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wait_for_video

Poll video retrieve until delivered or failed. Backs off on 502.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queue_idYes
max_pollsNoMax polls, default 120
interval_msNoPoll interval ms, default 5000

Schema Changelog

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

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and provides useful behavioral context: it polls until a terminal state and backs off on HTTP 502. It does not disclose handling of other error types or what occurs when max_polls is exhausted, but these are secondary for a poll tool.

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?

Two sentences with no filler. The first sentence states the core purpose and terminal condition; the second adds a valuable retry behavior. Every word contributes to the agent's understanding.

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?

The description is adequate for a simple poll loop: it states purpose, terminal conditions, and one error-handling behavior, with parameter defaults in the schema. However, it omits what 'delivered' or 'failed' map to in the response, behavior when max_polls runs out, and there is no output schema to explain return values. These gaps matter for an agent deciding whether this is the right tool.

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?

Schema coverage is 67%; max_polls and interval_ms already have descriptions. The description's 'Poll ... until ...' implies queue_id identifies the video retrieve operation, which adds some context to the undocumented parameter, but it does not fully compensate for the missing queue_id semantics.

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 states a specific action ('Poll video retrieve') and a terminal condition ('until delivered or failed'). It clearly implies the tool waits on the video retrieval process, distinguishing it from the direct video_retrieve sibling, though it doesn't explicitly name the alternative.

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?

Usage is implied: the agent should call this when it wants to wait for a previously requested video retrieval to finish. However, there is no explicit guidance on when to prefer this over direct polling of video_retrieve or other siblings, and no exclusions or alternatives are named.

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