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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the polling behavior, timeout handling, and file-saving action, which are useful. However, it lacks details on permissions, rate limits, error conditions beyond TimeoutError, or what 'Success message' entails, leaving gaps for a mutation tool.

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

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

The description is well-structured with a brief purpose statement followed by a bulleted list of parameters and returns. Every sentence earns its place by explaining critical details without redundancy, making it easy to scan and understand quickly.

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

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

For a tool with 5 parameters, no annotations, and no output schema, the description covers parameter semantics well but lacks behavioral context like error handling, permissions, or output details. It's adequate as a minimum viable description but has clear gaps given the tool's complexity and mutation nature.

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

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

Schema description coverage is 0%, so the description must fully compensate. It provides clear semantics for all 5 parameters: 'release_date' format options, 'dest_path' requirements, 'poll' purpose, 'poll_interval' units, and 'timeout' behavior. This adds significant value beyond the bare schema, explaining usage and constraints effectively.

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

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

The description clearly states the verb 'download' and resource 'inference data' with scope 'for a specific period', which is specific and actionable. However, it doesn't explicitly differentiate from sibling tools like 'download_training_dataset' or 'download_meta_model' beyond the resource type, which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives like 'get_inference_data_info' (which might provide metadata without downloading) or 'download_training_dataset'. There's no mention of prerequisites, dependencies, or typical use cases, leaving the agent to infer usage from context alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the action 'download' and a success/error return, but lacks details on permissions, rate limits, file format specifics, or what happens if the path is invalid. This is inadequate for a tool that writes files to the filesystem.

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

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

The description is front-loaded with the core purpose, followed by structured Args and Returns sections. Every sentence adds value without redundancy, making it efficiently sized and well-organized for quick comprehension.

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

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

Given the tool's complexity (file download with path constraints), lack of annotations, and no output schema, the description is incomplete. It misses critical details like authentication needs, error conditions, or what the meta model contains, leaving significant gaps for safe and effective use.

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

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

The description adds meaningful context for the single parameter 'dest_path', specifying it must be an absolute path ending with .parquet, which goes beyond the schema's basic string type. With 0% schema description coverage and only one parameter, this compensates well, though it doesn't explain the .parquet requirement further.

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

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

The description clearly states the verb 'download' and the resource 'consolidated meta model for the current challenge', making the purpose specific and understandable. However, it doesn't explicitly differentiate from sibling tools like 'download_inference_data' or 'download_training_dataset' beyond mentioning the specific resource type.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing an active challenge), exclusions, or comparisons to similar tools like 'get_challenge_info' or other download tools, leaving usage context unclear.

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

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

No annotations are provided, so the description carries full burden. It mentions the tool downloads a dataset and returns a success/error message, but lacks details on permissions, rate limits, file size, network behavior, or side effects. For a download operation with zero annotation coverage, this is insufficient behavioral disclosure.

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

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

The description is well-structured and front-loaded with the core purpose, followed by clear sections for args and returns. Every sentence adds value without waste, making it efficient and easy to parse for an AI agent.

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

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

Given 2 parameters with 0% schema coverage and no output schema, the description does well on parameters but lacks output details and behavioral context. It's adequate for a simple download tool but misses completeness on usage guidelines and transparency, making it minimally viable with clear gaps.

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

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

Schema description coverage is 0%, so the description must compensate. It effectively adds meaning by explaining 'version' as a string with examples ('1.0', '2.1', 'latest') and 'dest_path' as an absolute path ending with .parquet, which clarifies usage beyond the bare schema. This fully compensates for the lack of schema descriptions.

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

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

The description clearly states the verb 'download' and the resource 'specific training dataset', making the purpose evident. However, it doesn't explicitly differentiate from sibling tools like 'download_inference_data' or 'download_meta_model', which also download different resources, so it lacks sibling differentiation for a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context, or exclusions, such as when to choose 'download_training_dataset' over 'get_training_dataset_info' or other download-related siblings, leaving usage unclear.

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

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 the tool returns a dictionary with challenge details, which is helpful, but doesn't cover other important aspects: whether this requires authentication, if there are rate limits, what specific details are included, or if it's idempotent. For a tool with zero annotation coverage, this leaves significant gaps in understanding 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.

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

The description is very concise with only two sentences, and it's front-loaded with the core purpose. The second sentence about the return value adds useful information without redundancy. However, it could be slightly more structured by explicitly stating there are no parameters or including a brief example, but overall it's efficient.

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

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is adequate but has clear gaps. It explains what the tool does and the return type, but lacks context on usage, behavioral details, or how it fits with siblings. For a basic read operation, this is minimally viable but could be more complete by addressing when to use it or what 'current challenge' means.

