gift_nifty

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

With no annotations, the description carries the full burden of behavioral disclosure. It clearly explains what the tool returns (Nifty close/change, global indices, market status, links) and that it is free, which is useful context. It does not explicitly state read-only semantics, but the nature of a data retrieval tool is clear. It could add more about data freshness or limitations, but overall adequately discloses behavior.

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 with a clear opening sentence, bullet points for provided data, and a practical example. It is appropriately sized—concise yet informative—and front-loads the core purpose without unnecessary fluff.

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?

The description is complete for a 0-parameter tool with an output schema. It lists all provided data categories, includes an example invocation, and mentions the free-access advantage. Since an output schema exists, detailed return value descriptions are unnecessary. It provides enough context for an agent to select and use the tool correctly.

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?

The tool has 0 parameters, so the baseline score is 4 according to the rubric. The description does not need to explain parameter meanings since there are none. The example 'gift_nifty()' reinforces that no arguments are required.

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 provides 'GIFT Nifty pre-market data + overnight global indices for Indian market preview.' It lists exact data components (Nifty 50 close, global indices, market status) and distinguishes itself from siblings by focusing on pre-market global sentiment and GIFT Nifty, which is not covered by sibling tools like nse_market_status or nifty_index.

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 use for pre-market analysis with an example showing a call to get 'Pre-market global sentiment + Nifty reference.' It also notes this tool is a free alternative to Bloomberg, providing context on when to use it. However, it does not explicitly specify when not to use it or compare to alternatives like nse_market_status.

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