duplicate_custom_dataDuplicate a custom-data dataset with a new name.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Source custom-data ID (from list_custom_data) | |
| new_name | Yes | Name for the copy |
| Name | Required | Description | Default |
|---|---|---|---|
| data | No | The copy — its own new id, plus the name, columns and rows. | |
| success | Yes | True when the call succeeded. |
Changes observed during successful MCP inspections.
Input schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Output schema / $schemaRemoved value: -"http://json-schema.org/draft-07/schema#"Output schema / properties / success / descriptionPrevious value: -"True when the call succeeded. A failure comes back as an error result instead."New value: +"True when the call succeeded."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate the operation is not read-only and not destructive, and the description adds little behavioral context beyond reaffirming that a copy is made. It does not disclose potential duplicate-name conflicts, whether the copy is a full deep copy, or whether the operation can fail partway.
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, direct sentence with no filler. The core action and the distinguishing constraint ('with a new name') are both front-loaded and clearly expressed.
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 two-parameter duplication action with an output schema and safety-relevant annotations, the description is nearly sufficient. It lacks only minor context such as naming uniqueness expectations or whether the copy inherits all dataset contents, but these are not needed to invoke the tool 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?
The input schema fully describes both parameters, including the source ID origin and the meaning of new_name, giving 100% schema coverage. The description does not add parameter-level detail, so the baseline score of 3 is 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 uses the specific verb 'Duplicate' with the resource 'custom-data dataset' and adds the key constraint 'with a new name'. This clearly distinguishes it from related tools like create_custom_data, update_custom_data, and archive_custom_data 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is only implied by the verb 'Duplicate' rather than explicitly stated. An agent can infer this is for copying an existing custom-data dataset rather than creating one from scratch, but there is no direct comparison to create_custom_data or any mention of when not to use this tool.
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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