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sympy_latex

Convert a SymPy expression string into LaTeX format for mathematical typesetting.

Instructions

Convert expression to LaTeX.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
exprYesSymPy expression string

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler function for the sympy_latex tool. It is decorated with @mcp.tool(), takes a SymPy expression string as input, converts it to a SymPy object via _sympify(), and returns the LaTeX string representation using sympy's latex() function.
    @mcp.tool()
    def sympy_latex(expr: str) -> str:
        """Convert expression to LaTeX.
    
        Args:
            expr: SymPy expression string
    
        Returns:
            LaTeX string
    
        Example:
            >>> sympy_latex("x**2 + sin(y)")
            "x^{2} + \\sin{\\left(y\\right)}"
        """
        return str(latex(_sympify(expr)))
  • The @mcp.tool() decorator on line 809 registers the sympy_latex function as a tool with the FastMCP server instance named 'mcp' (defined on line 119).
    @mcp.tool()
  • The _sympify() helper function is used by sympy_latex to convert the input string expression into a SymPy Basic object, which is then passed to latex().
    def _sympify(expr: str) -> sympy.Basic:
        """Convert string expression to SymPy object."""
        return sympy.sympify(expr)
  • The 'latex' function is imported from sympy at the top of the file, providing the LaTeX conversion capability used in the sympy_latex handler.
    latex,
  • Test case for the sympy_latex tool, verifying that the LaTeX output contains expected formatting like 'x^{2}'.
    def test_latex(self):
        """Test LaTeX output."""
        result = tools.sympy_latex("x**2 + 1")
        assert "x^{2}" in result
Behavior2/5

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

With no annotations, the description should disclose behavioral traits. It only states the basic conversion, omitting details like error handling, supported expression types, or output format beyond 'LaTeX'.

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 a single, clear sentence with no unnecessary words. It is concise and front-loaded, though minimal.

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 simple one-parameter tool and presence of an output schema, the description is minimally adequate. However, it does not provide context on common use cases or limitations, which would improve completeness.

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% (one parameter with description), so baseline is 3. The description adds no additional meaning beyond what the schema provides.

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 'Convert expression to LaTeX' clearly states the action (convert) and the resource (expression to LaTeX), effectively distinguishing this tool from siblings like sympy_mathml or sympy_python_code.

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 provides no guidance on when to use this tool versus alternatives (e.g., sympy_mathml, sympy_repr_expr) or any prerequisites or limitations.

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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