check_dimensions

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

No annotations are present, so the description carries the transparency burden. It discloses that it uses SymPy units and shows concrete return-value examples. Since this is a read-only analysis tool, the lack of side-effect notes is acceptable, though failure modes are not described.

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 uses a compact docstring structure with Args, Returns, and Examples. Every section adds value, and the examples are directly usable. There is no redundant text.

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 two-parameter analysis tool with an output schema available, the description is largely complete. It defines both parameters, gives example inputs/outputs, and notes the underlying library. Minor gaps such as error handling or supported expression grammar are acceptable given the simplicity.

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?

With 0% schema description coverage, the description compensates by explaining both parameters. 'units_map' has a clear format with an example, and 'expression' is described as the expression to check. The examples illustrate valid inputs, though expression syntax could be more explicit.

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 the action ('Check dimensional consistency of an expression') and names the implementation (sympy.physics.units). It is distinct from sibling tools like validate_expression or solve_inequality, and the examples further clarify the expected output.

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 intended use is implied by the purpose and examples, but there is no explicit guidance about when to use this tool versus alternatives, and no exclusions or prerequisites are mentioned.

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