get_simple_answer

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 mentions the output is 'simplified' and 'LLM-friendly,' but doesn't cover critical aspects like rate limits, authentication needs, error handling, or what 'simplified' entails (e.g., formatting, length). This leaves significant gaps for an AI agent to understand the tool's behavior.

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 a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It is front-loaded with the core function ('Get a simplified, LLM-friendly answer'), making it easy to parse. Every part of the sentence contributes to understanding the tool.

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's low complexity (1 parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage guidelines, and output specifics. Without annotations or an output schema, more context on what the answer includes would improve completeness, but it meets a bare minimum.

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?

The input schema has 100% description coverage, with the 'query' parameter documented as 'The query to ask WolframAlpha.' The description adds no additional meaning beyond this, such as query format examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't need to heavily.

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 the tool's function: 'Get a simplified, LLM-friendly answer focusing on the most relevant information.' It specifies the action ('Get'), the output type ('simplified, LLM-friendly answer'), and the focus ('most relevant information'). However, it doesn't explicitly differentiate from sibling tools like 'ask_llm' or 'validate_key', which prevents a perfect score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'ask_llm' or 'validate_key', nor does it specify contexts or exclusions for usage. The phrase 'LLM-friendly answer' implies a target audience but lacks explicit usage rules.

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