitly state when to use it over alternatives (e.g., for quick snapshots vs. full landscape view). No exclusions or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral details: output is a 'compact table' and supports pagination. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise (three short phrases) and front-loaded with the primary action. Every word adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 7 parameters with 0% schema description coverage, the description should explain key parameters like 'search', 'page', 'sort_by'. It only mentions pagination generally. The output schema exists but is not detailed; the description does not clarify return structure beyond 'compact table'.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% with 7 parameters, but the description adds no information about any parameter (e.g., search, page, language). The AI must infer usage solely from parameter names and types, which is insufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'List/search' and resource 'notebooks (kernels)', specifies output format 'compact table', and notes pagination. It is distinct from sibling tools like 'kaggle_pull_kernel' which retrieve specific kernels.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for listing/searching kernels but does not explicitly state when to use this over alternatives. No when-not-to-use guidance is provided, though sibling names give some context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and destructiveHint=false, and the description does not contradict them. It adds behavioral context by describing the output as a 'single decision-ready artifact' rather than raw endpoint output, which provides useful insight into what the tool produces.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, using two sentences to convey purpose, output format, sorting, and differentiation from siblings. It is front-loaded with the most important information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description provides a clear overview of what the tool returns and its use case, but it omits any guidance on parameters. Since the tool has four optional parameters that control output (limit, search, category, include_ended), the description is incomplete for effective usage without schema inspection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, and the description does not mention any of the four parameters (limit, search, category, include_ended). Since there are no enums and no param descriptions in the schema, the description fails to add meaning to the parameters, leaving the agent unaware of filtering or limiting capabilities.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly defines the tool as a 'pre-digested triage report' of competitions, listing specific fields (slug, prize, evaluation metric, deadline, days_left) and states it is sorted by soonest deadline. It contrasts with other Kaggle MCPs that return raw output, making its purpose distinct.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description indicates using the tool to decide which competitions to enter at a glance, implying a quick overview use case. It explicitly contrasts with other Kaggle MCPs, giving a sense of when to use this versus alternatives. However, it does not explicitly state 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds 'paginated' and specifics of the output columns (compact ranked table), which provides some additional context but not deep behavioral detail.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is front-loaded with the action and resource, followed by details and constraints. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the existence of an output schema, the description could be minimal, but it lacks details on parameter usage and pagination behavior. The agent may need more context to use the 6 optional parameters effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should explain parameters, but it does not mention page, group, search, sort_by, category, or page_size at all. The default values and titles in the schema are insufficient for an agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('list' and 'triage') and resource ('competitions'), and distinguishes from siblings like kaggle_get_competition by emphasizing a compact ranked table overview.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Clear that it's for browsing/listing competitions (paginated, read-only), but does not explicitly state when to use it versus alternatives like kaggle_competition_landscape.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so description adds value by mentioning 'Paginated' and 'Read-only', which clarifies output format and safety. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded with key information: purpose, output format, pagination, and safety. No unnecessary words. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters with 0% schema coverage and no parameter descriptions in the description, the tool's interface is underdocumented. However, the core functionality is clear. Adequate but incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, but description only mentions 'list/search' and 'paginated' without explaining any of the five parameters (page, owner, search, sort_by, page_size). Fails to compensate for lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'List/search published models as a compact table', specifying the action and resource. It distinguishes from siblings like 'kaggle_get_model' which gets a single model, and 'kaggle_download_model' which downloads.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for listing and searching models but provides no explicit guidance on when to use instead of alternatives like 'kaggle_get_model' or 'kaggle_search_datasets'. No exclusions or when-not-to-use mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint=false, destructiveHint=false, openWorldHint=true) are present; description adds context about zip-slip guard, isolated work directory, and that return is metadata not contents. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with critical information, no unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers main functionality, security guard, and precondition. Output schema likely defines return structure. Lacks detail on parameter functionality but overall sufficient for a simple download tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%. The description does not explain any parameter meaning (e.g., 'file' for specific file download, 'unzip' controls extraction). Fails to add value beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool downloads competition data into an isolated work directory with auto-extraction and a zip-slip guard. It distinguishes from siblings like kaggle_download_dataset (different resource) and kaggle_submit_to_competition (different action).