Retrieve IC-CAP input datasets as DataFrames, with optional matrix row and column indexing. Use it to access specific data points for analysis and parameter extraction.
Get Input dataset as DataFrame with optional matrix indexing.
| Name | Required | Description | Default |
|---|---|---|---|
| m | No | Matrix row index | |
| n | No | Matrix column index | |
| msb | No | M | |
| imag | No | ||
| input_path | Yes |
Changes observed during successful MCP inspections.
v0.2.2Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral burden. It communicates a read-style operation ('Get') and the output form ('DataFrame'), but it does not explain side effects, error behavior, indexing conventions, or what msb and imag do. This is acceptable for a simple getter but leaves meaningful gaps.
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 compact sentence with the core purpose front-loaded. There is no redundant text, and every phrase contributes meaning.
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?
Given 5 parameters, one required but undocumented parameter, no annotations, and no output schema, this description is too thin for an agent to invoke the tool with confidence. Key semantics around input_path, msb, imag, and indexing behavior are missing.
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 coverage is only 40%, so the description must compensate for undocumented parameters. It adds meaning only to m/n through 'optional matrix indexing' and loosely to input_path via 'Input dataset'. The required input_path and the msb/imag parameters remain effectively unexplained, which is a significant gap for a 5-parameter tool.
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 the action ('Get'), the target resource ('Input dataset'), and the return form ('as DataFrame'), so an agent can tell this returns a dataframe rather than raw data. It does not explicitly differentiate from siblings like input_get_data or input_get_field, but the 'as DataFrame' phrase provides some separation.
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 phrase 'as DataFrame with optional matrix indexing' implies when this tool is useful—when a dataframe or matrix-indexed view is needed—but it does not state alternatives or when not to use it. No explicit routing to input_get_data or output_get_df is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.