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detect_unusual_activity

Scan NSE stocks for volume anomalies, options OI buildup, block deals, and promoter changes. Returns alert level and specific signals.

Instructions

Detect smart money and unusual activity for any NSE stock.

Scans 4 signals simultaneously: • Volume anomaly — current volume vs 20-day average (flags 2x+) • Options OI — strikes with 2x+ average open interest buildup • Block/bulk deals — institutional buy/sell transactions on NSE • Promoter change — QoQ shareholding increase (insider buying signal)

Returns an alert level (high/moderate/low/none) with specific findings.

Example output: "Unusual call OI at 3000 strike — someone is positioning for a breakout" "Promoter increased holding by 2.3% QoQ — insider buying signal"

Args: symbol: NSE stock symbol (e.g. RELIANCE, HDFC, TATAMOTORS)

Returns JSON with: - alert_level: high / moderate / low / none - verdict: human-readable summary - alerts: list of specific signals fired - findings: per-category details (volume, OI, deals, promoter)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations provided, so description bears full burden. It details the four signals and output format (alert level, verdict, alerts, findings), making behavior transparent. Does not mention permissions or side effects, but as a read-only monitoring tool this is acceptable.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with bullet points listing the four signals, an example output, and clear sections for args and returns. Every sentence adds value; no fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema (not shown), the description adequately explains return fields. The tool is standalone and the description covers all essential aspects: inputs, signals, output, and example.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter (symbol) with 0% schema description coverage. The description adds examples (e.g., RELIANCE, HDFC) and clarifies it is an NSE stock symbol, compensating for schema lack.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it detects smart money and unusual activity for NSE stocks by scanning four specific signals. It is distinct from sibling tools like nse_insider_trading or nse_bulk_deals which cover only individual aspects.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use (to detect unusual activity) but does not explicitly state when not to use or mention alternative tools. However, it provides clear context for its composite nature.

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

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