wolfram_alpha

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool as giving computed answers and data, and outlines return types. However, it omits potential limitations like API rate limits, authentication requirements, or behavior for ambiguous queries. The description is adequate but lacks some transparency for a reliable agent invocation.

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

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

The description is well-structured with a clear purpose statement, args, returns, and examples. It is front-loaded with the key action and adds value without verbosity. It could be slightly more concise, but it effectively communicates everything needed in a compact format.

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

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

Given the tool has only 2 parameters and the context indicates an output schema exists, the description is sufficiently complete. It covers parameter semantics, return types, and provides examples. The presence of an output schema reduces the need to detail return values. The description meets the needs for correct invocation and understanding.

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

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

Schema description coverage is 0%, so the description must compensate entirely. It provides excellent parameter details: 'query' is explained as 'Natural language question' with multiple domain examples, and 'return_type' is described with each enum value ('result', 'data', 'full') and their purposes. This far exceeds the schema's minimal title and type information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Query Wolfram Alpha with natural language,' specifying the verb and resource. It distinguishes from sibling tools like 'interpret_natural_language' and various Mathematica functions by calling it 'Mathematica superpowers' and emphasizing plain English queries, which is unique among the many computation tools listed.

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

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

The description provides clear use context: ask questions in plain English and get computed answers, data, and more. Examples illustrate typical use cases. However, it does not explicitly state when not to use this tool or mention alternatives, such as more specific Mathematica functions for symbolic computation or data lookup tools like 'entity_lookup' or 'interpret_natural_language'.

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