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query_assumptions

Determines whether a mathematical expression satisfies a specific property under given assumptions. Returns True, False, or Unknown to validate domains and identify singularities.

Instructions

    Query properties of an expression based on assumptions.

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    🆕 NOT AVAILABLE IN SYMPY-MCP! Uses sympy.assumptions module.
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    Use cases:
    - Check if expression is always positive
    - Verify domain validity
    - Check for potential singularities

    Available queries:
    - positive, negative, nonnegative, nonpositive
    - real, imaginary, complex
    - integer, rational, irrational
    - even, odd, prime
    - finite, infinite, zero, nonzero

    Args:
        expression: Expression to query about
        query: Property to check
        assumptions: Assumptions about symbols
            {"x": ["positive", "real"], "n": ["integer"]}

    Returns:
        Query result (True, False, or None if unknown)

    Examples:
        # Is x**2 always positive?
        query_assumptions("x**2", "positive", {"x": ["real", "nonzero"]})
        → {"result": True, ...}

        # Is exp(x) always real?
        query_assumptions("exp(x)", "real", {"x": ["real"]})
        → {"result": True, ...}
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
expressionYes
assumptionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

Since there are no annotations, the description must convey safety and operational semantics. It discloses return values (True, False, or None if unknown), the underlying sympy.assumptions module, and includes examples. The confusing 'NOT AVAILABLE IN SYMPY-MCP!' warning could be clarified, but overall it provides substantial behavioral insight.

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 headers, bullet points, and examples, but contains an eye-catching banner and decorative box that adds noise. It is appropriately sized for the tool's complexity, though the banner could be simplified.

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

Completeness4/5

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

The tool has an output schema and moderate complexity. The description covers parameters, usage scenarios, available queries, and examples, which is sufficient for an AI agent to invoke it correctly. However, the availability warning is cryptic and might confuse the agent about whether the tool can be used.

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?

With 0% schema description coverage, the description fully compensates by listing all query enum values, specifying the assumptions format with an example, and demonstrating parameter usage in two examples. This goes beyond the schema's bare property names.

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 clearly states the tool's function with a specific verb ('Query') and resource ('properties of an expression based on assumptions'). It further enumerates use cases and available query types, making it distinct from sibling tools like evaluate_numeric or symbolic_equal.

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 provides explicit use cases (check if expression is always positive, verify domain validity, check for singularities) that clarify when to use the tool. However, it does not mention alternatives or explicitly state when not to use it, so it falls at 'clear context, no 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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