get_stock_sentiment

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 mentions the tool is 'AI-powered' and includes a 'Price: $0.40', hinting at a paid service, but doesn't disclose critical behavioral traits like rate limits, authentication needs, data sources, or error handling. For a tool with no annotations, this leaves significant gaps in understanding its operation.

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 well-structured and appropriately sized. It starts with a clear purpose, lists parameters with brief explanations, specifies returns, and ends with price info. Every sentence adds value, though the 'Price' line could be integrated more smoothly. It's front-loaded and avoids redundancy.

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 moderate complexity (4 params, no annotations, output schema exists), the description is partially complete. It covers parameters and return values, but lacks behavioral context (e.g., costs, limitations). The output schema handles return structure, so the description doesn't need to detail that, but it should address usage and operational aspects more fully.

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. It effectively adds meaning by explaining each parameter: 'symbol' as 'Stock ticker symbol', and the boolean flags as controlling inclusion of news, technicals, and institutional analysis. This clarifies beyond the bare schema, though it doesn't detail format constraints (e.g., symbol validation).

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 purpose: 'Get AI-powered stock market sentiment analysis.' It specifies the action ('Get') and resource ('stock market sentiment analysis'), and distinguishes it from siblings like 'get_forex_sentiment' and 'get_token_sentiment' by focusing on stocks. However, it doesn't explicitly differentiate from 'get_sentiment_analysis' (a sibling), which might be more general.

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 siblings like 'get_forex_sentiment' for forex or 'get_token_sentiment' for tokens, nor does it specify prerequisites or exclusions. The only implicit context is the stock focus, but no explicit usage instructions are given.

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