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sdiehl
by sdiehl

simplify_expression

Simplify mathematical expressions using SymPy's functionality in the Symbolic Algebra MCP Server. Input an expression key to reduce complexity and obtain a streamlined result.

Instructions

Simplifies a mathematical expression using SymPy's simplify function.

Args:
    expr_key: The key of the expression (previously introduced) to simplify.

Example:
    # Introduce variables
    intro("x", [Assumption.REAL], [])
    intro("y", [Assumption.REAL], [])

    # Create an expression to simplify: sin(x)^2 + cos(x)^2
    expr_key = introduce_expression("sin(x)**2 + cos(x)**2")

    # Simplify the expression
    simplified = simplify_expression(expr_key)
    # Returns 1

Returns:
    A key for the simplified expression.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expr_keyYes
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool uses SymPy's simplify function, which implies mathematical simplification behavior, and the example shows it returns a simplified result (e.g., 'sin(x)^2 + cos(x)^2' simplifies to 1). However, it doesn't cover aspects like error handling (e.g., invalid expr_key), performance (e.g., complexity for large expressions), or side effects (e.g., whether it modifies state). The description adds some behavioral context but leaves gaps.

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 appropriately sized and front-loaded: the first sentence states the purpose clearly, followed by structured sections for Args, Example, and Returns. Each sentence earns its place by providing essential information without redundancy. The example is concise and illustrative, and the overall structure enhances readability.

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 moderate complexity (mathematical simplification with one parameter), no annotations, and no output schema, the description is partially complete. It covers the purpose, parameter semantics, and provides an example, but lacks details on return values beyond a brief note ('Returns: A key for the simplified expression'), and doesn't address error cases or integration with sibling tools. For a tool in a math-focused server with many siblings, more contextual guidance would be beneficial.

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 description adds significant meaning beyond the input schema, which has 0% description coverage. It explains that 'expr_key' is 'The key of the expression (previously introduced) to simplify,' clarifying its role and dependency on prior steps (e.g., using 'introduce_expression'). This compensates well for the schema's lack of documentation, though it doesn't detail format constraints or validation rules for the key.

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: 'Simplifies a mathematical expression using SymPy's simplify function.' It specifies the verb ('simplifies') and resource ('mathematical expression'), and mentions the underlying library (SymPy). However, it doesn't explicitly differentiate from sibling tools like 'quantity_simplify_units' or 'substitute_expression', which may also involve simplification or expression manipulation.

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

Usage Guidelines3/5

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

The description implies usage through the example, showing that it operates on an expression key introduced via 'introduce_expression'. It doesn't provide explicit when-to-use guidance vs. alternatives (e.g., 'quantity_simplify_units' for unit simplification or 'substitute_expression' for substitution), nor does it mention prerequisites like needing to introduce variables first. The example suggests context but lacks clear exclusions or comparisons.

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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