dynamic_2d_nonhomogeneous_solve

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

With no annotations, the description is the sole source of behavioral context. It explains the mathematical operation, the role of parameters, and the return dictionary (classification, solutions, trajectories). However, it does not disclose the numerical method, any limitations, or what the classification values mean. The lack of detail on how integration/portrait settings affect behavior leaves gaps.

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 compact and front-loaded. It opens with the core purpose, then organizes parameters into logical groups, and ends with a clear summary of return values. No wasted words; each sentence contributes.

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?

For a 16-parameter tool with no output schema and no annotations, the description gives a solid overview but lacks specifics: the exact structure of the returned dictionary, input constraints (e.g., h > 0, t_fin > t0), and the numerical method used. It is adequate for a math solver but leaves the agent uncertain about output shape and edge cases.

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%, so the description must compensate. It effectively groups and explains all 16 parameters: a, b, c, d as matrix entries; e, f as forcing components with an example; integration settings; and portrait settings. While some parameters like 'h' and 'x_min' are only briefly labeled, the description adds substantial meaning beyond the schema's bare titles and defaults.

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 specific verb ('Solve') and a well-defined resource: the non-homogeneous 2D system X' = A·X + B(t), with constant or time-varying forcing. This clearly distinguishes it from sibling tools like dynamic_2d_linear_solve and dynamic_2d_nonlinear_solve by specifying the equation form.

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 does not provide any guidance on when to use this tool versus alternatives. It does not mention exclusions, prerequisites, or suggest other tools for different scenarios (e.g., homogeneous systems, nonlinear systems). Users must infer usage from the equation alone.

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