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly mentions a key precondition: requires competition rules accepted (403 otherwise). Does not explicitly state when to use alternatives, but the requirement and return behavior provide implicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds the valuable context that the tool issues a confirm_token and has no side effects, aligning with the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core purpose and side-effect nature, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter, output schema exists, and annotations are provided, the description covers the main function. However, it could mention that the confirm_token is returned or how to use it with the delete tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description does not add any meaning to the 'dataset' parameter beyond the schema, failing to compensate for the lack of parameter documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool issues a confirm_token for an irreversible dataset delete, and explicitly says 'No side effects,' which distinguishes it from the actual delete tool kaggle_delete_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage as a preparatory step before deletion and notes it is safe, but it does not explicitly state when to use it versus alternatives (e.g., kaggle_delete_dataset) or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description explicitly states 'Async' and 'non-destructive (adds a version)', adding value beyond annotations (which only indicate destructiveHint=false). This gives the agent clear expectations about execution and 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two well-structured sentences with no redundancy. The main action is front-loaded, and key traits (async, non-destructive) are appended concisely. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having an output schema and annotations, the description fails to explain parameters, prerequisites (dataset must exist), or return value. For a tool with three parameters, this is incomplete for correct usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds no meaning to the three parameters (folder, version_notes, delete_old_versions). The agent must infer usage from names alone, which is insufficient for proper invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Push a new version/revision', identifies the resource as an existing dataset, and notes the persistence of engineered features. This clearly distinguishes from sibling tools like kaggle_create_dataset (which creates new datasets) and kaggle_delete_dataset (destructive).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states when to use (push a new version) and notes the async, non-destructive behavior. While it does not explicitly exclude alternatives, the context of versioning an existing dataset is clear. A small deduction for lacking explicit when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds critical behavioral details: messages are fenced as <untrusted-content> and truncated, which annotations do not cover.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. Every sentence adds value: purpose, content format, and safety hint. Front-loaded with the core action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 params, read-only), an output schema exists, and the description covers purpose, format, and safety, it is fully adequate for correct selection and invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description does not explain the parameters (topic_id, max_messages) beyond their names and default. No value added over the schema, so it fails to compensate for the poor schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Read a discussion topic's messages', specifying a concrete verb and resource. It distinguishes from siblings like kaggle_search_discussions and kaggle_get_competition. The mention of 'fenced' and 'truncated' adds precision.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says 'READ-ONLY — no reply/comment/vote', guiding the agent not to use this for write operations. However, it does not explicitly compare to search or other alternatives, so some guidance is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and destructiveHint=false, indicating no side effects. The description adds behavioral details such as validation and budget reporting, which are not in the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the key action, and every sentence adds value. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is fairly complete given the tool's simplicity and the presence of an output schema. However, it lacks explanation for the 'message' parameter, which may be confusing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must explain parameters. It only mentions file_path implicitly through validation, but does not explain competition or message parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is a dry-run submission, validates file existence/size, reports budget impact, and returns a confirm_token. It distinguishes itself from the sibling kaggle_submit_to_competition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool should be used before kaggle_submit_to_competition to obtain the required confirm_token. It does not explicitly state when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context: it is append-only, lists specific actions, mentions redaction, and notes inclusion of timestamps and budget. This goes beyond the annotations to detail what the tool exposes.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the core purpose and value. Every part adds meaning; no extraneous words. It is highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (one optional parameter, output schema exists), the description covers the main purpose, returned fields, and unique value. It lacks explanation of the limit parameter but otherwise provides adequate context for an agent to decide when and how to use it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter (limit) with 0% description coverage. The description does not mention limit or its effect—whether it controls number of entries returned. With no parameter explanation, the agent must guess from the name and default, which is insufficient for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool returns an append-only ledger of mutating actions (submit/create/version/delete) with timestamps and submission budget, and explicitly distinguishes it from sibling tools by noting no other Kaggle MCP tracks tokens or budgets. The title 'Audit log of mutating actions' reinforces the purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for inspecting safety machinery, stating 'Makes the safety machinery inspectable, which no other Kaggle MCP can offer.' This gives clear context, though it does not explicitly state when not to use or provide alternative tools for non-audit needs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint: false, destructiveHint: false, openWorldHint: true) are consistent; description adds context about data download and EDA automation. It explains what the bundle does beyond annotations without contradiction, though it doesn't detail side effects like API costs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Description is a single long sentence that front-loads the main purpose. It could be slightly more structured (e.g., bullet points) for clarity, but it is concise and avoids redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (multiple steps), the description covers the main actions and output (baseline plan, budget). Output schema exists but is not seen; description gives enough context. Lacks details on error handling or prerequisites beyond rule acceptance.