Poll a video project until it completes, errors, is canceled, or times out. Returns sanitized download fields. Use exact_download_urls[n] or downloads[n].url exactly as returned; do not shorten it, remove query parameters, or append expiration metadata.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | ||
| timeout_seconds | No | ||
| max_inline_downloads | No | ||
| poll_interval_seconds | No | ||
| include_inline_downloads | No |
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?
With no annotations, the description carries the transparency burden. It discloses the polling nature, the four terminal states, that download fields are sanitized, and explicitly warns against modifying returned URLs. This is useful behavioral context, though it does not describe error response shapes or side effects.
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 compact, with the core polling behavior front-loaded. Every sentence contributes: the action/terminal states, the output type, and a critical URL-handling instruction. There is no redundancy or filler.
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?
The description captures the main flow and includes a strong output warning, but for a tool with five parameters, no annotations, and no output schema, it leaves optional parameter semantics unexplained and does not fully describe what happens on failure or timeout. It is adequate for default-parameter use, but not complete.
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 0%, and the description does not explain any of the five parameters. While names and defaults make timeout_seconds and poll_interval_seconds inferable, max_inline_downloads and include_inline_downloads lack meaningful explanation, and id is not described as referencing a video project.
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 ('Poll') and a specific resource ('a video project'), and clearly distinguishes this from the sibling wait_for_image_project and wait_for_audio_project tools. It also states the terminal states and the kind of output returned.
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 the tool is used after a video project has been initiated, in order to wait for it to finish. However, it does not explicitly say when to prefer this over alternatives like video_projects_retrieve_details or how it relates to the other wait_for_* tools.
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.