Historical Price And Ohlcv Data Retrieval

via “market-data-query-and-historical-analysis”

Alpaca’s official MCP Server lets you trade stocks, ETFs, crypto, and options, run data analysis, and build strategies in plain English directly from your favorite LLM tools and IDEs

Unique: Integrates Alpaca's StockHistoricalDataClient directly, supporting batch queries for multiple symbols and flexible timeframe selection (minute through month) without requiring separate API calls per symbol or timeframe. The tool set exposes both bars (OHLCV) and quotes (bid/ask) as distinct tools, allowing LLMs to choose the appropriate data type for their analysis.

vs others: More efficient than tools that query one symbol at a time because batch queries reduce API round-trips, and includes native support for multiple timeframes which generic data APIs often require manual aggregation to provide.

via “ohlcv data retrieval”

Get real-time crypto prices, 24h stats, OHLCV, and order book depth. Ask for quick quotes or a synthesized overview with trend and volume insights. Monitor markets and inform trading decisions with up-to-date data.

Unique: Incorporates a time-series database for efficient storage and retrieval of OHLCV data, optimizing performance for analytical queries.

vs others: More efficient for historical data queries than traditional relational databases due to time-series optimizations.

via “historical-price-data-with-technical-indicators”

MCP Server for stock and crypto. 提供股票、加密货币的数据查询和分析功能MCP服务器 ## 功能 - **股票搜索**: 根据公司名称、股票名称等关键词查找股票代码 - **股票信息**: 获取股票的详细信息，包括价格、市值等 - **历史价格**: 获取股票、加密货币历史价格数据，包含技术分析指标 - **相关新闻**: 获取股票、加密货币相关的最新新闻资讯 - **财务指标**: 支持A股和港股的财务报告关键指标查询

Unique: Combines historical data fetching with on-demand technical indicator computation in a single MCP tool, eliminating the need for agents to call separate charting or math libraries — indicators are pre-calculated or computed server-side using standard financial formulas

vs others: Faster than agents computing indicators client-side and more flexible than static chart APIs — supports custom date ranges and multiple indicator types in one call

via “historical ohlcv time-series retrieval with configurable intervals”

** - Interact with [Twelve Data](https://twelvedata.com) APIs to access real-time and historical financial market data for your AI agents.

Unique: Exposes Twelve Data's multi-interval historical API through MCP, allowing agents to request specific date ranges and timeframes without managing pagination or API rate limits manually; abstracts away subscription-tier differences in data availability

vs others: More flexible than static data exports because agents can request arbitrary date ranges on-demand; more cost-efficient than calling raw APIs repeatedly because MCP caching can reduce redundant requests

** - Access real-time DEX analytics across 20+ blockchains with [DexPaprika API](https://docs.dexpaprika.com), tracking 5M+ tokens, pools, volumes, and historical market data. Built by CoinPaprika.

Unique: Provides normalized OHLCV data across multiple DEX protocols and blockchains with standardized time intervals, eliminating need to aggregate raw transaction data or query individual DEX subgraphs for price history

vs others: More comprehensive than single-DEX price feeds; enables cross-chain price analysis that individual DEX APIs cannot provide

via “historical stock price data retrieval with date range filtering”

MCP server: yahoo-finance-mcp

Unique: Integrates historical data retrieval as an MCP tool, allowing agents to autonomously fetch and analyze multi-year price histories without requiring manual data downloads or external data pipeline setup. Abstracts pagination and date validation logic within the MCP server.

vs others: Faster agent iteration than manual CSV imports or direct API calls — agents can request historical data inline during reasoning, enabling dynamic analysis without context switching to external tools.

via “historical ohlcv time-series retrieval with interval selection”

MCP server: yfinance-mcp-server2

Unique: Parameterizes yfinance's interval selection (daily/weekly/monthly) as MCP tool arguments, allowing agents to dynamically request different granularities without code changes; converts pandas DataFrames to JSON with explicit timestamp normalization for agent consumption

vs others: More flexible than fixed-interval endpoints; avoids agents needing to manage pandas or numpy dependencies directly

via “historical price and ohlcv candlestick data retrieval”

** - Official [CoinGecko API](https://www.coingecko.com/en/api) MCP Server for Crypto Price & Market Data, across 200+ blokchain networks and 8M+ tokens.

Unique: Exposes CoinGecko's aggregated historical price data via MCP with configurable candlestick granularities, eliminating need for developers to maintain separate time-series databases or integrate multiple exchange historical APIs

vs others: Provides unified historical data across 15,000+ coins and 1,000+ exchanges in a single query, whereas alternatives like Binance API typically cover only their own exchange data

via “historical ohlcv data aggregation with configurable time intervals”

MCP server: yfinance-mcp-server

Unique: Exposes yfinance's pandas-based resampling as an MCP tool, allowing agents to request pre-aggregated historical data without managing DataFrame transformations themselves. Automatically handles timezone normalization and market calendar adjustments.

vs others: More flexible than static CSV exports because agents can request arbitrary date ranges and intervals on-demand; more accessible than raw yfinance because MCP abstracts pandas/numpy complexity into simple JSON responses.

via “real-time and historical stock price retrieval with interval-based aggregation”

** - Stock market API made for AI agents

Unique: Provides interval-based price aggregation (daily/weekly/monthly) natively through the API rather than requiring client-side resampling, reducing data transfer and computation overhead for agents performing multi-timeframe analysis.

vs others: More efficient than agents querying raw tick data and aggregating locally because aggregation happens server-side; more reliable than web scraping stock price websites due to direct API access to normalized, deduplicated market data.

via “historical data querying”

All the server endpoints for API Bricks CoinAPI and FinFeedAPI products

Unique: Incorporates a caching layer to enhance performance and reduce latency when accessing historical data.

vs others: Faster than direct queries to individual data sources due to built-in caching and indexing.

via “price history visualization and trend analysis”

Free AI Price Tracker - Track any price of any product at any store using AI

via “historical-price-data-retrieval”