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has only one required 'competition' parameter with 0% description coverage. The description implicitly uses the parameter but does not add meaning beyond the name (e.g., format, validation). Baseline for low coverage expects compensation, but description is minimal on parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses specific actions ('fetch', 'download', 'run EDA', 'return plan') tied to the resource 'competition kickoff'. It clearly distinguishes from siblings by claiming 'no other Kaggle MCP does this', justifying its unique bundled approach.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states it is a 'one-call competition kickoff' for initial setup, implying use over separate tools like kaggle_download_competition_files or kaggle_eda_competition. However, it lacks explicit when-not-to-use or exclusions (e.g., if parts are needed separately).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes behavior as safe, capped at 50 rows, width-limited, and untrusted content. Annotations show readOnlyHint false, which could imply a write operation, but description's 'safe preview' suggests read. No strong contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences front-load key information. Could improve structure slightly but is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With output schema present, return values need not be described. Tool is simple and description covers core behavior and constraints. Some param details missing but acceptable for a preview tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so description must compensate. It explains the 'n' parameter (max 50 rows) but does not explain 'dataset' or 'file'. Meaning of 'file' parameter is omitted.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool previews first N rows of a dataset CSV with headers, dtypes, and capped rows. It addresses the user need 'What does the data look like?' and distinguishes from siblings like kaggle_download_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Description implies usage for quick previews without full download, using phrases like 'safe first-N-rows preview' and 'without dumping the whole file'. Context is clear but lacks explicit when-not-to-use or alternative tool names.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark destructiveHint=true, but the description adds crucial context: irreversibility, requirement for explicit config and confirm_token. This goes beyond annotations, though it could detail more about consequences like deletion of associated resources.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. The first sentence states purpose and irreversibility; the second explains prerequisites. Efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the destructive nature and special config, the description covers key aspects: operation, irreversibility, config flag, token requirement. Output schema exists, so return values are not needed. Lacking parameter format explanation holds it back from a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description should explain parameter details. It only mentions confirm_token as a requirement but does not clarify what 'dataset' expects (e.g., name or ID format). The agent has insufficient guidance to fill parameters correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool deletes a dataset and all its versions irreversibly. It uses strong, specific language that distinguishes it from sibling tools like kaggle_create_dataset or kaggle_download_dataset.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states the tool is disabled unless the server is started with KAGGLE_MCP_ENABLE_DESTRUCTIVE=1 and requires a confirm_token, never defaulting to yes. This provides clear when-to-use and prerequisites, though it doesn't mention alternatives like preview_delete_dataset.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide readOnlyHint=false and destructiveHint=false, but the description adds valuable behavioral context: 'isolated work dir', 'zip auto-extracted with a zip-slip guard', and 'Returns local paths + metadata'. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the essential action and key details. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema (not shown), the description covers the main purpose and safety behavior. However, it omits details about the isolated work dir's lifetime and cleanup, which could be relevant for usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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 hints at 'single file' (file param) and 'zip auto-extracted' (unzip param), but does not explicitly explain each parameter's purpose or constraints. Minimal meaning added beyond schema field names.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Download', the resource 'dataset (or single file)', and key actions like auto-extraction and zip-slip guard, distinguishing it from sibling tools like kaggle_download_competition_files.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for downloading datasets but does not explicitly state when to use this tool versus alternatives like kaggle_download_competition_files or kaggle_download_model. No exclusions or when-not guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=false and destructiveHint=false. The description adds valuable behavioral context: the summary is computed locally with pandas and raw rows are not streamed. This clarifies the scope and side-effect-free nature beyond what annotations offer.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with three sentences, each adding value. It front-loads the main action and output, then adds a key behavioral note and contextual remark. No extraneous information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters (with no schema descriptions) but an output schema exists, the description adequately covers the output nature. However, it lacks details on error handling, prerequisites (e.g., authentication), or how the parameters interact. Slight gaps in completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must explain parameters. It only explains the 'dataset' parameter as 'owner/slug'. The other three parameters (file, target, max_files) are not described, leaving their meaning and usage unclear. The description could have compensated by linking target to the mentioned 'target distribution' or file to multi-file datasets.