Automatically stretches an image's tonal range to improve contrast, optionally targeting a specific layer by name or image index.
Automatically stretch the tonal range of an image (auto levels / auto stretch contrast).
Parameters:
image_index: Index of the target image (default 0)
layer_name: Name of the layer to adjust; defaults to active layer
Returns status dict.
| Name | Required | Description | Default |
|---|---|---|---|
| layer_name | No | ||
| image_index | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It says the tool adjusts a layer and returns a status dict, but it does not disclose whether the change is destructive or undoable, how the active layer default interacts with the target image, or what conditions cause errors.
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 short and front-loaded with the operation, followed by a compact parameter list and return note. Every sentence conveys useful information and there is no redundant wording.
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 two-parameter image adjustment tool, the description covers the operation, parameters, defaults, and return type. However, with no annotations and no output schema, it leaves out mutation safety, undo behavior, and the contents of the status dict, so it is only minimally complete.
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 0%, so the description must compensate. It documents both parameters with useful meaning beyond the schema titles: image_index identifies the target image and defaults to 0, while layer_name identifies the layer to adjust and defaults to the active layer.
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 a specific verb and resource: automatically stretch the tonal range of an image, with the synonyms auto levels and auto stretch contrast. This is clear, but it does not explicitly distinguish the tool from nearby siblings such as adjust_curves or adjust_brightness_contrast.
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 states what the operation does but gives no when-to-use guidance. It does not mention alternatives like adjust_curves or adjust_brightness_contrast, nor does it say when auto levels is preferable or inappropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.