FinanceMCP

FinanceMCP: A Unified Architecture for Multi-Source Financial Data Integration in AI-Driven Analysis Abstract The convergence of artificial intelligence and financial technology has spurred new systems that leverage diverse data sources for market analysis. However, most existing approaches handle only isolated data types (e.g. price histories or news sentiment) and struggle to integrate the breadth of financial information available[1]. We present FinanceMCP, an open-source platform that fuses multi-source financial data – including stock and cryptocurrency markets (via Tushare and Binance APIs), technical indicators, real-time news, and macroeconomic metrics – within a unified architecture. FinanceMCP provides a standardized Model-Context Protocol (MCP) interface that empowers large language models and other AI agents (e.g. Claude) to retrieve up-to-date, multi-dimensional financial data seamlessly for analysis. Key features of the system include an intelligent technical indicator engine (automatically pre-fetching historical data for accurate indicator computation), real-time market feeds, and integrated news and macro data scraping, all accessible through natural-language queries. By unifying structured market data with unstructured information, FinanceMCP addresses the long-standing challenge of heterogeneous financial data integration[2]. The system’s design, situated at the intersection of AI and FinTech, enables more comprehensive analytic capabilities – for example, an AI agent can simultaneously evaluate price trends, company fundamentals, recent news, and economic indicators to generate well-rounded insights. This paper discusses the research background and system architecture of FinanceMCP, highlights its contributions to multi-source data fusion in financial AI, and outlines application prospects such as AI-driven decision support and intelligent financial assistants. The remainder of the paper is organized as follows: we first motivate the problem and related work, then detail the FinanceMCP architecture and data integration approach, and finally discuss use cases and future research directions. Introduction Integrating diverse information sources is a fundamental challenge in modern financial analysis. Market behavior is influenced by a wide spectrum of data streams – from high-frequency price ticks and technical indicators to news feeds, social media, and macroeconomic reports[3][4]. Traditional quantitative models and early machine learning approaches typically focus on a single type of data or a narrow feature set, such as historical prices or investor sentiment, in isolation[1]. While these specialized models can capture particular patterns, they often overlook the interdependencies among different factors (e.g. how news events, economic shifts, and technical trends jointly impact asset prices)[1][5]. Recent surveys and studies underscore that fully understanding market movements requires combining multiple modalities of financial data – including text, time-series, and fundamental data – and identifying their complex interactions[3][6]. In practice, however, automatically acquiring and analyzing such heterogeneous data at scale remains difficult[3]. This gap has limited the depth and breadth of AI-driven financial insights, highlighting the need for integrated systems that can seamlessly fuse multi-source information for more robust analysis. Meanwhile, the rise of large language models (LLMs) and advanced AI agents has opened new possibilities for financial decision support and analysis. LLMs have demonstrated the ability to interpret financial texts, perform ratio analysis, and even generate investment recommendations when provided with the right data and context[7][8]. For instance, recent research showed that GPT-4 can parse company filings and extract key metrics via chain-of-thought prompts, achieving interpretable results on earnings forecasts[7]. Other studies have used LLMs to produce trading signals or stock ratings from combined sources like reports and news, in some cases rivaling human analysts[8]. These advancements suggest that AI systems, particularly LLM-based ones, can serve as powerful “financial analysts” – but only if they are equipped with timely, comprehensive data. A critical limitation of current LLMs is their static knowledge: once trained, an LLM’s internal knowledge base remains fixed and quickly becomes outdated in a fast-moving domain like finance[9]. Indeed, even state-of-the-art models have knowledge cut-off dates and cannot access new information unless explicitly provided[10]. This issue, combined with the limited context window of many LLMs, means that important real-time developments (e.g. sudden market news or economic releases) may be missed or not properly understood by the model[11][10]. As a result, there is a strong motivation to develop systems that can bridge LLMs with live, multi-faceted financial data – enabling continuous, up-to-date analysis that leverages the full spectrum of available information. Several lines of recent research have started to address aspects of this integration problem. For example, retrieval-augmented generation (RAG) techniques have been proposed to supply language models with external documents and data on demand[12]. In the financial domain, RAG-based pipelines tailored to tasks like stock analysis have shown the value of feeding models with relevant filings, news articles, and reports[12][13]. Knowledge graph approaches have also emerged: one study constructed a dynamic financial knowledge graph that automatically incorporates real-time company fundamentals and events, in order to support LLM-based stock trend prediction[14]. This graph-based system, FinKario, demonstrated improved accuracy by overcoming the slow update cycles of static knowledge bases and keeping information timely[15][16]. These efforts reinforce a key insight – connecting AI models to up-to-date, structured representations of financial information yields significant benefits. Nonetheless, existing solutions often focus on specific data sources or require complex pipelines (e.g. extracting knowledge from lengthy reports into graphs)[16]. There remains a gap in general-purpose frameworks that simply and efficiently unify multiple data types (market data, technical indicators, news text, macroeconomic figures, etc.) in a form directly usable by LLMs and other AI tools[13]. In other words, the field lacks an accessible system architecture that can serve as a broad financial data hub for intelligent analysis engines. In this paper, we introduce FinanceMCP, a system designed to fill this gap by providing a unified, multi-source financial data platform for AI-driven analysis. FinanceMCP (Financial Market Data Model-Context Protocol) is