List all datasets in E2E TIR for AI/ML training and fine-tuning. Filter by project and location to manage your ML data.
List all datasets stored in E2E TIR for AI/ML training and fine-tuning.
| Name | Required | Description | Default |
|---|---|---|---|
| location | No | Location/region code. | |
| project_id | No | Project ID. |
Changes observed during successful MCP inspections.
v1.0.1Does 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 not state that the operation is read-only, whether permissions are required, whether results are paginated, or what side effects exist. Only the word 'List' implicitly suggests a safe read, which is minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It efficiently states the action and scope, though it could be slightly more informative without losing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple list tool with two optional parameters and no output schema, the description adequately conveys the resource being listed. However, it does not describe the return format (e.g., what fields a dataset contains) or pagination behavior, leaving some gaps for an agent to infer.
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 100%, so both parameters (location, project_id) are already documented in the schema. The description adds no additional meaning, syntax, or filtering context beyond what the schema provides, making the baseline 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('List'), resource ('datasets'), and domain context ('stored in E2E TIR for AI/ML training and fine-tuning'). It distinguishes the tool from generic list siblings like e2e_list_databases, but it does not explicitly differentiate from other TIR-specific list tools such as e2e_tir_list_notebooks or e2e_tir_list_model_endpoints.
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 offers no when-to-use guidance, prerequisites, or alternatives. It does not say when to prefer this tool over sibling list tools or what conditions would make it appropriate, leaving usage entirely implied by the tool name.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.