Look up RunAPI pricing for the gpt-image-2 model line by action or model slug, so you can check image generation or editing costs before starting a task.
Look up RunAPI pricing for the gpt-image-2 model line.
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Model slug. Defaults to the line's primary model. | |
| action | No | Endpoint name. Defaults to the endpoint that offers the model. |
Changes observed during successful MCP inspections.
v0.2.0Input schema / additionalPropertiesRemoved value: -falseInput schema / properties / model / enumRemoved value: -[
- "gpt-image-2"
-]v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It implies a read-only lookup but never confirms it, and says nothing about auth requirements, rate limits, or what the returned pricing actually represents (per image, per token, currency). For a cost-information tool these are material gaps.
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 zero filler. It is efficient, though its brevity is partly the cause of the missing behavioral and usage context rather than pure economy.
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 no annotations, so the description is the only source of behavioral context, yet it omits return-value shape and usage conditions. The model-scoping to gpt-image-2 also under-describes a tool whose model parameter accepts other slugs, leaving the agent with an incomplete picture.
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 100%, so both parameters (model, action) are already documented with defaults and an enum for endpoint names, making 3 the baseline. The description adds no syntax or default behavior beyond the schema, and only loosely references the model line without mentioning the action enum.
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?
States a specific verb and resource ("Look up ... pricing") and scopes it to the gpt-image-2 model line, so the agent can distinguish it from siblings like edit_image or get_task. However, the scoping to one model line slightly conflicts with the parameterized model input, so it is clear but not perfectly precise.
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 description never states when to call this tool versus alternatives — e.g., whether to check pricing before invoking edit_image/text_to_image, or how it relates to get_task. No prerequisites, no exclusions, no routing guidance is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.