math_eval

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

No annotations present, so description carries full burden. It outlines supported features (precision, complex numbers, symbolic computation) and gives examples, but does not mention error handling, rate limits, or edge cases. Overall good transparency for a 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?

Description is front-loaded with purpose, but includes a block of examples that could be shortened. Still, each example adds value. Minor redundancy in listing capabilities both in prose and examples.

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?

Given complexity (supports arbitrary precision, complex numbers, symbolic ops, many functions) and no annotations, description covers features, parameter details, and usage examples fully. Output schema exists but not needed for completeness.

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 has 0% description coverage, but description explains each parameter: expression format with examples, precision default of 50, substitutions format as 'x=5,y=10'. Adds significant meaning beyond the schema.

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?

Description clearly states it evaluates mathematical expressions with specific capabilities (arbitrary precision, complex numbers, symbolic operations). It distinguishes from sibling tools like math_calculus, math_solve, etc., by focusing on direct evaluation.

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?

Description implies use for computing mathematical expressions, but does not explicitly state when to use over alternatives like math_calculus or math_solve. No exclusions or when-not guidance provided.

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