Remove entities from a dataset (the entities themselves are not deleted).
| Name | Required | Description | Default |
|---|---|---|---|
| api_key | No | CatchAll API key. Optional if provided via x-api-key header or CATCHALL_API_KEY env var. | |
| dataset_id | Yes | The dataset ID to remove entities from. | |
| entity_ids | Yes | List of entity IDs to remove (required). |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry behavioral information. It discloses that entities are only removed from the dataset and not deleted, which is useful. However, it lacks details on side effects, permissions, or reversibility.
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, front-loaded sentence that conveys all essential information without any wasted words.
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 operation with a clear input schema and an existing output schema, the description is sufficiently complete. It could mention prerequisites like entity existence in the dataset, but the schema covers required fields.
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 100%, so all parameters are documented in the input schema. The description does not add any additional semantic meaning beyond what the schema already provides, resulting in a baseline score.
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 verb 'remove' and resource 'entities from a dataset', and clarifies that entities themselves are not deleted, distinguishing it from delete_entity. It is specific and non-tautological.
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 implies usage for removing entity associations without deletion, but does not explicitly state when to use versus alternatives like delete_entity or add_dataset_entities. No exclusions or context provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.