dynamic_2d_linear_solve

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

No annotations are provided, so the description carries the full burden, and it does so reasonably well: it discloses the return payload (classification, eigenvalues, nullclines, analytic solution, trajectories) and the important behavior change under plot=True (returns [dict, Image] instead of a dict). It does not mention numerical caveats (step size/stiffness sensitivity of h) or that 'analytic solution' coexists with numerical integration, which would be valuable for a solver.

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 purpose is front-loaded in the first line, followed by a structured Args/Returns block with no filler. The only minor inefficiency is the grouped argument lines (e.g. 'x0, y0, t0, t_fin, h'), which compress several distinct semantics into one clause.

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 covers both inputs and the shape of the return value, including the plot=True variant. What is still missing is sibling selection guidance and any note on integration accuracy/limits, but the core is complete enough to invoke 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% across 17 parameters, so the description must compensate and largely does: a,b,c,d are mapped to matrix entries, e,f to the forcing vector, x0/y0/t0/t_fin/h to the initial condition and integration grid, x_min..y_max to the phase-portrait window, cantidad_trayectorias to trajectory sampling, and plot to the PNG output. It is grouped rather than per-parameter and leaves the roles of t0 and t_fin versus h slightly implicit, so it stops short of a 5.

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 classify) and a precise resource scope (linear 2D system X' = A·X + B with constant matrix A), which is enough to distinguish it from the 1D solvers. However, it never differentiates itself from the close 2D siblings (dynamic_2d_nonhomogeneous_solve, dynamic_2d_conservative_solve, dynamic_2d_nonlinear_solve), which is a real ambiguity given the dense sibling cluster.

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?

There is no when-to-use, when-not-to-use, or alternative-tool guidance anywhere. With six 2D dynamic siblings, the agent is left to infer selection purely from the math formula in the opening line. Only implied usage is present.

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