get_nifty_indices

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 describes the data returned (price, change, etc.) and implies 'live' data, but does not disclose caching, authentication requirements, rate limits, or real-time nature. A 3 is appropriate as it offers some behavioral context 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, efficient sentence that lists key examples and data fields. It is front-loaded with purpose, but could be more structured (e.g., separate lines for return fields). No wasted words.

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 simple tool with one optional parameter and no output schema, the description adequately explains what data is returned (price, change, 52-week range, advances/declines) and the scope (all NSE indices). It is missing details like default behavior with no filter, but overall 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?

Schema coverage is 100% (the sole parameter 'name' is fully described in the schema). The description does not add any additional semantic value beyond the schema, so it meets the baseline of 3.

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 'Live data for all NSE indices' and lists specific examples (Nifty 50, Nifty Bank, etc.) and data fields (price, change, 52-week range, advances/declines). This distinguishes it from siblings like get_quote or search_by_symbol, which target individual symbols or different data.

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 the tool is for retrieving index data, but it does not explicitly state when to use this tool versus alternatives like get_quote or search_by_symbol. No usage scenarios or exclusions are provided, leaving the agent to infer from context.

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