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool downloads a dataset and returns a compact exploratory summary with specific elements (shape, dtypes, missingness, target distribution, top numeric correlations). It distinguishes itself from sibling tools by noting that most Kaggle MCP servers lack this primitive, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for initial data understanding ('find data -> understand it') and explicitly states it never streams raw rows. However, it does not explicitly mention when to use this over alternatives like kaggle_get_dataset_metadata or kaggle_dataset_preview, leaving some ambiguity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true and destructiveHint=false. The description adds useful behavioral detail: 'Description text is untrusted-wrapped and truncated,' which warns about data quality. This goes beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences with the purpose front-loaded. Every word earns its place; no redundancy or fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple metadata-fetch tool with one parameter, existing annotations, and an output schema, the description covers purpose, parameter format, and a key behavioral caveat (truncation). No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema coverage, the description must explain the parameter. It does so clearly: '`dataset` is 'owner/slug'.' This clarifies the required format, adding meaning beyond the schema's string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Fetch a dataset's metadata + file listing.' This specifies the verb-resource pair and differentiates from siblings like download or search. It also explains the dataset parameter format as 'owner/slug'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool vs. alternatives like kaggle_search_datasets or kaggle_download_dataset. It only states the tool's purpose, leaving the agent without contextual decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Explicitly states 'Read-only and untrusted-fenced', adding behavioral context beyond annotations (readOnlyHint, openWorldHint). Annotations already cover safety, so this adds value without contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise at two sentences, packed with purpose, context, and action. No wasted words; front-loaded with the core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, behavioral traits, and next steps. However, lacks parameter explanations, which are important given zero schema coverage. Output schema presence mitigates return value ambiguity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage for parameters (search, sort_by, page_size). The description does not explain any parameter meaning or usage, relying entirely on parameter names. This is a significant gap for an agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches for competition solution write-ups, explaining what they are and distinguishing from general discussions. It specifies the source as the 'competition_write_ups' category and pairs with a specific prompt.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Describes when to use (for strategy research via write-ups) and how to proceed (drill into one with kaggle_get_discussion). Implicitly differentiates from sibling tools like kaggle_search_discussions, though no explicit 'when not to use' is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds behavioral detail beyond annotations: download action, auto-inference of target, and error 403 if rules not accepted. No contradiction with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no filler: first explains core functionality and outputs, second states critical precondition. Information is front-loaded and efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the presence of an output schema, description adequately covers purpose, outputs, and a precondition. Could elaborate on parameter effects but sufficient for agent to understand tool scope.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and description does not explicitly describe individual parameters. It implies use of 'competition' and 'max_files', and mentions 'target' auto-inference, but lacks full parameter details.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it downloads competition data and returns an EDA digest with train-test diff, specifying 'pre-first-submission view' and differentiating from other Kaggle MCP tools by mentioning the unique orientation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Describes the context for use (before first submission) and a precondition (rules accepted), but does not explicitly exclude alternative tools or provide when-not-to-use scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds that it fetches details and a truncated rules digest, and explicitly states 'Read-only.' This aligns with annotations and provides additional behavioral context beyond what annotations offer.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single succinct sentence with no redundant words. It front-loads the action and result, and the clarification about the slug is efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has a single required parameter and an output schema is present (not shown but indicated), the description adequately covers what the tool does and what it returns at a high level. It could mention the output schema details, but not required.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates by explaining the parameter: 'competition' is the bare slug (e.g., 'titanic'). This adds crucial format and example information that the schema lacks, helping the agent form the correct slug.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Fetch one competition's details and a truncated, untrusted-wrapped rules digest.' This specifies the verb (Fetch), resource (competition), and distinguishes from sibling tools like kaggle_list_competitions which lists all competitions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for a single competition by emphasizing the bare slug parameter, but it does not explicitly state when to use this tool versus alternatives like kaggle_list_competitions or kaggle_competition_leaderboard.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, open-world, non-destructive. The description adds valuable behavioral details: bounded polling with backoff, not a hot loop, and that it returns the public score and closes the submit loop. