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sympy_sympify

Convert string expressions into SymPy symbolic expressions for symbolic mathematics, algebra, calculus, and equation solving.

Instructions

Convert a string to a SymPy expression.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
exprYesString expression to convert

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions conversion but doesn't specify error handling (e.g., for invalid strings), performance implications, or what the output looks like (though an output schema exists). For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior beyond the basic operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear sentence: 'Convert a string to a SymPy expression.' It is front-loaded with the core purpose, has zero wasted words, and is appropriately sized for a simple tool. Every part of the sentence earns its place by directly stating the tool's function.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (one parameter, 100% schema coverage, output schema exists), the description is minimally adequate. It states the basic operation but lacks details on usage context, error handling, or behavioral traits. With an output schema, it doesn't need to explain return values, but the absence of annotations means more behavioral context would be helpful for completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 100% description coverage, with the 'expr' parameter documented as 'String expression to convert.' The description adds no additional meaning beyond this, such as examples of valid strings or formatting constraints. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Convert a string to a SymPy expression.' It specifies the verb ('convert') and resource ('string to SymPy expression'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like sympy_evaluate or sympy_expr_type, which might have overlapping functionality, so it doesn't reach the highest score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. With many sibling tools like sympy_evaluate or sympy_expr_type that might handle similar input, there's no mention of specific use cases, prerequisites, or exclusions. It states what the tool does but not when it's the appropriate choice.

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

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