Compute totals

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

Annotations declare readOnlyHint=true, so the safety profile is already known. The description adds context that this involves OpenMDAO, implying computational work, but it does not disclose return format, performance characteristics, or potential side effects beyond the annotation. No contradiction with annotations.

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, efficient sentence with no filler. Every word contributes to stating the tool's purpose.

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?

While an output schema exists (reducing the need to explain return values), the description omits essential context for correct invocation: what 'of' and 'wrt' mean, how session_id works, and when to choose this over sibling tools. The tool appears complex (4 params, array inputs) yet the description is minimal.

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%, meaning the description adds no meaning to the parameters 'of', 'wrt', 'session_id', or 'return_format'. It fails to compensate for the schema's lack of parameter explanations, leaving the agent to guess what these arrays and enums represent.

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 states a clear verb ('compute') and resource ('total derivatives') with a specific framework ('OpenMDAO'). It distinguishes from siblings like run_cycle by focusing on derivative computation, though it doesn't explicitly name alternatives.

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 such as run_cycle or sweep_inputs. It does not mention conditions, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.

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