evaluate_numeric

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

No annotations are provided, so the description carries the burden. It says it returns a numeric result and mentions precision, but does not disclose error behavior, what happens if variables are missing, or whether it has side effects. It is adequate but lacks depth beyond the core function.

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 somewhat long but well-organized with clear sections, a workflow reminder, examples, and no filler. The length is justified by the need to clarify the correct context and parameter usage.

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?

The tool has an output schema and the description covers workflow, arguments, and return values with examples. It is complete for a straightforward evaluation tool, though it could mention edge cases like division by zero or invalid syntax.

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?

The input schema has zero descriptions, but the tool's description includes an Args section explaining each parameter (expression, values, precision) and provides two examples showing exactly how to pass values as a mapping. This fully compensates for the schema gap and adds practical meaning.

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 defines the tool as 'Evaluate expression numerically' with a specific verb and resource. It explicitly contrasts with symbolic work, distinguishing it from sibling symbolic tools like derivation_simplify and solve_inequality.

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 provides an explicit workflow: 'USE AFTER SYMBOLIC WORK', with numbered steps telling the agent to use SymPy-MCP first, then print_latex_expression, and finally this tool. It also states the tool is for 'final numeric values', making when-to-use clear.

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