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Twelvedata

by io.github.pipeworx-io

Technical Indicator

technical_indicator
Read-onlyIdempotent

Compute a technical indicator time series for a stock/forex/crypto symbol via Twelve Data — RSI, SMA, EMA, MACD, Bollinger Bands (bbands), ADX, ATR, Stochastic, CCI, and more. PREFER for "RSI(14) of AAPL", "50-day and 200-day SMA of TSLA", "MACD for BTC/USD", "Bollinger Bands of SPY". This is the right tool for ANY "RSI / SMA / EMA / MACD / moving average / technical indicator for " question. Computes ONE indicator per call: if several are requested (e.g. "RSI and the 50-day and 200-day SMA"), call this once per indicator starting with the first — do NOT decline just because multiple indicators are asked for. Returns dated indicator values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYesTicker/symbol. Stocks e.g. "AAPL", "MSFT"; forex "EUR/USD"; crypto "BTC/USD"; ETFs "SPY"; indices "IXIC". Comma-separate for a batch (e.g. "AAPL,MSFT").
intervalYesBar interval: 1min, 5min, 15min, 30min, 45min, 1h, 2h, 4h, 1day, 1week, or 1month.
indicatorYesIndicator (lowercase): rsi, sma, ema, wma, dema, tema, macd, bbands, stoch, stochrsi, adx, atr, natr, cci, mom, roc, willr, obv, ad, vwap, aroon, mfi, sar, trix, ppo, kama.
outputsizeNoNumber of data points to return (1–5000, default 30).
series_typeNoPrice series the indicator is computed on: close (default), open, high, low.
time_periodNoLook-back period in bars (e.g. 14 for RSI, 50 or 200 for SMA, 20 for bbands). Default varies by indicator.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, establishing safety. Description adds that it computes ONE indicator per call and returns dated indicator values. This aligns with annotations and adds context about call behavior and return format, though no additional limitations or edge cases are mentioned.

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?

The description is a single focused paragraph that front-loads the core action, then provides examples, usage guidance, and multi-request handling. Every sentence earns its place with no redundancy or filler.

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?

Given 6 parameters (3 required) and no output schema, the description adequately covers purpose, examples, and handling of multi-indicator requests. It mentions 'Returns dated indicator values' but omits specifics like default outputsize or default time_period (though schema covers those). Overall sufficient for correct invocation, but a brief note on defaults would enhance completeness.

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

Parameters3/5

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

Schema description coverage is 100% with detailed descriptions for all 6 parameters. The description does not expand on parameter semantics beyond listing indicator examples and giving sample queries. For high-coverage schemas, baseline is 3, and the description adds no further clarification on parameters.

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?

Description clearly states it computes a technical indicator time series for stocks/forex/crypto via Twelve Data, listing many indicator names (RSI, SMA, EMA, etc.) and providing concrete query examples. It differentiates from sibling tools by positioning itself as the right tool for any technical indicator question and noting it handles one indicator per call.

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

Usage Guidelines5/5

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

Explicitly tells when to use ('PREFER for...', 'right tool for ANY...'), gives clear examples, and instructs how to handle multiple indicators: call once per indicator and do not decline. This provides unambiguous guidance for the agent.

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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TDQS

A3.7/5.0
Disambiguation3/5

While many tools have distinct purposes, there is notable overlap between price, quote, eod, and time_series for price data. Also, the multiple ask_pipeworx variants and deep_research could cause confusion about which to use for factual queries. Some tools like bet_research and polymarket_arbitrage also have overlapping domains.

Naming Consistency4/5

Most tools follow a descriptive snake_case pattern (ai_visibility_check, ask_pipeworx, compare_entities). A few are single words (cryptocurrencies, indices, profile) which is acceptable. No mixing of camelCase or other conventions, so consistent overall.

Tool Count2/5

47 tools is quite high for a single server. While the domain is broad (financial data, prediction markets, SEC filings, etc.), many tools are highly specific (e.g., polymarket_arbitrage, bet_research, scan_dependency) and could be consolidated. The count feels bloated and adds cognitive load.

Completeness4/5

The tool set is impressively comprehensive, covering stocks, forex, crypto, economic data, SEC filings, prediction markets, entity resolution, and even claims validation. Minor gaps exist (e.g., limited drug data despite having some tools), but overall the surface supports a wide range of agentic workflows without obvious missing operations.

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