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get_generation_status

POST /v1/video/retrieve once — JSON status or delivered MP4.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queue_idYesQueue id from video_queue / generate_video

Schema Changelog

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

  1. First observed

TDQS

A3.8/5.0
Behavior4/5

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

No annotations are provided, so the description carries the behavioral burden. It discloses the HTTP method and endpoint, the one-time call behavior, and the two possible response forms (JSON status or delivered MP4). This is reasonably transparent for a status-retrieval tool, though it omits error and authentication details.

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 a single compact sentence that front-loads the endpoint, call count, and response type. Every word contributes useful information and there is no redundant filler.

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 one-parameter status tool with no output schema, the description adequately explains what the tool does, how to invoke it, how many times to call it, and what it returns. It could be slightly more complete by naming sibling alternatives, but it is sufficient for correct invocation.

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

Parameters3/5

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

There is only one parameter and the schema already documents it fully, including its source context from video_queue/generate_video. The description adds no additional parameter-level meaning, but none is needed given the complete schema coverage.

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

Purpose4/5

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

The description clearly identifies the action and resource: it retrieves video generation status via POST /v1/video/retrieve and returns either JSON status or a delivered MP4. This conveys the core purpose well, though it does not explicitly differentiate itself from sibling tools like video_retrieve or wait_for_video.

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 word 'once' gives useful guidance that this is a single status check rather than a polling operation, and the schema ties queue_id to video_queue/generate_video. However, it does not explicitly name alternatives or state when another tool such as wait_for_video should be preferred.

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

B3.3/5.0
Disambiguation2/5

Several video tools are effectively duplicates: generate_video and video_queue both target POST /v1/video/queue, while get_generation_status and video_retrieve both call POST /v1/video/retrieve. The non-video tools are distinct, but these overlapping boundaries make it hard for an agent to choose the correct variant.

Naming Consistency3/5

Tool names are uniformly snake_case and many follow a verb_noun pattern such as create_key, list_keys, and get_models. However, the video tools use an object-first video_* pattern, and names like agent_me, chat_completions, and funding_instructions break the dominant convention.

Tool Count3/5

At 18 tools, the surface is on the heavy side, and the count is inflated by lower-level variants that duplicate agent-facing tools such as video_queue vs generate_video and video_retrieve vs get_generation_status. A leaner set could consolidate these while still covering account, key, model, image, and video workflows.

Completeness5/5

The set covers the account/key lifecycle, funding and price controls, model discovery, chat, image generation, and a full video quote/queue/status/retrieve/cleanup flow. It also provides request-trace recovery and capacity checks, so agents have no obvious dead ends for the stated domain.

Resources