find_roots

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

No annotations exist, so the description bears the full disclosure burden. It usefully reveals the method-selection logic (bracket vs x0) which is real behavioral context, but it is silent on convergence, failure behavior, or what happens when conflicting/missing method arguments are given.

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?

A single front-loaded sentence with zero filler. Every clause (root type, two methods, their triggering arguments) earns its place.

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 numerical tool with no annotations and no output schema, key information is missing: the expression syntax, the role of params and variable, and mutual exclusivity of bracket vs x0. An agent could invoke it but would be guessing on several required details.

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% across all 5 parameters. The description explains the meaning of only bracket and x0 (method routing); it says nothing about expr syntax, params, or the variable default, leaving the majority of parameters undocumented in both schema and prose.

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?

States a specific verb and resource ('Find a scalar root of an expression') and even names the numerical methods (brentq, Newton's method), which distinguishes it from symbolic siblings like symbolic_solve. It does not explicitly contrast itself with symbol approach, so it stops short of full sibling 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?

The parentheticals imply the selection rule: supply a bracket to use brentq, supply x0 to use Newton's method. That is useful implied guidance, but it never says when to prefer this tool over symbolic_solve or optimize_minimize, nor any exclusion/precondition.

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