AiAuN

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation4/5
Each tool targets a distinct step in the Kaggle/Drive experiment workflow: validation, local inspection, dataset/kernel operations, script source, tracking, and Drive transfer. Only the two smoke-script tools are close variants, but their names and descriptions clearly separate color-class vs. ResNet50 GPU usage.
Naming Consistency2/5
Names are readable but grammatically inconsistent: some are verb-first like inspect_local_dir, some are object-verb like kaggle_kernel_push, and others are noun phrases like experiment_tracking_links or drive_folder_info. The kaggle_* and drive_* prefixes provide some grouping, but there is no single predictable verb_noun convention.
Tool Count5/5
14 tools is a reasonable size for an experiment pipeline spanning local inspection, Kaggle dataset/kernel lifecycle, and Drive output transfer. Each tool maps to a needed step, so the count feels well-scoped.
Completeness4/5
The set covers the main loop: validate request, inspect data, push dataset, push kernel, poll logs, move outputs to Drive, and provide tracking links. Minor gaps exist—configs can be listed but not read, and there is no update/cancel for kernels—but agents can generally work around them.
Average 3.4/5 across 14 of 14 tools scored. Lowest: 2.8/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It does reveal a size constraint, but it omits important details like whether existing datasets are overwritten, what the 'force' option does, and what side effects or authorization requirements apply.
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 short and front-loads the core action. The second sentence about size limits is relevant and not wasteful, though the overall under-specification prevents a perfect score.
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?
An output schema exists, so return-value explanation is not required, but the description is incomplete for safe and correct invocation. It lacks parameter semantics, usage context, auth expectations, and the behavior of the force flag.
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 compensate for documenting parameters. It only loosely implies 'local_path' through 'local folder' and gives no meaning for 'dataset_slug', 'title', or especially 'force'.
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 verb and resource: 'Upload a local folder as a Kaggle dataset.' This clearly differentiates the tool from siblings like kaggle_kernel_push and kaggle_dataset_check by naming both the action and the target.
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 gives no guidance on when to use this tool versus kaggle_dataset_check or kaggle_kernel_push. It also fails to mention prerequisites such as Kaggle authentication or whether the dataset slug must already exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. 'Poll' suggests a read-only status check, but the description does not say whether it blocks until completion, what statuses are returned, how often it should be polled, or how it behaves on failure.
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 short sentence with no filler. The verb and object are front-loaded, making it immediately scannable and efficient.
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?
Although this is a simple one-parameter tool and an output schema exists, the description lacks essential usage context: when to poll, how the status result should be interpreted, and how this complements or differs from sibling kernel tools. The minimal information leaves too much for the agent to infer.
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 description adds no meaning to the sole parameter, kernel_slug, beyond what the schema field name already implies. With 0% schema description coverage, the description was expected to clarify what a kernel slug is or where to find it, but it does not.
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 uses a specific verb, 'Poll', and a clear resource, 'Kaggle kernel execution status', which makes the tool's core purpose obvious. It is distinguishable from siblings like kaggle_kernel_push and kaggle_kernel_logs by the word 'status', though it does not explicitly name or contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as kaggle_kernel_logs or kaggle_kernel_output_to_drive. The description implies a polling workflow but does not state prerequisites, timing, or conditions for choosing this tool.
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?
With no annotations, the description carries the full burden. It discloses meaningful side effects: training runs, inference on best.pt, artifacts are uploaded to Drive, and a push occurs with enable_gpu=true. However, it does not explain what 'push' refers to, what artifacts are uploaded, or required permissions/credentials, so transparency is adequate but incomplete.
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 compact and front-loaded, opening with the tool's identity and then listing key actions. Every clause contributes information, and there is no filler or repetition of the tool name.
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 relatively simple (one optional parameter, output schema exists), and the description covers the pipeline's main behaviors. Still, it omits dataset_slug semantics and any comparison to aiaun_smoke_script, leaving the agent with gaps for confidently selecting and calling the tool in non-default cases.
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, dataset_slug, is completely undocumented by the description, and schema description coverage is 0%. The description does not even mention the dataset or explain how this parameter affects the smoke run, so it adds no semantic value beyond the bare property name and default.
