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Twelvedata

by io.github.pipeworx-io

Time Series

time_series
Read-onlyIdempotent

Twelve Data OHLC time series for a stock, forex, crypto, or ETF symbol. Requires symbol and interval (e.g. '1min', '1h', '1day'). Returns timestamped open/high/low/close + volume.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
orderNoSort order: "asc" or "desc" (default desc, newest first).
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").
end_dateNoOptional end of range, "YYYY-MM-DD" or "YYYY-MM-DD HH:MM:SS".
exchangeNoOptional exchange filter (e.g. "NASDAQ", "NYSE", "Binance").
intervalYesBar interval: 1min, 5min, 15min, 30min, 45min, 1h, 2h, 4h, 1day, 1week, or 1month.
timezoneNoOptional timezone, e.g. "America/New_York" or "UTC".
outputsizeNoNumber of data points to return (1–5000, default 30).
start_dateNoOptional start of range, "YYYY-MM-DD" or "YYYY-MM-DD HH:MM:SS".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoResponse code
statusNoResponse status
messageNoResponse message

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, confirming safe, non-destructive behavior. The description adds that the tool returns historical OHLC+volume data, aligning with annotations and providing context beyond structured fields.

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 two sentences: first states purpose and asset coverage, second specifies required inputs and output. Efficient, front-loaded, and free of unnecessary information.

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 rich input schema (100% coverage) and existence of an output schema, the description fully conveys the tool's core functionality. All key aspects (purpose, required params, output shape) are covered without over-explaining.

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?

Schema coverage is 100% with detailed parameter descriptions. The description reinforces required parameters (symbol, interval) and gives examples of valid intervals and symbol formats, adding practical guidance without redundancy.

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 identifies the tool as providing Twelve Data OHLC time series data for multiple asset types (stocks, forex, crypto, ETFs), specifying the output includes timestamped open/high/low/close and volume. It distinguishes from sibling tools like 'quote' or 'eod' by focusing on historical intraday/aggregated bars.

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 indicates the tool is for historical OHLC data and lists supported asset types, allowing inference of when to use it. However, it does not explicitly state when not to use it or suggest alternatives like 'price' or 'technical_indicator' for other use cases.

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.

Resources