List all datasets in a COMSOL model to identify available solution data for evaluation or visualization. Optionally specify the model by name.
List all datasets in a model.
Datasets represent solution data that can be evaluated or visualized.
Args: model_name: Model name (default: current model)
Returns: List of dataset names
| Name | Required | Description | Default |
|---|---|---|---|
| model_name | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It states the tool lists datasets but does not discuss scope (e.g., all models vs. current model), error behavior, pagination, performance, or side effects. The agent has limited insight into the tool's operational traits.
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 extremely concise and well-structured. It opens with a clear purpose statement, then briefly defines the term 'datasets,' followed by parameter documentation. Every sentence provides value with no extraneous text.
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 the tool's simplicity (one optional parameter, no output schema), the description is reasonably complete. It explains the return value ('list of dataset names') and the parameter. However, it omits details like authentication requirements, potential errors, or whether the list is exhaustive. For a simple read tool, this is acceptable but not exceptional.
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 has 0% description coverage, so the description compensates by explaining the model_name parameter's purpose and default value ('Model name (default: current model)'). This adds meaningful context beyond the schema, which only shows the type and default null. The description is adequate for the single parameter.
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 that the tool lists all datasets in a model, specifying the action (list) and resource (datasets). It also provides a brief explanation of what datasets are, aiding understanding. However, it does not explicitly distinguish from sibling tools, though no other tool appears to directly compete.
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 lacks guidance on when to use this tool versus alternatives. It does not mention prerequisites, when not to use it, or how it compares to other tools like model_list or solutions_list. The agent must infer usage context from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.