extract_features

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

No annotations are provided, so the description must fully convey behavior. It states it 'extracts' and returns JSON but does not indicate whether it's read-only, requires specific permissions, or handles errors. Behavioral traits beyond the basic operation are missing.

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 paragraphs for purpose, use cases, parameters, and returns. It is slightly verbose but every sentence adds value. No unnecessary 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?

With 2 parameters and an output schema present, the description explains the return format (JSON with vectors and statistics) and lists features. It does not mention prerequisites or edge cases, but is sufficient for typical use.

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%, but the description provides detailed parameter information. It explains 'image_path' as path, and 'features' lists 22 available feature names with categories, adding significant 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 'Extract raw feature vectors from an image' with a specific verb and resource. It lists use cases and available feature categories, distinguishing it from sibling tools like 'analyze_image' or 'get_dominant_colors'.

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 use cases (custom ML pipelines, database indexing) but does not explicitly state when to use this tool versus alternatives or provide exclusions. Usage is implied but not clarified.

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