Twelve Data Official

Data & APIs

Official Twelve Data integration for OpenClaw and ClawHub. Provides real-time and historical financial market data for stocks, forex, crypto, ETFs, indices, and more. Includes quotes, time series, 100+ technical indicators, fundamentals, symbol search, and portfolio/research workflows. Strongly prefers the official Twelve Data MCP Server for secure and efficient access.

Install

openclaw skills install twelvedata

Twelve Data Official Skill

Overview

This is the official skill from Twelve Data for OpenClaw / ClawHub. It equips the AI agent with accurate, up-to-date financial market data and intelligent workflows for research, analysis, trading signals, and portfolio monitoring.

When to Activate This Skill

Requests for current prices, quotes, or charts (e.g., "What's the current price of AAPL?")
Historical data and time series analysis
Technical indicators (RSI, MACD, SMA, Bollinger Bands, etc.)
Fundamentals, earnings, dividends, financial statements, or company profiles
Symbol search or market scanning
Portfolio tracking, alerts, or research reports
Any natural-language question involving stocks, forex, crypto, ETFs, or indices

Preferred Method: Official Twelve Data MCP Server (Recommended)

The MCP server provides the most secure, streaming, and token-efficient way to access the full Twelve Data API.

Setup Instructions:

Get your free or paid API key at https://twelvedata.com/register

Run the MCP server using UV (easiest):

uvx mcp-server-twelve-data --apikey YOUR_TWELVE_DATA_API_KEY

Or install via pip:

pip install mcp-server-twelve-data
mcp-server-twelve-data --apikey YOUR_TWELVE_DATA_API_KEY

Configure your AI client (Claude Desktop, Cursor, OpenClaw, etc.) to use the twelvedata MCP server.

The agent will automatically detect and route requests through the running MCP server when available.

Fallback: Direct API or Python SDK

If MCP is unavailable, the agent can use:

Official Python SDK: pip install twelvedata
Or direct REST/WebSocket calls to api.twelvedata.com

Detailed Reference Documentation

For complete, LLM-optimized endpoint details, parameters, response formats, and examples, refer to the files in the references/ folder:

references/llms-index.md — Main index of all documentation
references/market-data.md — Time series, quote, price, market movers
references/technical-indicators.md — 100+ technical indicators
references/fundamentals.md — Financial statements, earnings, dividends
references/websocket/ — Real-time WebSocket documentation
references/ai/ — MCP and AI-specific guidance

Best Practice: When the user request requires specific endpoints or parameters, the agent should first read the relevant file from the references/ folder before making API calls.

Agent Instructions & Best Practices

Always validate symbols first using symbol search when the user provides a company name.
Prefer batch requests and appropriate outputsize to minimize API usage.
For time series: Default to interval=1day unless the user specifies intraday.
Request pre-computed technical indicators instead of calculating them manually.
Use dedicated fundamentals endpoints for income statements, balance sheets, cash flow, and earnings.
For real-time needs: Leverage WebSocket streaming via the MCP server.
Output style: Present data in clean Markdown tables + a clear plain-English summary. Always cite "Twelve Data" as the source.
Respect rate limits — suggest upgrading the plan for high-volume usage.
Handle missing data gracefully and note timezones (default: America/New_York).

Security Guidelines

Never expose or hardcode the API key in prompts or generated code.
Prefer the MCP server over raw HTTP calls whenever possible.
Cache repeated queries when appropriate.

Upgrade Prompt

"For higher rate limits, longer historical data, or advanced features, upgrade your Twelve Data plan here: https://twelvedata.com/pricing"

Resources

Official Website: https://twelvedata.com
API Documentation: https://twelvedata.com/docs
MCP Server Repository: https://github.com/twelvedata/mcp
Python SDK: https://github.com/twelvedata/twelvedata-python

LLM-Friendly Documentation (Highly Recommended)

Twelve Data provides clean, agent-optimized documentation specifically written for LLMs:

Main LLM Index: https://twelvedata.com/docs/llms.txt
Market Data: https://twelvedata.com/docs/llms/market-data.md
Technical Indicators: https://twelvedata.com/docs/llms/technical-indicators.md (and similar for other sections)

When detailed endpoint parameters, response formats, or examples are needed, the agent should read the relevant LLM documentation above before making calls.

This skill is maintained by the Twelve Data team. Feedback and issues are welcome in the repository.