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derivation_add_note

Add contextual notes to mathematical derivations for capturing assumptions, limitations, observations, and corrections, preserving reasoning and clinical or physical meaning beyond formula steps.

Instructions

    在推導中加入說明(不是計算步驟)

    ═══════════════════════════════════════════════════════════════════════
    用於記錄「人類知識」- 不是計算,而是洞見、假設、警告、修正建議
    ═══════════════════════════════════════════════════════════════════════

    這很重要!數學推導不只是公式變換,還包含:
    - 為什麼選擇這個模型
    - 這個假設何時會失效
    - 臨床/物理意義是什麼
    - 需要注意什麼

    Args:
        note: 說明內容
        note_type: 說明類型
            - "assumption": 假設條件
            - "limitation": 限制/警告
            - "observation": 觀察/洞見
            - "correction": 修正建議
            - "clinical": 臨床意義
            - "physical": 物理意義
        related_variables: 相關的變數
        related_step: 相關的步驟編號(可選)

    Returns:
        記錄結果

    Example:
        # 在代入 Arrhenius 後加入說明
        derivation_add_note(
            note="酵素活性 vs 溫度不是線性的!在高溫 (>42°C) 酵素會變性,"
                 "此時 Arrhenius 方程不再適用。應考慮加入校正因子 γ(T)。",
            note_type="limitation",
            related_variables=["V_max", "T"]
        )

        # 加入修正建議
        derivation_add_note(
            note="建議加入 Hill-type 校正因子:γ(T) = 1 / (1 + (T/T_denat)^n)",
            note_type="correction",
            related_variables=["gamma", "T_denat"]
        )
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
noteYes
note_typeNoobservation
session_idNo
related_stepNo
related_variablesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description must fully disclose behavioral traits. It mentions that the tool is not a calculation step and returns a 'record result', but it does not explain side effects, session requirements, persistence, or how the note integrates with the derivation. This is insufficient for an unannotated tool.

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

Conciseness3/5

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

The description is well-structured with sections for Args, Returns, and Examples, but it includes lengthy motivational prose that is not strictly necessary. It is front-loaded with a clear purpose, yet the extra explanation makes it longer than needed for a simple note-adding tool.

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

Completeness3/5

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

The tool is moderately simple, and the description covers purpose, parameters, and examples. However, it does not explain the return value in any detail, omits session_id, and does not clarify how notes are stored or displayed. Given the absence of annotations, the description could be more complete.

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?

The schema description coverage is 0%, so the description must compensate. It provides detailed meanings for note, note_type (including all enum values and their significance), related_variables, and related_step. However, it omits session_id, which is in the schema, so it is not fully comprehensive.

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 clearly states the tool adds a note (not a calculation step) to a derivation, with a specific verb and resource. It distinguishes itself from calculation tools like derivation_record_step, but does not explicitly differentiate from the similarly-named sibling derivation_insert_note, which reduces clarity.

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 explicitly states the tool is for recording human knowledge (insights, assumptions, warnings, corrections) and includes a list of use cases. It implies it should not be used for calculation steps, but does not name alternatives or provide a when-not-to-use section, so it is just shy of full guidance.

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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