Lava MCP (熔岩) vs SiftingIO: Which Is Better in 2026?

Lava is a structured, read-only data subscription that tracks how hot individual China A-share stocks and concept sectors are in the news flow, based on commentary from institutions and market influencers. Each record gives the related concepts, tickers, a short summary of key points and an anonymous source ID, so quant traders and AI agents can spot rising themes early, rank sectors by attention, and feed the signal into screening, backtests or research workflows.

Connect in minutes: (1) subscribe and get an API key on the website; (2) call the REST API (OpenAPI spec provided) or add the MCP endpoint to Claude, Cursor, or any MCP client; (3) or install the ready-made Agent Skill. Typical uses: daily theme radar, sector heat ranking, stock attention alerts, and context for AI research assistants.

Endpoints: MCP https://lava.ai-dolphin.com/subscribe/v1/mcp ; OpenAPI https://lava.ai-dolphin.com/subscribe/v1/openapi.json ; Docs https://lava.ai-dolphin.com/subscribe/docs ; GitHub https://github.com/dustinliang666-blip/lava-mcp

中文简介：熔岩（Lava）是 A 股股票与概念板块的消息面热度监测数据订阅，提供概念、标的、要点摘要与匿名机构/大V ID，支持 REST API、MCP 与 Agent Skill 三种接入，适合量化研究与 AI Agent 调用。

What problem it solves

Spotting which A-share stocks and concept sectors are heating up in the news flow, in a structured form that quant tools and AI agents can consume.

SiftingIO provides real-time and historical market data for developers building financial applications. The platform covers US stocks and ETFs, forex, commodities and crypto through REST APIs and WebSocket streaming. It is designed for teams that need current prices for an application alongside historical data for research and analysis.

For US stocks, available historical coverage reaches 25+ years. The service includes ETFs as well as individual shares, so a developer can work with both in a market dashboard or research workflow. Forex history also reaches 25+ years, while commodities coverage extends to 20+ years and crypto coverage to 10+ years. These figures describe available dataset depth; the start date and fields depend on the instrument and dataset selected.

The REST API supports applications that request data when it is needed. WebSocket streaming supports applications that receive ongoing real-time updates. Python, TypeScript and Go SDKs provide language-specific ways to integrate the service. Public documentation explains the available endpoints and request fields, giving a team a concrete starting point for testing an integration.

Historical OHLCV bars can be used for price charts, time-series research and backtesting. A useful evaluation starts with a small set of instruments and a defined time range, then checks that the returned data suits the application's requirements. Teams can compare available intervals and fields in the documentation before building a larger data workflow.

SiftingIO also offers a browser-based market data export tool for people who want to analyse selected data in a spreadsheet. CSV and Excel downloads provide a route from the API-backed tool into a workbook, without requiring a custom application for that task.

A free plan is available for developers evaluating the platform, with paid subscriptions for broader usage requirements. SiftingIO is operated by SaltingIO LLC. It serves developers, research teams and financial software builders who need one place to work with several asset classes.

Read the documentation at https://sifting.io/docs, explore the products at https://sifting.io/product, or create a free account at https://sifting.io/register.

What problem it solves

Developers and research teams need real-time prices and historical bars across several asset classes. SiftingIO brings supported US stocks and ETFs, forex, commodities and crypto into one documented API platform, with streaming, SDKs and spreadsheet exports for different workflows.