derivation_prepare_for_optimization

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the conversion and return format but does not disclose whether the operation has side effects, requires an active session, or has any prerequisites or error conditions. This lack of behavioral caveats is a significant gap for a tool with no annotation support.

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 purpose, a numbered workflow, a Returns line, and a detailed example. It is front-loaded and efficient, though the example is somewhat elaborate. Overall, it is concise and well-organized.

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?

The description offers a detailed example of the return object and situates the tool in a workflow, which is helpful. However, it lacks an explanation of session_id, does not specify how the derivation result is obtained (e.g., from the session or current state), and omits edge cases. For a tool with one optional parameter and an output schema present, it is moderately complete but not fully self-contained.

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 defines only session_id, and the description never mentions this parameter or explains its purpose. Since schema description coverage is 0%, the description should compensate but does not, leaving the agent to infer from the default value or context. The example arbitrarily omits arguments, adding ambiguity.

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 function: preparing derivation results for optimization solvers by converting NSForge symbolic formulas into optimizer input. It uses specific verbs ('prepare', 'convert') and a resource (optimization solver format), and distinguishes itself from sibling derivation tools through its optimization-specific purpose and concrete example.

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 a clear three-step workflow indicating when to use the tool: after NSForge derivation and before sending to an optimizer. This gives explicit context but does not mention when not to use it or name alternative tools, so it falls short of a 5.

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