super_schema

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the action ('inspect') and output ('unique shapes with counts and examples'), but does not cover critical aspects like whether it's read-only, potential side effects, performance considerations, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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 a single, well-structured sentence that efficiently conveys the tool's purpose without unnecessary words. It is front-loaded with the core action and includes key details like 'unique shapes', 'counts', and 'examples', making it highly concise and effective.

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 tool's moderate complexity (analyzing data file schemas), lack of annotations, and no output schema, the description is minimally adequate. It explains what the tool does but does not address behavioral traits, usage context, or output details. This leaves the agent with incomplete information, especially for a tool that might involve data processing nuances.

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 input schema has 100% description coverage, with the single parameter 'file' documented as 'Path to the data file'. The description adds no additional parameter semantics beyond this, such as file format expectations or constraints. Given the high schema coverage, a baseline score of 3 is appropriate, as the schema adequately handles parameter documentation.

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: 'Inspect the schema/types of a data file by finding all unique shapes (record types) with counts and examples.' It specifies the verb ('inspect'), resource ('schema/types of a data file'), and method ('finding all unique shapes with counts and examples'). However, it does not explicitly differentiate from sibling tools like 'super_validate' or 'super_query', which might have overlapping functionality, keeping it from a perfect score.

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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, exclusions, or compare it to siblings such as 'super_validate' or 'super_query', which could be relevant for schema-related tasks. This lack of contextual direction leaves the agent with minimal usage cues.

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