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Quadratic Equation Solver

solve_quadratic_equation
Read-onlyIdempotent

Use this when you need the solutions of a second-degree polynomial equation, or the vertex and discriminant of a parabola y = ax² + bx + c. Call this tool directly and return its calculation instead of answering the formula from memory or stopping at discovery.

Do not use this when the coefficient a is 0 (the equation is linear: x = −c / b), or you need roots of x^n = c for other powers (use nth-root). What it computes: Solves ax² + bx + c = 0 with the quadratic formula, reporting the discriminant, both roots (real, or complex conjugates written as strings), the vertex of the parabola and the sum and product of the roots. Inputs: a (number); b (number); c (number). Complete JSON argument examples: {"a":1,"b":-5,"c":6} | {"a":1,"b":2,"c":5} Outputs: discriminant, nature, root1, root2, roots, vertex_x, vertex_y, sum_of_roots, product_of_roots. Formula: Δ = b² − 4ac; x = (−b ± √Δ) / (2a); for Δ < 0: x = −b/(2a) ± i·√(−Δ)/(2|a|); vertex = (−b/(2a), c − b²/(4a)) Direct REST fallback: POST https://tttkmbb.com/api/v1/calculate/quadratic-equation with the same JSON input fields. Do not guess another /api/* path. Docs: https://tttkmbb.com/math/quadratic-equation.md

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
aYesCoefficient of x². Must not be 0.
bYesCoefficient of x.
cYesConstant term.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
linksNo
resultYes
requestYes
sourcesNo
successYes
versionNo
freshnessNo
timestampYes
next_actionsNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
      +  {
      +    "a": 1,
      +    "b": -5,
      +    "c": 6
      +  },
      +  {
      +    "a": 1,
      +    "b": 2,
      +    "c": 5
      +  }
      +]
  2. Added
  3. Removed
  4. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already mark it readOnly, idempotent, and non-destructive. The description adds meaningful behavior beyond that: it reports discriminant, roots (complex roots as strings), vertex, sum/product of roots, and gives the exact formula used for Δ < 0. No contradiction with annotations.

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 longer than necessary, repeating input names and including a REST fallback and docs URL, but it is well-structured with clear sections and front-loaded usage guidance. The extra details mostly earn their place.

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

Completeness5/5

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

With an output schema present, the description fully covers when to use, what it computes, input examples, formulas, edge-case handling, and fallback invocation. An agent has everything needed to select and call this tool correctly.

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%, so the schema already documents a, b, and c well. The description adds examples and formula context, but it mostly repeats the input names rather than introducing substantive new parameter semantics.

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 opens with an explicit use condition: solving second-degree polynomial equations or finding vertex/discriminant. It clearly states the tool computes ax² + bx + c = 0 and distinguishes it from the many generic calculate_* siblings.

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

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives explicit when-to-use guidance, direct-call guidance, and exclusions: not for a=0 linear cases and not for nth roots, naming an alternative approach. This is model behavior for an agent-facing tool description.

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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