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technical_indicators

Calculate technical indicators like RSI, MACD, and Bollinger Bands for stocks to generate buy/sell signals. Supports Indian and US markets with local computation.

Instructions

Compute technical indicators for a stock with buy/sell signals.

Calculates RSI, MACD, SMA (20/50/200), EMA, Bollinger Bands, ATR, Stochastic, ADX, OBV — all computed locally, no API cost.

Works for Indian (NSE/BSE) and US stocks.

Args: symbol: Stock ticker (e.g., RELIANCE, TCS, AAPL, TSLA) period: Data period for calculation: 3mo, 6mo, 1y, 2y (default: 6mo) indicators: Comma-separated list of specific indicators. Options: RSI, MACD, SMA, EMA, BBANDS, ATR, STOCH, ADX, OBV Leave empty for ALL indicators.

Examples: technical_indicators("RELIANCE") → All indicators for Reliance technical_indicators("AAPL", "1y") → Apple with 1 year data technical_indicators("TCS", "6mo", "RSI,MACD,SMA") → Only RSI, MACD, SMA

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
periodNo6mo
indicatorsNo

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 carries full disclosure burden. It successfully reveals 'all computed locally, no API cost' (infrastructure trait), buy/sell signal generation (interpretation logic), and geographic limitations. Read-only nature is implied by 'computed locally' but not explicitly stated.

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

Conciseness4/5

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

Front-loaded with core purpose, followed by capability list, constraints, and structured Args/Examples sections. Slightly verbose due to necessary parameter documentation (justified by schema gaps), but every sentence provides value. Clear separation between description and parameter specs.

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

Completeness4/5

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

Comprehensive coverage of 3 parameters with examples compensates for zero schema descriptions. Output schema exists so return value explanation is unnecessary. Captures key behavioral traits (local computation, cost, signal generation) sufficient for agent selection despite lack of annotations.

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

Parameters5/5

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

With 0% schema description coverage, the Args section fully compensates by documenting all 3 parameters: symbol includes examples (RELIANCE, AAPL), period lists valid values (3mo, 6mo, 1y, 2y) with default, and indicators explains comma-separated format with complete enum options and empty-string behavior.

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 opens with 'Compute technical indicators for a stock with buy/sell signals'—a specific verb+resource combination. Listing RSI, MACD, SMA, etc. precisely distinguishes this from siblings like stock_quote (current price) or balance_sheet (financial data). The scope is unmistakably technical analysis.

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?

States geographic scope ('Works for Indian (NSE/BSE) and US stocks') and cost behavior ('no API cost'), which guides selection over external API alternatives. The Examples section demonstrates when to use specific indicator combinations. Lacks explicit 'don't use X when Y' statements, but provides strong contextual cues.

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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