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, two sentences, with no wasted words. It front-loads the core action and includes key qualifiers (bounded polling, backoff) efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, return values are handled. The description covers polling behavior, backoff, and loop closure. Minor gaps: it doesn't address error conditions (e.g., no newest submission) or edge cases, but overall sufficient for a poll tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description should compensate. While it does not explicitly describe the parameters, the names (timeout_s, interval_s) and defaults are self-explanatory in the context of polling. The description implies their role but lacks explicit detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool polls the newest submission until scoring completes and returns its public score. It specifies it 'closes the submit loop,' distinguishing it from siblings like kaggle_submission_best_score (which returns best score) and kaggle_list_submissions (which lists submissions).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is used after submission to get the score, as it 'closes the submit loop.' It also mentions bounded polling with backoff and explicitly warns against hot loops. However, it does not name specific alternative tools or state 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the tool is safe and non-destructive. The description adds valuable behavioral context: the tool is stateful (stores snapshots locally), requires sequential calls to compute meaningful deltas, and only works on the public leaderboard. This goes beyond the annotations by explaining the stateful, snapshot-based behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is only two sentences, yet it conveys the purpose, unique value, limitations, and output details. It is front-loaded with the core functionality and uses efficient phrasing. Every sentence contributes essential information without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low parameter count, existence of an output schema, and annotations covering safety, the description is fairly complete. It explains what the output contains (rank deltas, new entrants, etc.) but does not detail how snapshots are managed or how many snapshots can be stored. The stateful nature is mentioned, but the agent might need additional hints about persistence or resetting.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0% description coverage, so the description must compensate. It explains the role of 'top_n' and 'your_team' explicitly (e.g., 'your movement + who passed you'), but does not fully describe the format or constraints (e.g., top_n default is 20, your_team is an optional string). The description adds some meaning beyond the schema but leaves gaps for the agent to infer.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: snapshot the public top-N leaderboard and compute deltas relative to the last snapshot. It explains what the output includes (rank deltas, new entrants, climbers, your team movement). It also differentiates the tool from a simple leaderboard query by noting the stateful tracking and lack of historical endpoint on Kaggle.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description specifies when to use this tool: for tracking leaderboard changes over time using snapshots. It explicitly states that it only works for the public leaderboard and that deltas are against your previous snapshot, not an absolute time series. However, it does not explicitly exclude use cases where an absolute time series or private leaderboard data is needed, nor does it name sibling alternatives like kaggle_competition_leaderboard directly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds concrete behavioral details about what is checked (per-competition budgets, safety switches, work-dir), providing valuable context beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: two lines, front-loaded with 'Health check:', lists items without any filler. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given zero parameters and an existing output schema, the description adequately covers the tool's behavior. It mentions all key outputs (credentials, budgets, switches, work-dir) and is sufficiently complete for a status check tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0 parameters, the schema coverage is 100%. The description does not need to explain parameters, and the baseline of 4 is appropriate as per rubric.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool is a 'Health check' and lists specific items it checks (credential validity, submission budgets, safety switches, work-dir root), distinguishing it from siblings like kaggle_whoami or kaggle_get_competition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for checking status but does not provide explicit guidance on when to use this tool versus alternatives like kaggle_whoami. No when-not-to or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description goes beyond annotations (readOnlyHint=false, destructiveHint=false) by stating the tool never auto-accepts and that it is a relay for manual action. This provides full transparency about its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three compact sentences, each adding essential information: the API limitation, the tool's action, and a behavioral guarantee. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has an output schema (not shown) and only one parameter, the description covers the core behavior (returns URL, manual action) and key constraint (no auto-accept). It misses potential details like what the URL looks like, but overall adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% coverage (no description for the 'competition' parameter), and the tool description does not add any semantics about the parameter beyond its name. The agent must infer its meaning from context, which is insufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the rules URL and instructs manual acceptance, with a specific verb 'returns' and resource 'rules URL'. It distinguishes itself from other competition tools by explicitly stating there is no API to auto-accept.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description tells the agent to use this tool to get the rules URL and instruct manual acceptance, and explicitly states 'It NEVER auto-accepts.' This provides clear guidelines for when to use it, though it does not explicitly mention when not to use it or compare to siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=false (writes locally) and destructiveHint=false (no remote destruction). The description adds that it downloads to a work dir and returns paths, but does not detail overwrite behavior or permissions. Some behavioral context is provided, but more would be helpful.