analyze_stock

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

With no annotations, the description bears full responsibility for behavioral disclosure. It clearly states what the tool returns (price, key financials, LLM points) and that it uses an LLM, but it omits potential side effects like data freshness, error behavior, or whether real-time data is used.

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 and well-structured: a short purpose statement followed by a compact Args section. Every sentence adds value, with no redundancy or unnecessary detail.

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 absence of annotations and output schema, the description provides adequate coverage of purpose and parameters but lacks depth on output format specifics, financial metrics included, or selection criteria against sibling tools. It is sufficient for a simple tool but leaves some gaps.

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 compensates by explaining each parameter with concrete examples. It clarifies that 'query' is a natural language request and 'symbol' is optional with a specific code/name format, adding value beyond the bare schema.

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?

Description clearly states the tool analyzes A-share stocks using natural language and returns price, key financials, and LLM-generated investment points. This specific verb+resource+output distinguishes it from siblings like screen_stocks (screening) and compare_stocks (comparing).

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 usage context through query and symbol examples, implying when to use the tool (for natural language stock analysis). However, it does not explicitly mention when not to use it or alternatives such as stock_report or screen_stocks, leaving tool-selection guidance vague.

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