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 identifies a specific resource (ResNet50 GPU smoke test) and a sequence of actions: short train, inference on best.pt, Drive upload, and push with enable_gpu=true. It clearly distinguishes this from the sibling aiaun_smoke_script by mentioning ResNet50 and GPU, though it lacks a single explicit main verb like 'run a smoke test.'
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 is given about when to choose this tool over the closely related aiaun_smoke_script or other siblings. The phrase 'Push with enable_gpu=true' is an instruction, not usage context or an exclusion condition, so the agent is left to infer applicability.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden for behavioral disclosure. It only says the tool returns URLs and that they should always be shown in chat; it does not say whether this is read-only, whether it requires authentication, what data it reads from, or how failures are surfaced. This is a meaningful gap for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no fluff. The purpose is front-loaded and the chat display instruction earns its place as actionable guidance. It is concise without being padded, though a bit more behavioral detail would be possible without hurting clarity.
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?
This is a 4-parameter tool with no annotations and little description. The output schema existence reduces the need to explain return values, but the definition still lacks usage conditions, parameter semantics, and behavioral expectations. The agent would have to guess at critical details before invoking it reliably.
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 for empty parameter documentation. It maps loosely to the parameters by naming Kaggle (kernel_slug, dataset_slug), W&B (wandb_run_id), and Drive (drive_file_id), but does not explain expected formats, optionality, or how the parameters relate to the current experiment. The mapping is inferable but not explicit enough.
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 uses a specific verb ('Return') and a concrete resource: Kaggle / W&B / Drive URLs for tracking the experiment. This makes the tool's basic purpose clear and distinguishes it from sibling tools focused on inspecting, pushing, or uploading. It stops short of 5 because it does not explicitly contrast itself with any sibling.
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?
'To track this experiment' provides implied usage context, and 'Always show these links in the chat' gives an explicit display instruction. However, there is no guidance on when not to use this tool or how it compares to alternatives like resolve_experiment_request, drive_folder_info, or kaggle_kernel_logs.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states a push action but does not say whether this creates a new kernel, updates an existing one, requires authentication, or has side effects like overwriting content.
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 two short sentences with no filler, and the key purpose is front-loaded. The added dataset_slugs format note is directly useful, though the overall brevity leaves some gaps.
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?
For a 5-parameter tool with no annotations, the description is too minimal to be complete. It does not explain most parameters, prerequisites, effects, or when to choose this tool over its siblings; the output schema mitigates return-value ambiguity but not the other 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 compensate, but it only clarifies dataset_slugs as a comma-separated owner/slug list. The other four parameters—kernel_slug, script_content, title, and enable_gpu—receive no semantic explanation beyond their schema titles.
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?
"Push a Kaggle script kernel" names a concrete action and resource, making it immediately clear what the tool does. The kernel resource also distinguishes it from sibling tools like kaggle_dataset_push and kaggle_kernel_status.
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 gives no guidance on when to use this tool versus alternatives, no prerequisites, and no exclusions. The sibling tool names imply a kernel-focused workflow, but that is left to inference rather than stated.
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?
There are no annotations, so the description carries the full behavioral burden. It does disclose one non-obvious trait: logs are often only available after the kernel is complete. However, it does not mention whether the call is read-only, what happens when logs are not ready, or whether it errors or returns partial data.
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, front-loaded sentence with no filler. It efficiently communicates the core action and a key timing caveat.
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?
This is a simple one-parameter tool with an output schema, so the return format need not be described. Still, the description is thin: it leaves usage timing vague with 'often,' lacks parameter format details, and does not help an agent decide between this and closely related sibling tools.
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%, and the description does not explain the kernel_slug parameter or its expected format. The parameter title 'Kernel Slug' and the tool name provide some self-evident meaning, but there is no guidance on forms like 'owner/kernel-slug' versus just a slug, and the description adds no parameter-level value.
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 states a clear verb and resource: 'Fetch kernel output logs.' The caveat 'often only after complete' adds context and helps distinguish from status-checking tools like kaggle_kernel_status. However, it does not explicitly differentiate itself from sibling tools or specify whether 'logs' means stdout/stderr, full output, or something else.
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 explicit when-to-use or when-not-to-use guidance is provided. The phrase 'often only after complete' implies logs should be fetched after kernel completion, but it does not explain how this relates to kaggle_kernel_status or kaggle_kernel_output_to_drive, nor when an agent should prefer an alternative.
