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sympy_eq

Create a symbolic equality from two mathematical expressions. Use this to define equations for solving or simplification.

Instructions

Create equality.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lhsYesLeft-hand side
rhsYesRight-hand side

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The sympy_eq tool handler: takes two string expressions (lhs, rhs), converts them to SymPy objects via _sympify, creates an Eq object, and returns its string representation.
    @mcp.tool()
    def sympy_eq(lhs: str, rhs: str) -> str:
        """Create equality.
    
        Args:
            lhs: Left-hand side
            rhs: Right-hand side
    
        Returns:
            Equality as string
    
        Example:
            >>> sympy_eq("x", "y")
            "Eq(x, y)"
        """
        return str(Eq(_sympify(lhs), _sympify(rhs)))
  • The function signature serves as the input schema: it takes two strings (lhs and rhs) and returns a string.
    def sympy_eq(lhs: str, rhs: str) -> str:
  • The @mcp.tool() decorator registers sympy_eq as an MCP tool.
    @mcp.tool()
  • The _sympify helper converts string expressions to SymPy objects, used internally by sympy_eq.
    def _sympify(expr: str) -> sympy.Basic:
        """Convert string expression to SymPy object."""
        return sympy.sympify(expr)
Behavior2/5

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

No annotations are provided, and the description gives no behavioral details beyond 'Create equality'. It does not disclose potential side effects, safety, or return behavior beyond the schema's basic info.

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 extremely concise (two words), but this is at the expense of clarity. It is well-structured for its length, but lacks substantive content.

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

Completeness2/5

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

Given the context of many sibling tools, the description is insufficient. It does not explain what 'equality' means in the SymPy domain, nor does it clarify the return type or usage scenarios.

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% coverage with descriptions for 'lhs' and 'rhs', so the baseline is 3. The description adds no additional meaning or usage hints for the parameters.

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

Purpose3/5

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

The description 'Create equality' minimally indicates the tool creates an equality object, but lacks specificity about what type of equality (e.g., SymPy Eq) and how it relates to sibling tools like sympy_ne or sympy_ge.

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?

No guidance is provided on when to use this tool versus alternatives such as sympy_ne (not equal) or sympy_ge (greater or equal). The description does not mention context, prerequisites, or exclusions.

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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