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wait_for_audio_project

Poll an audio project until it completes, errors, is canceled, or times out. Returns the final project JSON and, when complete, attempts to inline audio 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

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains that the tool polls until terminal states, attempts to inline audio downloads for Inspector or compatible clients, returns sanitized download fields, and warns against modifying returned URLs. This goes beyond the tool name and provides useful operational context.

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 three sentences with no filler. The core polling purpose is front-loaded, followed by return behavior, then a concrete URL-handling caveat. Every sentence contributes useful information.

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

Completeness4/5

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

For a polling tool with six parameters and no output schema, the description covers the main return contract: final project JSON, inline download behavior, sanitized fields, and URL integrity rules. It could be more explicit about how terminal states are represented or how download inclusion limits behave, but the essential guidance for selecting and using the tool is present.

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

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, and the description provides no input parameter semantics. It discusses output fields like `exact_download_urls[n]` and `downloads[n].url`, but does not explain `id`, `timeout_seconds`, `poll_interval_seconds`, `max_inline_downloads`, `max_bytes_per_download`, or `include_inline_downloads`. The parameter names are somewhat self-explanatory, but the description does not compensate for the absent schema documentation.

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 opens with a specific verb and resource: 'Poll an audio project until it completes, errors, is canceled, or times out.' This clearly distinguishes it from the sibling wait_for_image_project and wait_for_video_project tools by naming the resource type and the polling behavior.

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

Usage Guidelines4/5

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

The description makes the usage context clear: it is for waiting on an audio project and returning its final state. It does not explicitly name alternatives or exclusions, but the purpose is specific enough that an agent can infer when to use it versus project retrieval or creation 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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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.