openai_edit_image

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden, and it discloses almost nothing beyond the verb. It does not state that the operation produces a new local file (implied only by the undocumented 'filename' parameter), whether the source image is modified in place, what the mask actually constrains, or what happens on failure. With 14 parameters and zero annotation coverage, this is a substantial gap, though the output schema covers return values.

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?

Two short sentences with the core operation front-loaded and no filler. The text is efficient, but its brevity reflects under-specification rather than disciplined editing.

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 14-parameter mutation tool with no annotations, this definition is far too thin; an agent cannot confidently populate most arguments or predict side effects. The presence of an output schema removes the need to describe return values, but the input-side and behavioral gaps remain large.

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% across 14 parameters, so almost all semantics rest on the description, which explains only the mask. Critical knobs such as model, size, quality, output_format, output_compression, background, frame_count/frame_duration_ms (suggesting animation support), complexity, and safety_identifier are left entirely unexplained beyond their titles and defaults.

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 gives a specific verb and resource: 'Edit one local image with a GPT Image model,' which clearly separates it from openai_generate_image by naming editing rather than generation. It also notes the mask input. It stops short of explicitly routing the agent away from the sibling image generator, so it is clear but not fully differentiated.

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 only guidance-like sentence is 'An optional alpha mask may be supplied,' which is a parameter note rather than a when-to-use statement. There is no indication of when an agent should choose this over openai_generate_image, what prerequisites exist (API key, model availability, local file access), or what cases should use generation instead.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.