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derivation_record_step

Record derivation steps with expression, description, and notes for a traceable math history. Use after SymPy calculations to log actions and reasoning with source and operation type.

Instructions

    記錄一個推導步驟(從 SymPy-MCP 或手動)

    ═══════════════════════════════════════════════════════════════════════
    這是 SymPy-MCP 和 NSForge 之間的橋樑!
    ═══════════════════════════════════════════════════════════════════════

    用途:
    1. 在 SymPy-MCP 計算後,把結果記錄到 NSForge 會話
    2. 可以加入 notes 說明「為什麼這步要這樣做」
    3. 保持完整的推導歷史

    工作流程:
    1. SymPy-MCP: intro + introduce_expression + substitute...
    2. SymPy-MCP: print_latex_expression (確認結果)
    3. NSForge: derivation_record_step (記錄這步 + 加入說明)
    4. 重複 1-3
    5. NSForge: derivation_complete

    Args:
        expression: SymPy 格式的表達式(從 SymPy-MCP 結果複製)
        description: 這步做了什麼
        latex: LaTeX 格式(可選,會自動生成)
        notes: 額外說明(非計算性的人類知識!)
               例如:「這裡假設線性,但酵素活性實際上是 S 型曲線」
        source: 來源 ("sympy_mcp", "manual", "literature")
        operation_type: 操作類型 ("substitute", "simplify", "solve", "custom")
        set_as_current: 是否設為當前表達式(預設 True)

    Returns:
        記錄結果

    Example:
        # 在 SymPy-MCP 計算完成後
        derivation_record_step(
            expression="C*V_max_ref*exp(E_a*(1/T_ref - 1/T)/R)/(C + K_m)",
            description="Substituted Arrhenius equation for Vmax",
            notes="假設 Vmax 的溫度依賴遵循 Arrhenius,但實際上酵素在高溫會變性",
            source="sympy_mcp"
        )
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latexNo
notesNo
sourceNosympy_mcp
expressionYes
session_idNo
descriptionYes
operation_typeNocustom
set_as_currentNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description discloses that LaTeX is auto-generated if not provided, that set_as_current controls whether this step becomes the current expression, and that it maintains a complete derivation history. However, it does not explain session handling, error conditions, or what happens if no active session exists.

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 clear sections (purpose, workflow, args, returns, example). The workflow section is somewhat repetitive, but the formatting and section headers make it easy to scan, and each part adds context.

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 description provides a complete picture: purpose, usage workflow, parameter explanations, and an example. The output schema exists, so return details are not necessary. However, the missing session_id parameter and lack of explicit prerequisites or failure conditions prevent a perfect score.

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 Args section explains 7 of 8 parameters in detail, including defaults and examples. It omits session_id entirely, which is a notable gap since that parameter could be important for multi-session workflows.

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 records a derivation step, and explicitly frames it as the bridge between SymPy-MCP and NSForge. This distinguishes it from sibling tools like derivation_add_note or derivation_get_steps by emphasizing its role in the derivation workflow.

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?

It provides a concrete 5-step workflow showing exactly when to use this tool after SymPy-MCP calculations, and when to use derivation_complete. The source parameter also clarifies usage contexts (sympy_mcp, manual, literature), but it does not explicitly name alternatives for exclusion.

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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