analyze_image

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

With no annotations, the description fully discloses the tool's behavior: it extracts statistical properties, not semantic content. It explains the analysis depth options and the output format (human-readable analysis). While it doesn't cover side effects or permissions, for a read-only analysis tool, this is adequate.

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 well-structured with a clear intro, args section, and returns. It is front-loaded with the tool's purpose. Although it is lengthy due to the preset list, the details are necessary and well-organized, earning its space.

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?

Given the complexity of the tool and the presence of an output schema, the description is complete. It covers both parameters thoroughly, explains the output clearly, and distinguishes from sibling tools sufficiently. No gaps are apparent.

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?

The schema coverage is 0%, but the description compensates fully by explaining both parameters: image_path (path to image file) and preset (with all options and their dimensions). This adds considerable meaning beyond the schema's type 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 clearly states the tool's purpose: analyzing an image's visual composition using mathematical features, extracting statistical properties like color distribution and texture, explicitly distinguishing it from content recognition. It also differentiates from sibling tools like check_image_quality and filter_by_vibe by focusing on mathematical features rather than quality or vibe.

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 provides detailed preset options and their use cases (e.g., 'best for similarity' for 'combined'), but it lacks explicit guidance on when to use this tool versus alternatives like extract_features or filter_by_vibe. It does not state when not to use it or list exclusions.

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