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states purpose, second defines the parameter format. No extraneous information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (1 parameter, output schema exists), the description covers the core action and parameter format. It could mention default work dir or output structure, but is sufficient for an agent to use correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter 'model_version' has 0% schema description coverage, making the description's format string essential. It provides an exact template ('owner/model/framework/variation/version'), which is highly valuable for correct invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Download' and the target 'specific model instance version's artifacts'. It defines the model_version format precisely, distinguishing this tool from siblings like kaggle_get_model or kaggle_list_models.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for downloading artifacts of a specific model version, but does not explicitly state when to use it versus alternatives (e.g., kaggle_get_model for metadata). However, the purpose is clear enough for an agent to select it correctly.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint and destructiveHint. Description adds context: no corresponding write tools exist, titles are untrusted. No contradiction. Could mention pagination details, but still adds value beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three short sentences, front-loaded with core purpose. Every sentence adds essential information. No fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Covers purpose, output format, read-only nature, security note, and link to detailed view. Missing guidance on pagination behavior despite a page_size parameter, but overall sufficient for a simple search tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Parameter schema has 0% coverage (no descriptions). The description does not elaborate on the meaning or usage of search, sort_by, category, or page_size, leaving the agent to infer from names and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Search/list Kaggle discussion topics' and specifies the return format as a compact table with fields. It distinguishes itself from sibling tools like kaggle_get_discussion and other search tools by noting the lack of write capabilities.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states READ-ONLY and notes that there is no post/reply/vote tool, guiding when (not) to use. Also advises caution about untrusted titles and directs drill-down to kaggle_get_discussion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (which indicate a write operation), the description reveals key traits: async behavior (returns 'queued'), polling requirement, default privacy, environment variable for public, and required metadata file. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Four concise sentences with no fluff. The purpose is front-loaded in the first sentence, followed by essential details in a logical order. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (async, public toggle, metadata validation) and the existence of an output schema, the description covers the main workflow and prerequisites. It could include error handling or validation details, but it is largely complete for an agent to invoke correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must compensate. It explains the `folder` parameter via the local folder and metadata requirement, `public` via default privacy, and `confirm_token` via the public making condition. Each parameter is contextualized, adding significant meaning beyond names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a new dataset from a local folder, specifies default privacy, and includes prerequisites. It distinctly focuses on creation, differentiating it from sibling dataset tools like version or delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied: use for creating new datasets. However, it does not explicitly contrast with alternatives like kaggle_version_dataset for updates, nor does it state when not to use it. The description lacks explicit guidance but is sufficient for basic usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description highlights irreversibility and the need for an environment variable and confirmation token, which adds behavioral context beyond the destructiveHint annotation. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff. Every word adds value: irreversibility, environment variable condition, and token requirement. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that output schema exists and annotations are present, the description covers key behavioral aspects (irreversibility, destructive guard, token prerequisite). It could briefly explain how to obtain the model ID, but overall it is complete enough for a destructive action.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, and the description only mentions confirm_token in context but does not describe the 'model' parameter or its format. This leaves the user guessing how to specify the model, which is insufficient for a 2-parameter tool with no schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action 'Delete a model' and emphasizes 'IRREVERSIBLE', distinguishing it from other tools. The resource (model) is unambiguous, and the irreversibility contrasts with preview or other non-destructive tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states that the tool is disabled unless KAGGLE_MCP_ENABLE_DESTRUCTIVE=1, and requires a confirm_token from kaggle_preview_delete_model. This provides clear when-to-use and prerequisites, distinguishing it from non-destructive operations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and description reinforces 'Read-only'. Adds context that it returns metadata and instances/variations, which is beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no waste. Front-loaded with main action 'Fetch a model's metadata', then provides param format and read-only note. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With an output schema present and only one parameter, the description covers all needed context: action, resource, parameter format, and read-only nature. No gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but description specifies that `model` must be in 'owner/slug' format. This is critical for correct usage and compensates fully for missing schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states 'Fetch a model's metadata and instances/variations' with specific verb and resource. Distinguishes from siblings like kaggle_list_models (list) and kaggle_download_model (download) by emphasizing read-only fetch of metadata and variations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage via 'Fetch a model's metadata' but provides no explicit when-to-use or alternatives. The format hint for 'model' is helpful but does not guide selection among sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate the tool is not read-only, not destructive, and open-world, which aligns with the description's mention of writing to a local directory and handling untrusted code. The description adds important behavioral details ('untrusted-wrapped and truncated') and the side-effect of local file creation, going beyond annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the main purpose, followed by a critical caveat about untrusted code and the parameter format. No unnecessary words; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (2 parameters, output schema exists), the description covers the essential aspects: purpose, parameter format, and a behavioral caveat. It could clarify what 'local work dir' means, but overall it is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description carries the full burden. It explains the kernel parameter format explicitly ('owner/slug') and implies the 'with_metadata' parameter by mentioning 'optionally metadata'. While not exhaustive, it adds significant meaning.