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sympy_python_code

Convert SymPy expressions into executable Python code for direct use in scripts or applications.

Instructions

Convert expression to Python code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
exprYesSymPy expression string

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function for the 'sympy_python_code' tool. It is decorated with @mcp.tool() (registration), takes a string expression, sympifies it, and returns the Python code representation via sympy.python().
    @mcp.tool()
    def sympy_python_code(expr: str) -> str:
        """Convert expression to Python code.
    
        Args:
            expr: SymPy expression string
    
        Returns:
            Python code string
    
        Example:
            >>> sympy_python_code("x**2 + 1")
            "x**2 + 1"
        """
        return str(sympy.python(_sympify(expr)))
  • The MCP server instance (FastMCP) that the @mcp.tool() decorator registers the handler with.
    mcp = fastmcp.FastMCP("mcp-sympy")
  • The _sympify helper function that converts a string expression into a SymPy object, used by the handler.
    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 exist, so description must carry behavioral transparency. It only says 'convert expression' without specifying whether the output is a string, what format it takes, or any side effects.

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 very short and to the point, but could benefit from a bit more structure or examples while remaining concise.

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 large number of sibling tools, the description lacks contextual completeness. It does not clarify when converting to Python code is appropriate or what the output looks like, even though an output schema exists.

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?

Schema coverage is 100% so the schema already documents the 'expr' parameter. The description adds no additional parameter semantics beyond the schema.

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?

Description clearly states it converts SymPy expressions to Python code. However, among many sibling conversion tools (e.g., sympy_latex, sympy_mathml), it does not distinguish what 'Python code' means or how it differs from other representations.

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 usage guidance is provided. The description does not indicate when to use this tool over alternatives like sympy_latex, sympy_str, or sympy_repr_expr.

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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