Retrieve detailed information about specific inference data periods for CrowdCent prediction challenges, enabling analysis of datasets and submission requirements.
Get detailed information about a specific inference data period.
Args:
release_date: The release date in 'YYYY-MM-DD' format or 'current' or 'latest'
Returns:
Dictionary containing inference data details| Name | Required | Description | Default |
|---|---|---|---|
| release_date | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It 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.
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