Retrieve transform datasets from IC-CAP as dictionaries. Select measured, simulated, or both data, optionally including imaginary components.
Get a Transform dataset as a dictionary. Returns measured (M), simulated (S), or both (B) data. Set imag=true to include imaginary components.
| Name | Required | Description | Default |
|---|---|---|---|
| msb | No | M=measured, S=simulated, B=both | B |
| imag | No | Include imaginary parts | |
| transform_path | Yes | Transform path |
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It communicates that this is a read operation returning a dictionary and describes the M/S/B selection and imaginary-part flag. However, it does not disclose error behavior, dictionary key structure, or any potential side effects, leaving some behavioral uncertainty.
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 two short sentences with no filler. It front-loads the core purpose and then adds the key options, making it easy for an agent to parse quickly.
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 retrieval tool with only three parameters and no output schema, the description is largely sufficient: it names the return type, the data variants, and the optional flag. It does not define the exact dictionary keys, but the overall usage is clear enough for a straightforward getter.
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 already covers all parameters with 100% description coverage, so the baseline is 3. The description reinforces the meaning of msb (M/S/B) and imag, but does not add new information beyond what the schema provides.
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 a specific verb ('Get'), a resource ('a Transform dataset'), and the return format ('as a dictionary'). It also defines the M/S/B data-type choice, which helps distinguish this from related tools such as transform_get_df. However, it does not explicitly name any sibling alternative, so it falls just short of full differentiation.
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?
No guidance is given about when to use this tool instead of alternatives like transform_get_df, input_get_data, or output_get_data. The description explains what data can be requested but does not state when to choose this function over another.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.