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

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

The tool has 0 parameters, and the input schema has 100% description coverage (though empty). The description doesn't need to explain any parameters, which is appropriate. It could theoretically mention that no parameters are required, but this is implied by the empty schema, so a baseline of 4 is justified for a parameterless tool.

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

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

The description clearly states the tool's purpose: 'Get detailed information about the current challenge.' This specifies the verb ('Get') and resource ('current challenge'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'list_all_challenges' or 'get_inference_data_info', which prevents a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when this tool is appropriate, what prerequisites might exist (e.g., needing a current challenge set), or how it differs from siblings like 'list_all_challenges'. This lack of context leaves the agent to infer usage from the tool name alone.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves information (implying read-only), but doesn't cover critical aspects like authentication needs, rate limits, error handling, or what 'detailed information' entails. The description is too vague to fully inform the agent about behavioral traits.

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

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

The description is appropriately sized and front-loaded, with the purpose stated clearly in the first sentence. The 'Args' and 'Returns' sections are structured efficiently, providing essential information without unnecessary elaboration. Every sentence earns its place.

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

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

Given the tool's low complexity (1 parameter, no nested objects) and lack of annotations/output schema, the description is minimally adequate. It covers the parameter semantics well but lacks behavioral context and usage guidelines. For a simple read operation, it meets basic needs but leaves gaps in operational understanding.

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

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

The description adds significant value beyond the input schema, which has 0% description coverage. It explains the 'release_date' parameter's semantics, including valid formats ('YYYY-MM-DD', 'current', 'latest'), which the schema alone doesn't provide. This compensates well for the schema's lack of documentation.

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

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

The description clearly states the tool's purpose with a specific verb ('Get detailed information') and resource ('inference data period'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_training_dataset_info' or 'get_challenge_info', which follow similar patterns for different resources.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, context for selecting 'release_date' values, or how it differs from other information-retrieval tools in the sibling list, leaving the agent to infer usage patterns.

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

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 states the tool gets details, implying a read-only operation, but does not disclose behavioral traits like error handling, permissions required, rate limits, or what happens if the submission_id is invalid. This leaves significant gaps in understanding how the tool behaves.

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

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

The description is brief and structured with clear sections for Args and Returns, making it easy to read. However, the 'Returns' section is vague ('Dictionary containing submission details'), which slightly reduces efficiency, but overall it is appropriately sized and front-loaded.

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

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

Given no annotations, no output schema, and low schema coverage, the description is incomplete. It lacks details on return values, error cases, and behavioral context. For a tool with 1 parameter and no structured support, more information is needed to ensure the agent can use it correctly.

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

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

Schema description coverage is 0%, so the description must compensate. It adds that 'submission_id' is 'The ID of the submission,' which provides basic meaning, but does not elaborate on format, constraints, or examples. With 1 parameter and low coverage, this minimal explanation is insufficient for full understanding.

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

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

The description states the tool 'Get details about a specific submission,' which clearly indicates its purpose as retrieving information. However, it lacks specificity about what 'details' entail and does not differentiate from sibling tools like 'list_submissions' or 'get_challenge_info,' making it vague in comparison.

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

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

The description provides no guidance on when to use this tool versus alternatives such as 'list_submissions' or 'get_inference_data_info.' It mentions retrieving details for a specific submission but does not specify contexts, prerequisites, or exclusions, leaving usage unclear.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states this is a read operation ('Get'), which is helpful, but doesn't mention important behavioral aspects like whether this requires authentication, rate limits, error conditions, or what specific details are included in the returned dictionary. The description is too minimal for a tool with no annotation coverage.

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

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

The description is perfectly structured and concise. It begins with a clear purpose statement, then provides an 'Args' section with specific parameter guidance, and a 'Returns' section indicating the output format. Every sentence earns its place with no wasted words.

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

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

Given the tool's moderate complexity (single parameter read operation) and lack of both annotations and output schema, the description is minimally adequate but has clear gaps. It explains the parameter well and indicates a dictionary return type, but doesn't describe what specific details the dictionary contains or important behavioral constraints. The description does the basics but could provide more context for optimal agent usage.

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

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

The description provides excellent parameter semantics despite 0% schema description coverage. It clearly explains that 'version' accepts version strings like '1.0' or '2.1' and the special value 'latest', which adds crucial meaning beyond what the bare schema provides. This fully compensates for the lack of schema descriptions.

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

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

The description clearly states the tool's purpose with a specific verb ('Get') and resource ('detailed information about a specific training dataset'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this tool from sibling tools like 'list_training_datasets' or 'get_inference_data_info', which would be needed for a perfect score.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'list_training_datasets' (which might be used to discover available datasets first) or clarify the relationship between this tool and 'download_training_dataset'. There's no context about prerequisites or typical workflows.

