Huggingagi MCP Baostock Server

The Huggingagi MCP BaoStock Server bridges the gap between AI assistants and real‑time Chinese stock market data. By exposing a suite of RESTful endpoints over the Model Context Protocol, it allows Claude or other MCP‑enabled agents to retrieve comprehensive market information without leaving the conversational context. This eliminates the need for developers to build custom data pipelines or maintain separate financial APIs, streamlining research workflows and accelerating the development of finance‑focused applications.

At its core, the server taps into the BaoStock library—a lightweight, community‑maintained interface to China’s primary stock exchanges. It provides a range of endpoints that cover every stage of financial analysis: from basic company profiles and industry classifications to granular K‑line histories, dividend schedules, quarterly financial metrics (profitability, operating efficiency, growth), index movements, and valuation ratios. Each endpoint returns data in a consistent JSON format that can be immediately consumed by an AI model, enabling prompt generation of insights, summaries, or visualizations.

Key capabilities include:

• Real‑time and historical data: Fetch daily, weekly, or monthly price bars with support for pre‑adjusted pricing.
• Financial statements: Pull quarterly profitability, operating and growth metrics for any listed company.
• Corporate actions: Access dividend and split histories to evaluate shareholder returns.
• Market breadth: Retrieve index constituents and their performance over custom date ranges.
• Industry context: Query sector classifications to perform peer comparisons or thematic analysis.

Developers can integrate the server into their AI workflows in several practical ways. For example, a portfolio management bot can ask for the latest earnings of a set of holdings and receive instant JSON responses that the model can turn into risk reports. A financial education assistant could generate step‑by‑step tutorials on interpreting K‑line patterns by pulling live data for illustrative examples. Moreover, because the server follows MCP conventions, it can be chained with other tools—such as natural language generation or charting services—to build end‑to‑end analytical pipelines without manual data handling.

The standout advantage of this MCP implementation is its simplicity and breadth. It requires only Python 3.10+, the baostock package, and pandas—no heavy database setup or API key management. Once running, the server exposes a unified interface that is immediately consumable by any MCP‑compatible assistant. This makes it an ideal backbone for finance teams, quantitative analysts, or hobbyists who want to embed live Chinese market data into conversational AI applications with minimal friction.