root_punto_fijo

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

With no annotations provided, the description carries the full burden. It explains what the tool does, reports the Lipschitz check, and enumerates the return fields: 'fixed point, the Lipschitz check, iteration table and convergence flag.' This is substantial behavioral disclosure for a numerical computation tool.

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 purpose statement, convergence note, and Args/Returns sections. It is concise, front-loaded, and every sentence adds value—no fluff or repetition of schema fields.

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 5-parameter tool with no output schema and no annotations, the description is quite complete: it explains the mathematical method, convergence condition, and return contents. A minor gap is not detailing the exact format of the iteration table, but this is not essential for using the tool.

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 Args section adds clear meaning to every parameter (g_str, x0, tol, max_iter, precision) beyond the schema's bare names and types. For example, g_str is described as 'Math expression for the iteration function g(x)' and tol as 'Convergence tolerance.' This fully compensates for the 0% schema description coverage.

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 'Find a fixed point of g(x) = x with the fixed-point iteration method.' This is a specific verb+resource and distinguishes from sibling root-finding tools like root_newton_raphson and root_bisection by naming the exact 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?

The description provides a clear convergence condition: 'Converges when |g'(x)| < 1 near the root (Lipschitz check is reported).' This effectively tells users when the method is appropriate, though it does not explicitly compare with alternative root-finding tools.

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