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sympy_ge

Construct a symbolic greater-than-or-equal inequality between two algebraic expressions.

Instructions

Create greater than or equal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lhsYesLeft-hand side
rhsYesRight-hand side

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The handler function that implements the 'sympy_ge' tool. It takes two string expressions (lhs, rhs), sympifies them, constructs a SymPy Ge (greater-than-or-equal) object, and returns its string representation.
    @mcp.tool()
    def sympy_ge(lhs: str, rhs: str) -> str:
        """Create greater than or equal.
    
        Args:
            lhs: Left-hand side
            rhs: Right-hand side
    
        Returns:
            Greater than or equal as string
    
        Example:
            >>> sympy_ge("x", "y")
            "x >= y"
        """
        return str(Ge(_sympify(lhs), _sympify(rhs)))
  • The type signature defines the input schema: two string parameters (lhs, rhs) and a string return value.
    def sympy_ge(lhs: str, rhs: str) -> str:
  • The tool is registered via the @mcp.tool() decorator, which is the FastMCP framework's registration mechanism.
    @mcp.tool()
  • Supporting helper function _sympify used to convert string input into a SymPy expression object before comparison.
    def _sympify(expr: str) -> sympy.Basic:
        """Convert string expression to SymPy object."""
        return sympy.sympify(expr)
  • The Ge (greater-than-or-equal) class is imported from sympy at the top of the file and used inside the handler.
    Ge,
Behavior2/5

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

With no annotations, the description carries full burden. It only says 'Create greater than or equal,' which does not disclose behavioral traits such as side effects, return type, or whether it produces a relational expression. This is minimal disclosure.

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 at just four words. While front-loaded and waste-free, it sacrifices useful context, earning a high but not perfect score because it could include brief additional information without losing conciseness.

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 complexity of SymPy relational operations and the presence of many sibling tools, the description is incomplete. It does not explain what type of object is created (e.g., a relational expression), nor does it leverage the existing output schema to clarify return values.

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 description coverage is 100% with generic descriptions ('Left-hand side', 'Right-hand side'). The tool description adds no additional meaning beyond that. Baseline 3 is appropriate as the schema already documents parameters.

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 'Create greater than or equal.' uses a clear verb-object structure, and the tool name 'sympy_ge' further clarifies the inequality direction. It distinguishes from siblings like 'sympy_gt' (greater than) and 'sympy_eq' (equal), but adds no extra detail beyond the name itself.

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

Usage Guidelines1/5

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 like sympy_gt, sympy_lt, or sympy_eq. There is no mention of context, prerequisites, or common usage scenarios.

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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