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

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

With no annotations provided, the description carries full burden for behavioral disclosure. It only states what the tool does and mentions the return format. It doesn't describe important behavioral aspects like whether this is a read-only operation, if it requires authentication, if there are rate limits, pagination behavior, or what happens when no challenges exist. For a tool with zero annotation coverage, this is insufficient.

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

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

The description is appropriately concise with two sentences that each serve a clear purpose. The first sentence states the tool's function, and the second describes the return format. There's no wasted verbiage or redundant information. However, it could be slightly more structured with clearer separation between purpose and return details.

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

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

Given the tool's simplicity (0 parameters, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and return format. However, for a list operation that likely interacts with a challenge system, it should ideally mention whether this lists all challenges globally, for a specific user, or within some context. The absence of output schema means the description should do more to explain the return structure.

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

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

The tool has 0 parameters with 100% schema description coverage. The description correctly doesn't waste space describing non-existent parameters. Since there are no parameters to document, the baseline score of 4 is appropriate - the description focuses on what matters (the tool's purpose and return value) without unnecessary parameter discussion.

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

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

The description clearly states the tool's purpose with a specific verb ('List') and resource ('all available challenges'). It distinguishes from some siblings like 'get_challenge_info' (which gets details for a specific challenge) and 'list_submissions' (which lists submissions rather than challenges). However, it doesn't explicitly differentiate from all siblings like 'list_training_datasets'.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use 'list_all_challenges' versus 'get_challenge_info' (for specific challenge details) or 'switch_challenge' (which likely changes active challenge). No explicit when/when-not instructions or alternative tool references are provided.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool lists 'recent submissions' and describes parameter behavior, but lacks critical details: what 'recent' means (timeframe, pagination), authentication requirements, rate limits, error conditions, or whether it's read-only (implied but not stated). For a tool with zero annotation coverage, this is insufficient.

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

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

The description is appropriately sized and well-structured: a clear purpose statement followed by organized sections for Args and Returns. Every sentence adds value - no redundant or unnecessary information.

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

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

Given no annotations, no output schema, and a simple single parameter, the description covers the basic purpose and parameter semantics adequately. However, it lacks details about the return format (beyond 'dictionary containing list'), error handling, or what constitutes 'recent' submissions. For a listing tool with minimal structured context, it's minimally viable but has clear gaps.

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

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

The description adds significant value beyond the input schema, which has 0% description coverage. It explains the 'period' parameter's purpose, provides concrete examples ('current', 'YYYY-MM-DD'), and clarifies it's optional. This fully compensates for the schema's lack of documentation.

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

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

The description clearly states the tool's purpose with 'List recent submissions' - a specific verb ('List') and resource ('submissions') with a temporal qualifier ('recent'). However, it doesn't differentiate from sibling tools like 'get_submission' or 'list_all_challenges', which would require explicit comparison for a score of 5.

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

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

The description implies usage through the parameter explanation (filtering by period), but doesn't explicitly state when to use this tool versus alternatives like 'get_submission' (which appears to retrieve a single submission) or 'list_all_challenges'. No explicit when-not-to-use guidance or named alternatives are provided.

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

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 mentions the return type ('Dictionary containing list of training datasets'), which adds some value, but lacks details on permissions, pagination, error handling, or data format. For a read operation with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves.

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

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

The description is brief and front-loaded with the main purpose, followed by a return statement. Both sentences are relevant, with no wasted words. However, the structure could be slightly improved by integrating the return info more seamlessly, but it's still highly efficient.

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

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

Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is adequate but minimal. It covers the basic purpose and return type, which is sufficient for a straightforward list operation. However, it lacks details on behavioral aspects like error cases or data structure, leaving room for improvement in completeness.

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

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

The tool has 0 parameters, and schema description coverage is 100%, so no parameter documentation is needed. The description doesn't add param info beyond the schema, but with no parameters, a baseline of 4 is appropriate as there's nothing to compensate for or improve upon.

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

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

The description clearly states the verb ('List') and resource ('training datasets') with scope ('for the current challenge'), making the purpose specific and understandable. It distinguishes from siblings like 'get_training_dataset_info' (detailed info) and 'download_training_dataset' (download). However, it doesn't explicitly differentiate from 'list_all_challenges' (different resource), so it's not a perfect 5.

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

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

The description provides minimal guidance, stating only 'for the current challenge' without explaining when to use this tool versus alternatives like 'get_training_dataset_info' or 'download_training_dataset'. No explicit when/when-not instructions or prerequisites are given, leaving usage context implied rather than clearly defined.

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

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 the tool submits predictions, implying a write operation, but doesn't disclose critical traits like required permissions, whether submissions are reversible, rate limits, or error handling. The return value is vaguely described as a 'Dictionary with submission details' without specifying content or structure, leaving gaps in understanding the tool's behavior.