an open-source architecture that consolidates a wide array of financial data through a single interface. It integrates over forty data APIs from sources like Tushare (for equities, funds, bonds, and economic indicators) and Binance (for cryptocurrency markets), among others, encapsulating them behind a coherent protocol. The system is capable of retrieving real-time price quotes, historical time series, and calculating popular technical indicators on-the-fly (such as MACD, RSI, KDJ, Bollinger Bands, and moving averages) using its intelligent indicator engine. It also gathers unstructured content including financial news articles (via news search and scraping of mainstream media) and macroeconomic data (e.g. GDP, CPI, PMI from official statistics) into a unified output format. Crucially, FinanceMCP exposes this rich data fusion through a standardized MCP interface that can be directly queried by large language models and other AI agents. This design allows an LLM (like OpenAI GPT or Anthropic Claude) to ask complex analytical questions in natural language – for example, “What is the recent technical and fundamental outlook for stock X, and how might recent news and macroeconomic trends affect it?” – and receive an assembled answer with supporting data, all via the FinanceMCP backend. By abstracting away the complexities of calling disparate APIs and merging results, FinanceMCP enables AI models to focus on reasoning over the data, while the system ensures that the model’s knowledge is continuously refreshed with the latest multi-dimensional financial information. The FinanceMCP architecture emphasizes modularity and extensibility in the context of financial AI systems. At its core is a unified data retrieval and computation engine that handles requests from AI agents under a common protocol. This engine performs several steps: interpreting the query (including any technical indicator specifications), fetching or calculating the required data from the appropriate source modules, and packaging the results in a machine-readable yet concise format. For technical indicators, the system automatically determines how much historical data is needed and fetches it to avoid issues like NaN values at the start of indicator series, a known problem if not enough history is provided. This “smart pre-fetch” capability ensures that even complex indicator computations are accurate and ready for immediate use by the model. Furthermore, the platform covers multiple financial markets and instruments – currently spanning Chinese A-shares, U.S. and Hong Kong stocks, global indices, forex and commodity futures, mutual funds, bonds, and digital assets – giving AI analysts a comprehensive view of global markets through one system. It also integrates real-time news by querying financial news sources and macro-economic indicators (such as GDP growth rates, CPI inflation, PMI, interest rates) which provide the broader context often crucial for interpreting market conditions[4]. This breadth of coverage distinguishes FinanceMCP from prior siloed approaches and reflects the real-world needs identified in literature for combining micro-level and macro-level data[5]. Contributions: In summary, this work makes the following contributions to the intersection of AI and financial technology: (1) We design and implement a novel multi-source financial data integration system (FinanceMCP) that aggregates a broad range of data types and delivers them through a unified protocol suitable for LLM consumption. Unlike previous frameworks that handle one or few data modalities, FinanceMCP seamlessly merges structured data (market prices, fundamentals, economic metrics) with unstructured data (news text, reports) in real time, improving the scope of information available for analysis[2]. (2) We introduce a modular system architecture emphasizing data fusion and extensibility, including an intelligent technical indicator engine and support for real-time updates. The architecture is designed to handle the challenges of frequency mismatch (e.g. aligning low-frequency macro data with high-frequency market data) and data consistency, which earlier studies noted as a significant challenge in multi-source integration[17][18]. (3) We demonstrate how FinanceMCP can serve large language models and other AI agents as a powerful back-end, effectively bridging the gap between natural language analysis and quantitative data. By using the MCP interface, an LLM can dynamically query for needed facts or figures (bypassing its internal knowledge cutoff[10]) and ground its responses in fresh data, thereby reducing model hallucinations and improving decision relevance. (4) We validate the system’s utility through illustrative use cases (e.g. an AI assistant performing a holistic stock analysis by combining technical signals, financial news, and macro indicators via FinanceMCP). These examples highlight the potential of our approach to enhance investment research, algorithmic trading strategies, and personalized financial advising with AI. We believe FinanceMCP’s open-source release will accelerate research and development at the nexus of AI and finance by providing a readily extensible foundation for multi-source data analytics. The rest of this paper is organized as follows. Section 2 reviews related work in multi-source financial data analysis and AI-driven financial systems, positioning our approach within the literature. Section 3 details the FinanceMCP system architecture, including its data integration modules and the MCP communication protocol. Section 4 presents experimental scenarios and case studies demonstrating the capabilities of FinanceMCP when interfaced with large language models. Section 5 discusses the broader implications of our system, potential applications in industry and research, and future enhancements (such as incorporating additional data sources or improving real-time performance). Finally, Section 6 concludes the paper, summarizing our contributions and outlining directions for subsequent research at the intersection of artificial intelligence and financial data infrastructure. [1] [2] [4] [5] [7] [8] [10] [12] [13] [17] [18] MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents https://arxiv.org/html/2502.00415v1 [3] Web Media and Stock Markets: A Survey and Future Directions from a Big Data Perspective - University of Arizona https://experts.arizona.edu/en/publications/web-media-and-stock-markets-a-survey-and-future-directions-from-a [6] Modeling the Momentum Spillover Effect for Stock Prediction via Attribute-Driven Graph Attention Networks https://cdn.aaai.org/ojs/16077/16077-13-19571-1-2-20210518.pdf [9] [11] [14] [15] [16] FinKario: Event-Enhanced Automated Construction of Financial Knowledge Graph https://arxiv.org/html/2508.00961v1