root_newton_raphson

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

With no annotations, the description carries the transparency burden. It does disclose that the derivative is numeric and that the return is a dict with root, iteration table, and convergence flag. However, it lacks warnings about divergence, sensitivity to x0, or behavior when the derivative is zero.

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 efficient and well-structured with an opening sentence followed by concise Args/Returns sections. No filler, each line provides necessary information.

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 gives the essential inputs and outputs but omits important context such as potential convergence failures, need for good initial guess, and numerical derivative step size. Since there is no output schema, the return is described but not detailed enough for all usage scenarios.

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%, but the description compensates by listing every parameter with a concise meaning: func_str as 'Math expression in x', x0 as 'Initial guess', tol as 'Convergence tolerance', etc. This adds value beyond the schema titles, though it could specify allowed math syntax.

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 starts with a specific verb+resource: 'Find a root of f(x) = 0 with the Newton-Raphson method (numeric derivative).' This clearly distinguishes it from sibling root-finding tools like root_bisection and root_punto_fijo by naming the method.

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?

No guidance is provided on when to prefer Newton-Raphson over other root-finding methods. It does not mention that a good initial guess is needed, or when bisection or fixed-point might be safer. Implied only through the method name.

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