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?
No annotations are provided, so the description carries the full burden. It discloses a key behavior — asking the user and blocking other tools when missing is non-empty — but it does not clarify whether the operation is read-only, what 'missing' contains, or what happens when nothing is missing.
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 two short sentences, front-loaded with the core action and then the conditional behavior. It is efficient, though the undefined term 'missing' slightly hurts clarity.
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?
For a 7-parameter tool with no annotations and no schema descriptions, this is too thin. An agent cannot determine what makes an experiment request complete, what 'missing' refers to, or how to interpret the result beyond the stated pause-and-ask behavior.
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 names none of the seven parameters. The phrase 'required experiment fields' hints that the parameters are the subject of the check, but it does not specify which fields are required or how the default-empty strings should be interpreted.
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 names a specific action — checking required experiment fields — and adds a conditional follow-up: ask the user if missing is non-empty and do not run other tools. It is clearly distinct from the sibling kaggle and drive tools, though it does not name an alternative or define what 'required' means.
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?
It gives useful sequencing guidance: if missing is non-empty, ask the user and do not run other tools yet. However, it does not explicitly state when to call this tool compared to its siblings, nor does it mention any alternatives.
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?
With no annotations, the description carries the full burden. 'Verify' and 'service-account access' convey a read-only authentication check and identify the auth context. However, it does not disclose side effects, failure behavior, or operational details such as required environment setup.
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 with no wasted words; it front-loads the action and target. Appropriate length for such a simple, parameter-less tool.
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 zero parameters and the presence of an output schema, the description is close to sufficient for selecting and invoking the tool. But with no annotations, it leaves unspecified prerequisites, side-effect expectations, and what a failed verification looks like.
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 tool has zero parameters, so schema coverage is trivially complete. The description adds useful context by indicating that the target folder is identified externally via GOOGLE_DRIVE_FOLDER_ID rather than through a tool parameter.
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 identifies the action ('Verify') and the target ('Google Drive service-account access to GOOGLE_DRIVE_FOLDER_ID'). This distinguishes it from sibling upload tools, though it does not explicitly describe what folder information will be returned.
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 gives no explicit guidance on when to use this tool versus alternatives, and it names no sibling tools. It implies a preflight/setup access-check use case, but the agent must infer this context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does reveal useful auth context with 'Shared Drive + SA; no browser', but it omits important side-effect details such as whether existing files are overwritten, how name collisions are handled, and what permissions or prerequisites are needed for a write operation.
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 front-loaded sentence with no filler. The parenthetical adds meaningful deployment context without redundancy. Every word earns its place.
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 relatively simple with two parameters and an output schema, so the description does not need to explain return values. However, the missing semantics of the optional 'name' parameter and the lack of behavioral details around overwriting or collisions make it incomplete for fully reliable invocation.
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?
Input schema description coverage is 0%, and the tool description does not mention either parameter. While 'local_path' is fairly self-explanatory, the optional 'name' parameter's meaning and its default behavior are unclear, so the description does not 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 a specific action and resource: 'Upload a file to the configured Drive folder'. It also adds useful scoping context in parentheses: 'Shared Drive + SA; no browser'. This makes the tool's purpose obvious and distinguishes it from siblings like drive_folder_info or the Kaggle-related tools.
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 the tool is for uploading a local file to the configured Drive folder, which gives reasonable context for when to use it. However, it does not explicitly name alternatives or state when not to use it, leaving the agent to infer routing from the action verb alone.
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?
With no annotations, the description carries the transparency burden. It clearly signals a read-only retrieval via 'Return' and adds a useful safety disclosure with '(no secrets)'.
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, front-loaded sentence with zero waste. Every word contributes meaning, and the important scoping words come first.
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 and has an output schema, so return-value documentation is not needed. However, the optional dataset_slug parameter is unexplained and no usage context is given, leaving a small but real completeness gap.
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 needed to explain dataset_slug, but it does not mention the parameter at all. The schema's title and default offer minimal meaning, but the description adds no semantic value beyond that.
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?
States a specific verb and object: 'Return the inlined synthetic color-class smoke kernel source.' The qualifier 'synthetic color-class' distinguishes it from the sibling 'aiaun_resnet50_gpu_smoke_script' without needing to inspect the schema.
