Fetch paginated example rows for an evaluation dataset. Provide the dataset ID and optional cursor token to inspect records, supporting debugging and monitoring of agent behavior.
List example rows for a dataset (paginated).
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| limit | No | ||
| cursor | No | opaque pagination token from previous response next_cursor; pass through unchanged |
Changes observed during successful MCP inspections.
v0.3.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate safety. It adds the pagination behavior, which is useful but already reflected in the tool name and cursor parameter. It does not explain what a response contains or whether results are bounded.
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 entire description is one compact, front-loaded sentence with no filler. It conveys the core action, the resource, and the pagination trait efficiently.
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 paginated list operation, the description is mostly adequate, but it omits the meaning of the required 'id' parameter and any indication of the response shape. With no output schema, some additional detail about the returned rows would improve completeness, though the core call is understandable.
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 only 33%, so the description must compensate for undocumented parameters. It does not explain that 'id' refers to the target dataset or clarify how 'limit' controls page size, leaving the agent to infer these meanings from the tool name alone.
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 uses a specific verb ('List'), a clear resource ('example rows for a dataset'), and notes pagination. This distinguishes it from sibling tools like invariance_eval_dataset_list and invariance_eval_dataset_get, which operate on datasets themselves rather than their example rows.
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 on when to use this tool versus alternatives, such as invariance_eval_dataset_get or invariance_eval_dataset_append_example. The description does not mention any preconditions, output size limits, or cases where another tool would be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.