error_level_analysis

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

No annotations are provided, so the description must fully disclose behavioral traits. It describes the method (resaving, analyzing error patterns) but does not state whether the original image is modified, whether temporary files are created, permission requirements, or other side effects.

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 front-loaded with the purpose and method, then lists arguments efficiently. It avoids redundancy, though the Args section partially repeats schema information.

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?

Output schema exists, so return values need not be explained. However, with no annotations and no usage guidance relative to siblings, the description leaves gaps about when to choose this forensic method and whether it has side effects.

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: image_path as an absolute path and quality as the primary JPEG quality (1-100, default 90), adding meaning beyond the schema's bare titles and default.

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 a specific verb and resource: 'Perform multi-scale Error Level Analysis (ELA) on an image.' It further specifies resaving at multiple JPEG quality levels and consistency analysis, clearly distinguishing this technique from sibling forensic methods.

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 mentions 'for better AI detection' but does not state when to use this tool versus alternatives like noise_map or frequency_analysis. No explicit when-to-use, exclusions, or 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.