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

Annotations already declare this is a safe, read-only, closed-world calculation. The description adds that derivatives may be computed automatically via SymPy, which is useful behavioral context. However, it does not disclose convergence behavior, failure cases, or what happens when max_iterations is reached.

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 a single front-loaded sentence with no wasted words. It is perhaps too terse for the tool's complexity, but it is structurally efficient.

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?

An output schema exists and annotations cover safety, so the description need not explain return values. Still, for a numerical solver with five parameters and several root-finding siblings, the description lacks enough guidance on convergence, failure, and alternative tools to be fully complete.

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 100%, so the schema fully documents parameters. The description adds only the auto-derivative behavior, which is also stated in the derivative parameter's schema description. Baseline 3 is appropriate because the schema carries most of the semantic load.

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 numerical method (Newton-Raphson) and resource (root finding), which clearly distinguishes it from generic siblings like calculator_basic. It does not explicitly contrast with calculator_root_finder or calculator_numerical_solve, but the algorithm name gives useful differentiation.

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 guidance on when to choose this over calculator_root_finder, calculator_numerical_solve, or calculator_root. The mention of optional/auto derivative implies a use case, but no conditions, prerequisites, or exclusions are stated.

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