get_fii_retail_divergence

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

With no annotations, the description carries the full burden of disclosure. It explains the data source (NSE public shareholding disclosures), the output structure (signal, confidence, interpretation), and the signal logic. The quarterly data lag is disclosed, providing a key behavioral trait. It does not mention rate limits or auth, but these are likely not critical for an analysis tool.

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 structured with sections for purpose, signal logic, data source, arguments, and returns. It is front-loaded with the main goal, and the example quote is illustrative. It is not overly verbose; each section contributes to understanding, though it could be slightly trimmed.

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 covers the tool's purpose, signal logic, data frequency, parameter format, and expected return fields. It explains the output includes divergence_type, signal, confidence, interpretation, historical implication, and raw QoQ data. This makes it highly complete given the single symbol argument and the analysis nature of the tool.

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 input schema has only 'symbol' with no description, but the description explicitly explains 'symbol: NSE symbol (e.g. HDFCBANK, RELIANCE, TATAMOTORS)'. This fully compensates for the schema gap and adds practical examples, making parameter semantics clear.

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 opens with a specific verb+resource: 'Detect FII vs retail divergence'. It defines the signal logic with clear buy/sell scenarios and a concrete example (HDFC Bank), distinguishing it from raw FII/DII data tools like nse_fii_dii_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?

It clearly states when this signal is relevant: when FII and retail move in opposite directions. It also notes the data comes from quarterly NSE disclosures, implying trend-based usage. However, it does not explicitly mention when not to use it or list alternative tools, though the context is sufficiently clear.

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