FinData MCP Server

Overview

FinData is a purpose‑built Model Context Protocol (MCP) server that bridges large language models with high‑quality financial data. By exposing a standardized MCP interface, it allows AI assistants—such as Claude or other LLM‑powered tools—to request real‑time market prices, company fundamentals, and macroeconomic indicators without leaving the conversational context. The server acts as a lightweight data gateway that abstracts away authentication, API rate limits, and provider quirks, letting developers focus on building domain‑specific workflows.

Problem Solved

Financial analysis often requires pulling data from multiple vendors, each with its own authentication scheme and response format. Traditional integration involves writing bespoke connectors, handling pagination, and dealing with inconsistent field names—tasks that consume development time and increase maintenance overhead. FinData consolidates these pain points by offering a single, MCP‑compliant endpoint that accepts structured queries and returns clean, pandas‑friendly data. Developers no longer need to write boilerplate code for each provider; instead they can issue a declarative request and receive the same shape of data regardless of whether it came from Tushare, Wind, or any future provider.

Core Capabilities

• Provider Agnostic: The server supports multiple data sources (currently Tushare, with Wind and DataYes on the roadmap). A simple environment variable selects the provider at runtime.
• Transport Flexibility: FinData works over both standard I/O and Server‑Sent Events (SSE), giving teams the choice between lightweight local execution or scalable HTTP deployment.
• Rich Tool Set: Exposed tools cover daily market data, stock basics, company fundamentals, financial statements (income, balance sheet, cash flow), and macroeconomic series such as GDP, CPI, and LPR. Each tool maps directly to a provider’s API endpoint but returns data in a uniform schema.
• Stateless, Scalable: The server can be launched as a single process or orchestrated behind an API gateway, making it suitable for both prototyping and production environments.

Real‑World Use Cases

1. Investment Research Bots – An LLM can ask for the latest earnings data of a ticker and immediately receive an income statement, enabling on‑the‑fly valuation calculations.
2. Portfolio Management – Automated tools can pull daily price feeds and macro indicators to adjust rebalancing strategies in real time.
3. Financial Education – Chatbots can explain how macro variables influence market movements by fetching up‑to‑date CPI or GDP figures on demand.
4. Compliance & Risk – Auditing systems can query historical cash flow statements to verify financial health before issuing credit.

Integration with AI Workflows

In an MCP‑enabled environment, the server is added to a client’s configuration as a named resource. When an AI assistant receives a user request that requires data, it invokes the appropriate tool via the MCP protocol. The server authenticates with the chosen provider using a token supplied through environment variables, executes the query, and streams back the result. Because the response is delivered in a consistent JSON schema, downstream processing—whether it’s generating a chart, performing statistical analysis, or feeding into another model—is straightforward and error‑free.

Unique Advantages

• Zero Vendor Lock‑In: Switching from Tushare to Wind merely involves changing an environment variable, with no code modifications needed in the AI client.
• Unified Data Model: All outputs conform to a pandas‑friendly structure, eliminating the need for custom parsers.
• Ease of Deployment: The server can run locally via standard I/O or be exposed over SSE for cloud deployment, offering flexibility without compromising performance.
• Extensibility: Adding a new provider or tool is as simple as implementing the corresponding wrapper and updating the configuration, making FinData a future‑proof solution for evolving financial data ecosystems.