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get_expression_info

Analyze the structure of any Wolfram expression, returning head, full form, depth, leaf count, and type flags for debugging and comprehension.

Instructions

Get detailed structural information about a Wolfram expression.

Like Python's type() on steroids - shows Head, FullForm, tree structure, depth, leaf count, and type checks (NumericQ, ListQ, etc.)

Args: expression: Wolfram Language expression to analyze

Returns: Structural information: head, full form, depth, leaf count, type flags

Example: get_expression_info("{{1,2},{3,4}}") -> {head: "List", depth: 3, dimensions: [2,2]} get_expression_info("Sin[x] + Cos[x]") -> {head: "Plus", leaf_count: 3}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expressionYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

With no annotations, the description carries full burden. It lists the returned information (head, depth, etc.) but does not disclose potential limitations, side effects, or required permissions. It is adequate but not comprehensive.

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 concise, well-structured with Args and Returns sections, and includes helpful examples. Every sentence contributes value without redundancy.

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

Completeness4/5

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

Given the simple parameter set and existence of an output schema, the description is largely complete. It covers the tool's purpose, inputs, outputs, and examples, though it could mention edge cases or expression length limits.

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

Parameters4/5

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

The single parameter 'expression' is described as 'Wolfram Language expression to analyze', adding meaningful context beyond the schema type string. The description enhances understanding, though additional examples of valid expressions would be even better.

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 it gets detailed structural information about a Wolfram expression, using a specific verb and resource. It includes examples that clarify the output, but does not explicitly distinguish from sibling 'get' tools like get_symbol_info.

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 lacks guidance on when to use this tool versus alternatives. No when-to-use or when-not-to-use criteria are mentioned, leaving the agent to infer usage solely from the description.

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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