SymKit

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generate_latex_derivation

Generate LaTeX documentation for mathematical derivations by providing title, steps, and final result. Outputs a ready-to-use LaTeX string.

Instructions

    Generate LaTeX documentation for a derivation.

    Args:
        title: Derivation title
        steps: List of {"description": str, "latex": str}
        final_result: Final result in LaTeX

    Returns:
        LaTeX document string

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "title": "<title>",
      "steps": "<steps>",
      "final_result": "<final_result>"
    },
    "name": "generate_latex_derivation"
  }
}

Input Schema

Name	Required	Description	Default
`steps`	Yes
`title`	Yes
`final_result`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Jul 8, 2026

TDQS

A3.6/5.0

Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It describes inputs and output but does not explicitly state whether the operation is side-effect-free, whether it validates inputs, or how it handles edge cases. While it implies a pure generation function, this is not explicitly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a well-structured docstring with Args and Returns sections, containing no unnecessary words. It is succinct and clearly organized, making it easy for an agent to parse.

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?

For a simple generation tool, the description covers the essential inputs and return value. It does not include examples or edge-case behavior, but the tool's purpose is straightforward and the output schema likely provides additional structure. The description is adequate given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description compensates by explaining each parameter: title is the derivation title, steps is a list of {description, latex} objects, and final_result is the final result in LaTeX. This adds meaningful semantic context beyond the raw schema types.

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: generating LaTeX documentation for a derivation. It uses a specific verb (Generate) and resource (LaTeX documentation for a derivation), and distinguishes itself from related generation tools by specifying the output format (LaTeX). The Args and Returns sections further clarify the purpose.

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 such as generate_derivation_report or generate_sympy_script. It only states what it does, without any contextual or exclusionary information to help an agent choose it over siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.