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formula_constants

List physical constants from SciPy CODATA 2018, including values, units, and uncertainty. Search by keyword or filter by category such as fundamental, electromagnetic, atomic, or conversion.

Instructions

    列出物理常數

    從 SciPy CODATA 2018 獲取物理常數。

    Args:
        category: 分類
                 - "fundamental": 基本常數(c, h, G)
                 - "electromagnetic": 電磁常數
                 - "atomic": 原子常數
                 - "conversion": 換算因子
        query: 搜尋關鍵字(可選)

    Returns:
        物理常數列表(含數值、單位、不確定度)

    Example:
        # 列出所有基本常數
        formula_constants(category="fundamental")

        # 搜尋電子相關常數
        formula_constants(query="electron")
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
categoryNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses the data source (SciPy CODATA 2018), return format (values, units, uncertainty), and available categories, which covers essential behavioral aspects for a read-only retrieval tool. It does not mention edge cases like empty results, but this is minor for this simple tool.

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 a short purpose statement, Args, Returns, and Example sections. It is slightly verbose but every section provides useful information, making it easy for an agent to quickly grasp the tool's behavior.

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?

Given the tool's simplicity and the presence of an output schema, the description covers purpose, parameters, returns, and examples. There are no annotations, but the description provides enough detail for an agent to select and invoke the tool correctly, though it could mention default behavior when no arguments are provided.

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?

Schema description coverage is 0%, so the description fully compensates. It explains 'category' with enumerated values (fundamental, electromagnetic, atomic, conversion) and 'query' as an optional search keyword. Examples further clarify parameter usage, adding significant meaning beyond the bare schema.

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 uses a specific verb ('列出' / list) and identifies the resource ('物理常數' / physical constants from SciPy CODATA 2018). It clearly distinguishes this tool from the sibling formula-related tools by focusing on constants lookup.

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 clear context on how to use the tool via category explanations and examples. It does not explicitly mention when not to use it or compare with alternatives, but the scope is well-defined, and the examples demonstrate typical use cases.

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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