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 the similar sibling smoke-script tool or other notebook/kernel tools. There are no context cues, prerequisites, or alternative routing 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?
No annotations are present, so the description is the only behavioral source. It adds useful traits: local service account, SSH-safe, no browser, and the preference for kernel-side upload. It does not disclose prerequisites like Drive credentials, temp-file handling, or potential collisions, leaving some ambiguity.
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?
One tight sentence front-loads the action and follows with the key constraint and alternative. No filler.
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 a multi-step transfer with a single parameter and an output schema, and the description covers source, destination, method, and an alternative. It leaves the 'when possible' condition for the alternative unspecified and does not mention any required local account configuration, so completeness is adequate but not strong.
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% and the description does not explain the kernel_slug format or provide examples. It only contextualizes the parameter as identifying the kernel whose output is downloaded, which is minimal compensation for the low schema coverage.
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 the exact workflow: download Kaggle kernel output, then upload it to Drive via the local service account. It also mentions the preferred alternative, distinguishing this tool from kaggle_kernel_logs, drive_upload_file, and similar siblings.
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?
It explicitly says to prefer kernel-side User Secrets upload when possible, giving the agent a routing rule. However, 'when possible' is not defined, so the agent lacks clear criteria for deciding between this tool and the alternative.
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?
With no annotations, the description carries the full burden. It discloses the core behavior (an existence check) and implies a read-only operation, but it does not mention network dependence, authentication, or failure behavior. It is not misleading, but it is thin.
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 with no filler. Every word earns its place, and the key scoping detail (owner/slug) 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 a single required parameter and the presence of an output schema, the description is adequate for basic invocation. The main gaps are explicit usage guidance and parameter format examples, but the simple nature of the check makes this acceptable.
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 only lists 'dataset_slug' with no description. The description clarifies that the parameter is the 'owner/slug' combined identifier, which is valuable information not apparent from the schema. However, it stops short of providing a concrete format example.
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 verb ('check') and a specific resource ('Kaggle dataset owner/slug existence'), which clearly distinguishes it from sibling tools that push, status, or log. The purpose is immediately understandable.
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 does not explicitly state when to use this tool or mention alternatives, though the sibling kaggle_dataset_push implies this check would be performed before pushing a dataset. The usage context is implied but not stated.
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?
With no annotations, the description carries the full burden. 'Inspect' implies a read-only operation, and listing the returned summary items (file count, total bytes, sample names) gives a solid behavioral contract. It does not cover edge cases like missing paths or permission errors, but the core behavior is clear.
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, efficient sentence with no redundant wording. The core action and output detail are front-loaded, and every part of the sentence earns its place.
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, the description does not need to explain return formatting. The tool is simple, has one required parameter, and the description plus sibling context provide a complete picture for correct invocation.
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 contains only a bare 'path' string with 0% description coverage. The description adds essential meaning by specifying that the path must refer to a local data directory, which is enough for the single parameter.
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 ('Inspect') with a clear resource ('local data directory') and enumerates concrete outputs (file count, total bytes, sample names). This clearly differentiates it from sibling tools that target Kaggle, Drive, or experiment resources.
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 clearly establishes that this tool is for local directory inspection, making the use case apparent against the remote-focused siblings. It does not explicitly name alternatives or state when not to use it, but the context is strong enough for an agent to route 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?
With no annotations provided, the description carries the behavioral disclosure burden. It states the operation is a list (implying read-only) and scopes it to YAML files under a specific path. It does not mention recursion or path formatting, but for a simple list tool this is adequate.
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, information-dense sentence. It front-loads the verb and resource, includes the target path, and contains no filler or repetition.
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 has no parameters, an output schema exists, and the operation is a simple directory listing, the description is complete enough for an agent to invoke it confidently. Nothing critical is missing.
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 tool has zero parameters, so the input schema is trivially complete. The description adds value by specifying the exact repository path and file type, which is the only semantic information an agent needs.
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 a precise resource ('experiment YAML configs under semi-mask2former/configs'). It clearly identifies what the tool operates on and the exact location, which distinguishes it from the broader set of sibling tools.
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 by naming the exact repository path, so an agent can infer when this tool is appropriate: when repository configs need to be listed. It does not explicitly mention alternatives or exclusions, but the zero-parameter, read-only nature makes this less critical.
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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