Computes the transpose of a stored tensor.
Args:
name (str): The name of the tensor to transpose.
Returns:
Tensor: The transposed tensor.
Raises:
ValueError: If the tensor name is not found in the store.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Name of the stored tensor to transpose. |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections.
Input schema / properties / name / descriptionAdded value: +"Name of the stored tensor to transpose."Output schema / (root)Previous value: -nullNew value: +{
+ "properties": {
+ "result": {
+ "anyOf": [
+ {
+ "type": "number"
+ },
+ {
+ "items": {
+ "type": "number"
+ },
+ "type": "array"
+ },
+ {
+ "items": {
+ "items": {
+ "type": "number"
+ },
+ "type": "array"
+ },
+ "type": "array"
+ },
+ {
+ "items": {
+ "items": {
+ "items": {
+ "type": "number"
+ },
+ "type": "array"
+ },
+ "type": "array"
+ },
+ "type": "array"
+ }
+ ],
+ "title": "Result"
+ }
+ },
+ "required": [
+ "result"
+ ],
+ "title": "transposeOutput",
+ "type": "object"
+}Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds beyond that by stating the return value is the transposed tensor and by documenting the ValueError when the tensor name is not found.
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 tight docstring with a one-sentence summary plus compact Args, Returns, and Raises sections. Every part earns its place and there is no filler or redundant prose.
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 one-parameter, non-mutating operation backed by annotations, an output schema, and a documented failure mode, the description is largely complete. It does not specify exact transpose behavior for higher-dimensional tensors, but the low complexity and standard meaning make this a minor gap.
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 the schema already fully documents the single 'name' parameter. The description repeats the parameter's meaning with 'The name of the tensor to transpose' but does not add new constraints, formats, or edge-case guidance.
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 opens with 'Computes the transpose of a stored tensor,' giving a specific verb and resource. This clearly distinguishes it from sibling operations like matrix_inverse, determinant, and view_tensor without needing to open 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?
The description clearly establishes the context: it operates on a stored tensor and requires the tensor's name. It does not explicitly list exclusions or alternative tools, but the operation is unique enough among siblings that the usage context is clear.
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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