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wait_for_image_project

Poll an image project until it completes, errors, is canceled, or times out. Returns the final project JSON and, when complete, attempts to inline image downloads for Inspector or compatible clients. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes
timeout_secondsNo
max_inline_downloadsNo
poll_interval_secondsNo
max_bytes_per_downloadNo
include_inline_downloadsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A3.8/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden and handles it well: it discloses polling until terminal states, attempts to inline image downloads, returns sanitized download fields, and warns that URLs must be used exactly as returned. This is substantive behavior beyond what the schema offers.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and well-structured: the core purpose is front-loaded, followed by output details and a critical usage warning. Every sentence earns its place, with no redundant or misleading filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must clarify return behavior; it does explain the final JSON and sanitized download fields. However, it omits how parameters affect polling and downloading, what happens on timeout/error at the response level, and how this relates to retrieval/fetch tools. This is a meaningful gap given six parameters and no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

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 compensate: none of the six parameters (id, timeout_seconds, max_inline_downloads, poll_interval_seconds, max_bytes_per_download, include_inline_downloads) are explained. The only tangential mention is 'times out,' which does not clarify parameter behavior. With low schema coverage, the description should have provided parameter guidance but does not.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Poll') and resource ('image project') and specifies the terminal conditions: completes, errors, is canceled, or times out. This clearly distinguishes the tool from sibling wait_for_audio_project and wait_for_video_project, as well as from non-blocking retrieval tools like image_projects_retrieve_details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool—after creating an image project and when a blocking wait is needed—and mentions suitability for Inspector or compatible clients. However, it does not explicitly contrast this with siblings such as wait_for_video_project or image_projects_retrieve_details, leaving the when/when-not decision largely implicit.

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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TDQS

A3.6/5.0
Disambiguation3/5

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 Consistency2/5

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.

Tool Count2/5

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.

Completeness3/5

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.