dynamic_2d_nonhomogeneous_solve

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

With no annotations, the description carries the full burden. It does disclose the return shape (a dict with classification, particular solution, analytic solution, trajectories, and a [dict, Image] variant when plot=True), which is valuable. It says nothing about error conditions, when an analytic solution is unavailable, or performance/step-size constraints.

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?

Front-loaded with the governing equation, then cleanly organized into Args and Returns sections with grouped parameter lines. Little waste, though the grouping shorthand (e.g., 'x_min..y_max') is compact to the point of requiring schema lookup for exact names.

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 17-parameter tool with no output schema and no annotations, the description does substantial work: it covers parameter meaning and return values including the plot variant. Remaining gaps are what 'classification' contains and solver/step-size caveats, but an agent has enough to invoke it correctly.

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, and it does: it maps all 17 params into meaningful groups (a–d as matrix entries, e/f as forcing components with a format example like "sin(2*t)", x0/y0/t0/t_fin/h as integration settings, x_min..y_max and cantidad_trayectorias as portrait settings). The grouping is terse and omits defaults or validity ranges, so it is not exhaustive.

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 gives a specific verb and resource ('Solve the non-homogeneous 2D system X' = A·X + B(t)') and states the scope ('constant or time-varying forcing'), which clearly identifies the mathematical problem. However, it never explicitly names or contrasts with the close siblings (dynamic_2d_linear_solve, dynamic_2d_nonlinear_solve, dynamic_2d_conservative_solve), leaving sibling differentiation to inference from 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?

Usage is only implied: the equation form makes it clear this tool is for non-homogeneous forced 2D systems, but there is no explicit when-to-use/when-not guidance and no mention of the alternative 2D solvers an agent should pick instead. Adequate but with a clear routing gap for a family of five similar dynamic_2d tools.

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