noise_map

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

With no annotations, the description carries the full burden, and it does disclose the underlying algorithm (subtracting a Gaussian-blurred version) and interpretation guidance ('AI-generated images tend to have unnaturally uniform or structured noise'). It omits file-format support, failure behavior, and permission needs, but the algorithmic and interpretive disclosure is substantial.

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?

Front-loads the purpose in the first sentence, then adds mechanism and interpretation. The Args block is efficient; slight redundancy between the opening line and the third sentence, but no significant waste.

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?

An output schema exists, so return values need not be explained, and both parameters are documented. For a two-parameter analysis tool the description is nearly complete; only the routing against sibling analyses is missing.

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, and it does: image_path is specified as an absolute path, and blur_sigma is explained as a smoothing strength with a stated default of 3.0, matching the schema 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?

States a specific verb and resource ('Extract the noise pattern from an image') plus the intended goal ('help detect AI generation'). It is clearly distinguishable from siblings like error_level_analysis, frequency_analysis, and spectral_decay_analysis.

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 stated goal (AI-generation detection) implies when the tool is relevant, but there is no explicit guidance on when to prefer this over the six sibling forensic tools, nor any exclusions or prerequisites.

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