Flips a matrix over its diagonal, turning rows into columns and columns into rows. Use it to reorganize data or prepare matrices for multiplication.
Transposes a matrix.
Args: matrix: The matrix to transpose as a list of lists.
Returns: On success: {"result": } On error: {"error": }
Examples: >>> matrix_transpose([[1, 2], [3, 4]]) {'result': [[1, 3], [2, 4]]}
| Name | Required | Description | Default |
|---|---|---|---|
| matrix | Yes |
Changes observed during successful MCP inspections.
v1.1.1Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It does disclose the return envelope on success and on error, which is real behavioral value given no output schema exists. However, it says nothing about edge cases such as ragged or non-numeric input, so the disclosure is only partial.
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 docstring is front-loaded with the purpose, then Args, Returns, and a worked example, with no filler sentences. It is slightly redundant in restating the matrix argument in prose, but every block serves a purpose.
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 pure function with no output schema and no annotations, the description supplies the input format, the success/error response shape, and a concrete example. Only the handling of malformed input is left unstated, which is a minor gap at this complexity level.
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 does describe the single parameter as "a list of lists", which matches the schema's array-of-arrays-of-numbers type. That is a marginal clarification rather than added semantics (e.g., no rectangularity or dimension constraints), so it lands at the baseline.
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?
"Transposes a matrix." gives a specific verb and resource, and the operation is unambiguously distinct from siblings like matrix_addition, matrix_multiplication, and matrix_determinant. No sibling tool overlaps with transposition, so the agent can route without ambiguity.
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?
There is no explicit statement of when to use this tool versus the other matrix/vector siblings, nor any prerequisites or exclusions. Usage is implied by the highly self-descriptive name and single-purpose operation, which is the minimum viable level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.