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

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

The description is appropriately sized and front-loaded, starting with the core purpose. The Args and Returns sections are structured clearly, with no redundant sentences. However, it could be slightly more concise by integrating the parameter explanations into a single sentence, but overall, it's efficient and well-organized.

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

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

Given the complexity of a submission tool with no annotations, 2 parameters (one required), 0% schema coverage, and no output schema, the description is incomplete. It lacks details on behavioral traits, parameter constraints, and return value specifics. For a tool that performs a write operation, more context is needed to ensure safe and correct usage, such as error conditions or submission limits.

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

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

Schema description coverage is 0%, so the description must compensate for undocumented parameters. It adds some meaning by explaining 'df' as 'dataframe containing predictions data' and 'slot' as 'Submission slot number (1-based, default: 1)', which clarifies the purpose and default value. However, it doesn't detail the JSON format for 'df' or constraints on 'slot' (e.g., valid ranges), leaving significant gaps in parameter understanding.

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

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

The description clearly states the tool's purpose: 'Submit predictions from a JSON representation of a dataframe.' It specifies the verb ('submit'), resource ('predictions'), and source format ('JSON representation of a dataframe'), which is specific and actionable. However, it doesn't explicitly differentiate from its sibling 'submit_predictions_from_file', which handles file-based submissions versus JSON dataframes.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'submit_predictions_from_file' for file-based submissions or other related tools like 'get_submission' or 'list_submissions'. There's no context on prerequisites, such as needing to have predictions ready in JSON dataframe format, or exclusions for 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.

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool submits predictions, it doesn't describe what 'submission' entails - whether it's a write operation, if it requires specific permissions, what happens to existing submissions, or any rate limits. The return format is mentioned but not detailed.

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

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

The description is efficiently structured with a clear purpose statement followed by Args and Returns sections. Every sentence adds value - the first establishes the core function, while the parameter and return explanations provide essential details without redundancy.

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

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

For a submission tool with no annotations and no output schema, the description provides basic operational information but lacks important context about what submission means in this system, how it relates to challenges, or what the submission details dictionary contains. It's minimally adequate but leaves significant gaps.

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

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

The description provides meaningful context for both parameters: it specifies that file_path must be an absolute path ending with .parquet, and clarifies that slot is 1-based with a default of 1. With 0% schema description coverage, this compensates well by adding crucial semantic information beyond what the bare schema provides.

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

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

The description clearly states the action ('Submit predictions') and resource ('from a Parquet file'), making the purpose evident. However, it doesn't explicitly distinguish this tool from its sibling 'submit_predictions_from_dataframe', which appears to serve a similar purpose with different input format.

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

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling 'submit_predictions_from_dataframe' or explain when file-based submission is preferred over dataframe-based submission, nor does it provide any prerequisites or context for when submission is appropriate.

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

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 the tool 'switches' to a challenge, implying a state change or context setting, but doesn't describe what this switch entails (e.g., does it affect other tools, require specific permissions, have side effects, or return detailed output?). The mention of a 'Success message' return is minimal and doesn't clarify behavior beyond the basic action.

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

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

The description is appropriately concise with three short sentences that cover purpose, parameter, and return value. It uses a structured format with 'Args:' and 'Returns:' sections, making it easy to parse. However, the front-loading could be improved as the first sentence is somewhat vague, and the structure, while clear, is minimal without additional explanatory context.

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

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

Given the complexity of a 'switch' operation (likely involving state changes), no annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain what 'switch' means operationally, potential prerequisites, effects on other tools, or details of the return value beyond 'Success message'. For a tool that might alter context or settings, this leaves significant gaps in understanding its full behavior and implications.

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

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

The description adds meaningful context for the single parameter 'challenge_slug' by explaining it's 'the slug of the challenge to switch to'. With 0% schema description coverage and only one parameter, this adequately compensates by providing the parameter's purpose, though it doesn't specify format constraints or examples. The baseline for 0 parameters would be 4, but here the single parameter is well-explained relative to the schema gap.

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

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

The description states the purpose ('Switch to a different challenge') which is clear but vague. It specifies the action ('switch') and resource ('challenge'), but doesn't explain what 'switch' means in this context or how it differs from similar tools like 'get_challenge_info' or 'list_all_challenges'. The purpose is understandable but lacks specificity about the operational effect.

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

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

The description provides no guidance on when to use this tool versus alternatives. With siblings like 'get_challenge_info' and 'list_all_challenges', it's unclear whether this tool changes an active context, selects a challenge for subsequent operations, or performs some other function. There are no explicit when/when-not statements or named alternatives mentioned.

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