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Pull a kernel's source') with a specific resource and destination ('into a local work dir'). It distinguishes from sibling tools like push_kernel (opposite direction) and kernel_output (retrieves output, not source).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use the tool (to retrieve kernel source) and gives the required format for the kernel parameter ('owner/slug'). However, it does not explicitly state when not to use it or mention alternative tools, but the sibling list allows inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds behavioral details beyond annotations: explains daily slot consumption, irreversibility, and return values (status + remaining budget). Aligns with destructiveHint=true and idempotentHint=false.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no filler. Critical info (gating, budget, irreversibility, return) is front-loaded and efficiently communicated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Provides sufficient context for a submission tool: gating, budget limit, consumption, and return values. Slight gap in parameter details, but overall complete given complexity and sibling context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% coverage; description does not describe individual parameters (e.g., file_path, message) beyond mentioning confirm_token. Lacks detail to help the agent understand parameter roles.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Submit a predictions file.' It specifies that it is gated, requires a confirm_token, consumes a daily slot, and returns status, leaving no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states prerequisites: 'requires a valid confirm_token from kaggle_preview_submission AND available submission budget (~5/day/team).' Warns it's 'hard to undo,' guiding against misuse. Effectively distinguishes from sibling tools like kaggle_preview_submission.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and openWorldHint=true. The description adds that only public scores are used (private scores hidden until deadline), which is useful context. No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two concise sentences that cover the tool's purpose and limitations. No unnecessary words, and key information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description adequately covers the tool's return content (best score, trend, count, failure reasons). It is complete for a digest tool with good annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds meaning by explaining that higher_is_better respects metric direction. The competition parameter is clear from context. It compensates well for the lack of schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reduces raw submission history to a decision signal including best public score, trend, today's count, and failure reasons. It distinguishes from sibling tools like kaggle_list_submissions by emphasizing it provides a digest, not a raw list.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for when you want the 'is it worth iterating?' answer, contrasting with raw list tools. However, it does not explicitly state when to use it versus alternatives or provide exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses non-trivial behavioral traits: it creates or updates and queues a run (write operation), is asynchronous, and requires polling via kaggle_kernel_status. Annotations already indicate not read-only and not destructive, which the description reinforces and expands upon.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each adding essential information: action, prerequisite, default setting, and async behavior. No wasted words; the description is front-loaded with the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the single parameter, the presence of an output schema (not shown but noted), and the annotations covering safety, the description provides complete guidance for using the tool. It explains the workflow, input, and expected follow-up (polling).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 0% description coverage for its only parameter 'folder'. The description adds meaning by stating it comes from a local folder and mentions the required kernel-metadata.json, clarifying the expected input. This compensates for the schema gap, though more detail on folder structure could be helpful.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it pushes (creates/updates and queues-run) a notebook from a local folder using Kaggle's free GPU/TPU, a specific verb-resource combination. It distinguishes from siblings like kaggle_pull_kernel (which pulls) and kaggle_kernel_status (for polling).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use: to push and run a notebook locally. It mentions a prerequisite (kernel-metadata.json) and a default behavior (private). It does not explicitly list alternatives or when-not-to-use, but the context is sufficient for most agents.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description discloses a critical behavioral trait: it does not transmit or echo the API key, adding important security context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the main purpose, and every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with no parameters and an output schema, the description covers purpose, security, and usage comprehensively. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters; schema coverage is 100%. The description does not need to add parameter details as there are none.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns the authenticated Kaggle username and credential source, using a specific verb and resource. It distinguishes itself from sibling tools by focusing on authentication identity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly advises using the tool to confirm the server resolved credentials for the expected account, which is a clear usage scenario. It does not discuss when not to use it or alternatives, but given its simple nature, it is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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