video_retrieve
POST /v1/video/retrieve — power-user status/media poll.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | Queue id from video_queue / generate_video |
POST /v1/video/retrieve — power-user status/media poll.
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | Queue id from video_queue / generate_video |
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not state whether the operation has side effects, whether it requires special permissions ('power-user' hints but doesn't specify), whether it consumes the queue, or what the response contains. The word 'poll' suggests a read-only pattern, but this is not explicit.
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 and front-loaded with the endpoint and a rough functional label. It avoids filler, though 'power-user' is somewhat vague and the terse style sacrifices useful context.
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?
There is no output schema, and the description does not explain what the tool returns, such as status fields or media URLs. Given the many sibling tools focused on video generation and status, the description is not complete enough for an agent to confidently decide when to call this tool and how to interpret its response.
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 coverage is 100% for the single parameter queue_id, and the schema already explains that it comes from video_queue or generate_video. The description adds no additional semantic meaning about the parameter, so the baseline of 3 applies.
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 identifies a specific action ('retrieve'), the resource ('video'), and the purpose ('status/media poll'). It is clear enough to understand the tool's core function, though it does not explicitly contrast it with sibling tools like get_generation_status 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'status/media poll' implies this tool is for polling video status or media by queue_id, which provides some usage context. However, it gives no explicit guidance about when to choose this over get_generation_status, wait_for_video, or other sibling tools, and no exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.