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calculate_series

Compute Taylor, Laurent, or Fourier series expansions to approximate functions near a point, enabling linearization and perturbation analysis.

Instructions

    Calculate series expansion of an expression.

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    🆕 NOT AVAILABLE IN SYMPY-MCP!
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    Use cases:
    - Approximate functions near a point
    - Linearization (order=1)
    - Small-signal analysis
    - Perturbation methods

    Args:
        expression: The expression to expand
        variable: The expansion variable
        point: The expansion point (default: "0" for Maclaurin series)
        order: Number of terms (default: 6)
        series_type: Type of series
            - "taylor": Taylor/Maclaurin series (default)
            - "laurent": Laurent series (for singularities)
            - "fourier": Fourier series (periodic functions)

    Returns:
        Series expansion with LaTeX

    Examples:
        # Maclaurin series of sin(x)
        calculate_series("sin(x)", "x", "0", order=5)
        → {"result": "x - x**3/6 + x**5/120", ...}

        # Taylor series around x=1
        calculate_series("ln(x)", "x", "1", order=4)
        → {"result": "-1 + x - (x-1)**2/2 + ...", ...}

        # Linearization (first-order approximation)
        calculate_series("exp(-E/(R*T))", "T", "T0", order=1)
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orderNo
pointNo0
variableYes
expressionYes
series_typeNotaylor

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses the return format ('Series expansion with LaTeX') and includes a notable unavailability warning for SymPy-MCP. However, it does not mention whether the operation is read-only, potential limitations, or error behavior, which would be helpful for full transparency.

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

Conciseness4/5

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

The description is well-structured with clear sections (Use cases, Args, Returns, Examples) and front-loads the purpose. It is slightly verbose due to decorative separators and the prominent unavailability warning, but every substantive part earns its place.

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

Completeness5/5

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

Given the tool's complexity with 5 parameters and an output schema, the description is highly complete. It covers all inputs, provides examples, states the output type, and includes an availability caveat. The output schema covers return structure, so the description does not need to explain it further.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by detailing every parameter, including defaults and explanations for series_type values. The Args section adds meaning beyond the raw schema, and examples illustrate usage effectively.

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

Purpose5/5

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

The description states 'Calculate series expansion of an expression' with a clear verb and resource. It distinguishes from sibling tools like calculate_limit and calculate_summation by focusing specifically on series expansions, and provides concrete examples.

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

Usage Guidelines4/5

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

The description includes a 'Use cases' section listing four specific scenarios (e.g., linearization, perturbation methods), giving a clear sense of when to apply the tool. However, it does not explicitly mention alternative tools or exclusions when other calculus operations would be more appropriate.

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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