get_all_symbol_groups

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 only states the basic function and output format, omitting critical details such as authentication requirements, rate limits, pagination behavior, data freshness, or any side effects. The mention of 'toon' being optimized for AI is a minor behavioral hint but insufficient for comprehensive transparency.

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 concise, with a clear one-line purpose followed by structured parameter and return info. It is front-loaded with the main action. However, the Args/Returns formatting slightly reduces conciseness and could be streamlined (e.g., removing Python syntax) without losing clarity.

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?

The description lacks completeness for a tool that returns 'all symbol groups'. It mentions a return type (pd.DataFrame) but does not specify the schema (columns, data types), potential size, or any constraints. Without an output schema, the description should provide more detail about what the dataframe contains. Additionally, no information is given about error cases or empty results.

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 description adds significant meaning to the single parameter 'output_format' beyond the schema. It lists the options ('json', 'dataframe', 'toon'), specifies the default ('toon'), and explains that 'toon' is optimized for AI. Since the schema has 0% description coverage, the description compensates well, though it could further clarify the structure of the 'toon' format.

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 action ('Get'), the resource ('all symbol groups'), and the source ('from stock market'). This distinguishes it from sibling tools like 'get_all_symbols' (which gets individual symbols) and 'get_all_symbols_by_group' (which filters symbols by group), making the purpose specific and unambiguous.

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 does not mention scenarios, prerequisites, or contexts where this tool is preferred. Given the large set of sibling tools, this omission limits its usefulness for an AI agent deciding which tool to invoke.

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