Permanently delete the rendered image(s). This action is not reversible, please be sure before deleting.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Unique ID of the image project. This value is returned by all of the POST APIs that create an image. |
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 carries the full burden of disclosing behavioral traits. It effectively communicates that the operation is permanent and irreversible, which is the most critical trait for a delete operation. However, it does not mention other behaviors such as idempotency, error handling (e.g., behavior on nonexistent ID), or any side effects on related resources. For a simple delete, this is reasonably transparent but leaves room for more detail.
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: two short sentences with no filler. The key action ('permanently delete') is front-loaded, and the important caution about irreversibility follows immediately. Every word earns its place, and the structure supports quick comprehension by an agent.
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 tool with a single simple parameter, no output schema, and a clear destructive action, the description covers the essential context: what it does and its irreversibility. It does not explain return format or error behavior, but those are not specified by an output schema and may be less critical for a delete. The lack of any guidance on retrieving the ID (though present in the schema) is minor. Overall, it is adequate for the tool's simplicity.
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 schema description coverage is 100% – the 'id' parameter is fully described in the schema, including an example and the note that it is returned by POST APIs. The tool description adds no additional meaning about the parameter beyond what the schema already provides. Since the schema fully covers it, the baseline of 3 is appropriate.
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 ('Permanently delete') and the resource ('the rendered image(s)'). It distinguishes itself from sibling delete tools (audio_projects_delete, video_projects_delete) primarily through the tool name and the word 'image', but the description itself does not explicitly reference siblings or contrast with them. This is clear and specific, but lacks explicit differentiation.
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?
There is no guidance on when to use this tool versus alternatives. While the name indicates it is for image projects, the description gives no context about when deletion is appropriate, prerequisites (e.g., whether the project must exist), or any mention of other tools that might be relevant (like image_projects_retrieve_details to confirm the ID). The usage context is only implied by the action itself.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Most generation tools target distinct media types or effects (e.g., clothes changer, head swap, lip sync), but several boundaries blur: ai_image_editor_create_image is a generic edit tool that overlaps conceptually with ai_face_editor_edit_image, ai_image_upscaler_create_image, and background remover. The wait_for_*_project helpers also overlap functionally with the *_projects_retrieve_details status tools, and ai_voice_cloner_create_audio vs. ai_voice_generator_create_audio are easy to confuse by name.
Naming conventions are mixed: many tools follow ai_<product>_create_<media>, but others are product-first (animation_create_video, body_swap_create_image) and resource-group tools follow a different noun_verb pattern (audio_projects_retrieve_details, video_projects_delete). Verbs are inconsistent too (create_image, edit_image, detect_faces, retrieve_details, wait_for, fetch), so an agent cannot reliably predict the next tool name.
At 44 tools, the set is heavy: it includes 27 generation tools plus three wait helpers, three status retrieval tools, three delete tools, three fetch helpers, and upload/ping utilities. While the underlying product is broad, many helpers could be consolidated, and the overall surface exceeds the range where each tool earns a clear place.
The lifecycle is mostly covered for image, video, and audio projects: create, poll/retrieve, fetch download, delete, and file upload/presigned-URL generation are all present. However, there is no project listing or cancel operation, and face detection only has detect/details with no delete or wait helper, leaving some workflow gaps an agent must work around.