Retrieve experiments run on a dataset by ID or name, returning array of experiment metadata for review.
List experiments run on a dataset.
Example usage: Show me all experiments run on dataset RGF0YXNldDox
Expected return: Array of experiment objects with metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| dataset_id | No | ||
| dataset_name | No |
Changes observed during successful MCP inspections.
v1.0.38v1.0.37v1.0.15v1.0.13v1.0.1Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / datasetIdRemoved value: -{
- "type": "string"
-}Input schema / properties / dataset_idAdded value: +{
+ "type": "string"
+}Input schema / properties / dataset_nameAdded value: +{
+ "type": "string"
+}Input schema / properties / limitAdded value: +{
+ "default": 100,
+ "maximum": 500,
+ "minimum": 1,
+ "type": "number"
+}Input schema / requiredRemoved value: -[
- "datasetId"
-]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations. Description implies a read-only operation but does not explicitly state behavior such as pagination, ordering, or authentication requirements. Expected return format is mentioned but remains vague. Adequate but minimal.
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?
Brief and to the point (3 sentences plus example), but lacks structure. Could be more informative without being verbose. Adequately concise.
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 3 parameters, no output schema, and no annotations, the description is incomplete. It fails to explain parameter usage, return details, or how to identify a dataset. Agent would struggle to use the tool correctly without additional insight.
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 has 0% description coverage. Description does not explain any parameter (limit, dataset_id, dataset_name) despite the example using a dataset ID. Agent lacks understanding of how to specify the dataset or control pagination.
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?
Clearly states the verb 'List' and resource 'experiments run on a dataset'. However, it does not differentiate from sibling tool 'list-experiments-for-dataset', which appears to serve the same purpose. Lacks explicit distinction.
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 on when to use this tool versus alternatives like 'list-experiments-for-dataset' or other tools. Agent receives no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.