detect_unusual_activity

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does well by detailing the four signal sources, the alert levels (high/moderate/low/none), providing an example output, and listing the exact return JSON fields (alert_level, verdict, alerts, findings). However, it does not disclose potential limitations or data freshness, which would have made it fully transparent.

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 clear sections: purpose, signals, return format, example, and args. It is front-loaded with the core purpose and uses bullet points for readability. Although it is somewhat lengthy (about 200 words), each section contributes value and the example output helps illustrate behavior without being superfluous.

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 tool is complex (4 signal categories, multiple output fields) and has an output schema, which reduces the burden on the description. The description nevertheless covers all the important aspects: what signals are scanned, the alert levels, a concrete example, and the return JSON structure. It does not mention data limitations or prerequisites, but it is sufficiently complete for an agent to understand and invoke 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 input schema provides only a bare 'symbol' string with no description. The description compensates fully by including an Args section that explains the parameter: 'symbol: NSE stock symbol (e.g. RELIANCE, HDFC, TATAMOTORS).' This clarifies the expected format and provides concrete examples, which is essential for correct invocation. Given the 0% schema coverage, this is a strong compensation.

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: 'Detect smart money and unusual activity for any NSE stock.' It specifically enumerates the four signal categories (volume anomaly, options OI, block/bulk deals, promoter change), distinguishing it from sibling tools that focus on individual signals like options_oi_analytics or nse_bulk_deals. This makes the tool's role and scope explicit.

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 a combined multi-signal scanner, but it does not explicitly state when to use it versus more specialized alternatives. No when-not-to-use guidance or alternative tool references are provided. For instance, it does not say 'for only OI analysis, use options_oi_analytics.' The usage context is implied but not directly articulated.

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