get_ai_predictions

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

No annotations are provided, so the description carries the full burden. It states it fetches recommendations (read-only) but does not disclose any specific behavioral traits such as rate limits, update frequency, or permission requirements. The description is not misleading but lacks depth.

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 sentence that conveys the core purpose efficiently with no wasted words. It is front-loaded with the key action and result.

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?

For a tool with no parameters and no output schema, the description is mostly complete. It specifies the output type (recommendations) and scope (25 stocks). Additional details on output format would improve completeness, but the current description is sufficient.

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?

There are zero parameters, and the schema coverage is 100%. With no parameters to document, the description cannot realistically add parameter information, earning a baseline score of 4.

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 fetches daily BUY/SHORT/LEAVE recommendations for 25 specific NSE stocks using AI from news and technicals. It uses a specific verb ('fetch') and resource ('daily recommendations'), and distinguishes from siblings like get_news_sentiment and get_ai_picks.

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 implies it's for daily recommendations but does not explicitly state when to use this tool versus siblings. No exclusions or conditions are mentioned, leaving the agent without clear guidance on choosing between tools like get_ai_picks or get_news_sentiment.

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