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

Annotations already provide readOnlyHint=true, and the description adds useful behavioral context by enumerating the components returned (thesis, break conditions, bear case, catalysts, status) and noting response_mode=plain for beginners. This goes beyond the annotation without contradicting it.

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 compact and front-loaded with the core purpose in the first sentence. The usage triggers, response_mode note, and argument summary are all relevant, though the 'Args:' line is a bit compressed and could be clearer.

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?

With no output schema, the description carries the burden of explaining returns and parameters. It does list the thesis components, but it leaves reading_level unexplained and does not clarify what each view variant returns. This is adequate but has notable gaps for a tool with four parameters.

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 for parameter meaning. It does explain view values ('case | review | story') and response_mode=plain, but it gives no explanation of reading_level and only a vague sense of entity from 'stock.' This is partial compensation, not full.

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 that the tool returns a stock's standing investment thesis, including testable claims, bear case, catalysts, and whether it still holds. It is specific about the resource and content, but it does not explicitly distinguish itself from the sibling tool get_thesis_impact.

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 gives concrete trigger examples ('is X still a buy; what's the bull case for X; is X's story holding up'), which tells an agent when to use the tool. It does not mention when not to use it or name alternative tools, so it stops short of full usage guidance.

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