POST /v1/image/generate — intent tool for image generation. Rejects enable_web_search. Returns images plus cost trace.
| Name | Required | Description | Default |
|---|---|---|---|
| n | No | Variants 1-4 | |
| model | Yes | Live image model id from get_models | |
| prompt | Yes | ||
| quality | No | ||
| resolution | No | ||
| aspect_ratio | No | ||
| hide_watermark | No | ||
| enable_web_search | No |
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. It does add useful behavioral disclosures: the tool rejects enable_web_search and returns images plus a cost trace. However, it does not disclose whether the operation is asynchronous, whether it consumes credits, or what the failure behavior might be.
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 one sentence and front-loads the endpoint and purpose. It also packs in a limitation and return value, though 'intent tool' is jargon that slightly muddies the clarity.
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?
With 8 parameters, no output schema, and no annotations, the description leaves too much unsaid. An agent would not know which values are valid for quality or resolution, whether the call blocks or returns immediately, or how the cost trace is structured.
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 description coverage is only 25%, so the description must compensate but only clarifies that enable_web_search is rejected. It does not explain how to set quality, resolution, aspect_ratio, or hide_watermark, nor valid values. The n and model descriptions come from the schema, not the description.
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 states the resource (image generation via POST /v1/image/generate) and that the tool returns images plus cost trace, which distinguishes it from the sibling generate_video. It is clear but somewhat diluted by the vague phrase 'intent tool.'
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?
No explicit guidance on when to use this tool versus alternatives such as generate_video. The only operational hint is that enable_web_search is rejected, but there is no context about prerequisites, model selection via get_models, or when generation should be preferred over other endpoints.
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.