video_complete
POST /v1/video/complete — optional cleanup after retrieve.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| queue_id | Yes | Queue id from video_queue / generate_video |
POST /v1/video/complete — optional cleanup after retrieve.
| 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?
With no annotations, the description carries the full burden. 'Optional cleanup' hints at side effects but does not say whether it deletes data, frees capacity, is idempotent, or requires preconditions. The actual behavior after calling retrieve is not disclosed.
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?
A single front-loaded sentence with no filler words and easy to scan. It loses a point only because the brevity sacrifices semantic completeness, though that is largely a completeness concern.
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?
For a lifecycle tool with no annotations and no output schema, the description leaves major gaps: side effects, return value, idempotency, and relationship to video_retrieve are all unstated. The single parameter is covered, but the operation itself is under-specified.
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%, so the baseline is 3; the description adds no parameter meaning beyond the schema. It does not reinforce that queue_id is the identifier returned by video_queue or generate_video.
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 names the endpoint and says 'optional cleanup after retrieve,' which gestures at the action but never states what 'complete' actually does or what resource is affected. It does not clearly distinguish itself from siblings like video_retrieve or wait_for_video beyond the vague 'after retrieve' timing.
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?
It provides some usage context by indicating the tool is optional and should follow retrieve. However, it gives no when-not-to-use guidance, no alternatives, and no conditions that would make cleanup necessary or unnecessary.
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.