gpu_eigen_batch

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

With no annotations, the description must cover behavioral traits. It mentions 'GPU fallback' but fails to explain what triggers it, performance implications, or any side effects. The tool's read or write nature is not indicated.

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?

One concise sentence front-loading the verb and primary purpose. No extraneous words, ideal length for quick scanning.

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?

Despite having an output schema, the description lacks details on input shape constraints, error handling, return format, and GPU requirements. Given the complexity of batched eigen decompositions, the description is insufficient for correct invocation.

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%, and the description adds no information about the three parameters (matrices, symmetric, eigenvalues_only). The agent must rely solely on parameter names, which may be insufficient for correct usage.

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 action ('compute'), resource ('batched eigen decompositions'), and mentions GPU fallback, distinguishing it from the sibling eigen_decomp. However, 'GPU fallback' is somewhat ambiguous, lacking details on when fallback occurs.

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?

No guidance on when to use this tool versus alternatives like eigen_decomp. The description does not mention prerequisites, performance considerations, or when not to use it.

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