root_aitken

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns a dict with the accelerated root, iteration table, and convergence flag, which is helpful. However, it does not mention potential failure modes (e.g., divergence, invalid math expression), how rounding is applied, or whether the method checks the convergence of the original iteration. It is transparent about the output but lacks deeper behavioral caveats.

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 concise and well-structured. It opens with a one-sentence summary, then uses an Args block to list parameters and a Returns block to describe the output. Every sentence contributes necessary information with no filler. The format is front-loaded and easy to scan.

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 numerical method tool with no output schema, the description provides a reasonable overview: it names the algorithm, lists all inputs with meanings, and explains the return value. It lacks some context like the expected syntax of g_str (e.g., variable name, allowed functions) or preconditions for the method to converge. But given the tool's moderate complexity and sibling context, this is largely sufficient.

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 coverage is 0% (no descriptions in the input schema), so the description must compensate. It fully compensates by providing a clear, one-line meaning for every parameter: g_str (math expression), x0 (initial guess), tol (convergence tolerance), max_iter (maximum iterations), and precision (rounding digits). This adds meaningful semantic context beyond the schema's 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 opens with a specific, action-oriented statement: 'Accelerate fixed-point iteration with Aitken's delta-squared method.' This clearly identifies the tool's purpose and method, distinguishing it from siblings like root_bisection, root_newton_raphson, or root_punto_fijo. It names the exact resource (fixed-point iteration) and the technique (Aitken's delta-squared).

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 implies usage through 'Accelerate fixed-point iteration' but does not explicitly state when to prefer this over alternatives, such as when convergence is slow or when a fixed-point method exists. It also does not mention any prerequisites or exclusions. There is no reference to sibling tools, so guidance is only implicit.

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