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emquant-docs-mcp

v1.0.0

Published

MCP server for EMQuant (东方财富量化终端) documentation - provides search, query and browse tools for Python & C# SDK docs

Readme

emquant-docs-mcp

English | 中文

npm version License: MIT

MCP server for EMQuant (东方财富量化终端 / Goldminer Quant) documentation

Built on the Model Context Protocol (MCP), this server lets AI assistants directly query the complete EMQuant SDK documentation, covering both Python and C# APIs.

Features

  • 📚 60 Documents — Covers getting started guides, Python SDK, C# SDK, data references, and FAQ
  • 🔍 Full-text Search — Search across all docs with multi-keyword intersection support
  • 🎯 API Lookup — Find function signatures, parameters, and return types by name
  • 📖 Smart Pagination — Large documents auto-paginate (~20K chars/page) with table of contents
  • 🏷️ Categorized Browsing — Documents organized into 8+ categories for easy discovery
  • Quick Start Guides — Built-in step-by-step guides for common tasks (quotes, trading, backtesting, etc.)
  • 🖼️ Offline Images — 76 documentation screenshots bundled locally

Documentation Coverage

| Category | Count | Description | |----------|-------|-------------| | Getting Started | 2 | Terminal setup, strategy development guide | | Python SDK | 27 | Architecture, data structures, base functions, market data, trading, events, enums, error codes | | C# SDK | 23 | Quick start, typical scenarios, base class, member functions, data queries, data structures, enums, error codes | | Data Reference | 6 | Field definitions for stocks, funds, futures, indices, convertible bonds, sectors | | Smart Strategy | 1 | Smart strategy features | | FAQ | 1 | Frequently asked questions |

Available Tools

| Tool | Description | |------|-------------| | list_topics | List all available documents grouped by category | | search_docs | Full-text keyword search (space-separated keywords, intersection match) | | search_api | Search API function definitions (searches both Python and C# docs) | | get_document | Get full document content (auto-paginated for large files) | | get_section | Get a specific section from a document (includes subsections) | | get_enum_constants | Get enum constant definitions (supports language param: python/csharp) | | get_error_codes | Get error codes (supports language param: python/csharp) | | get_faq | Get FAQ (no args = TOC; specify topic for filtered results) | | get_quickstart | Get quick start guides for common tasks (quotes/trading/backtesting/etc.) |

Installation

Prerequisites

  • Python 3.10+
  • MCP SDK: pip install mcp

Option 1: Via npx (Recommended)

No installation needed — just configure your MCP client:

{
  "mcpServers": {
    "emquant-docs": {
      "command": "npx",
      "args": ["-y", "emquant-docs-mcp"]
    }
  }
}

Option 2: Global Install

npm install -g emquant-docs-mcp

Then use in your configuration:

{
  "mcpServers": {
    "emquant-docs": {
      "command": "emquant-docs-mcp"
    }
  }
}

Option 3: From Source

git clone https://github.com/erwanjun/emquant-docs-mcp.git
cd emquant-docs-mcp
pip install mcp
python server.py

Client Configuration

VS Code (GitHub Copilot)

Add to .vscode/mcp.json or user settings.json:

{
  "mcp": {
    "servers": {
      "emquant-docs": {
        "command": "npx",
        "args": ["-y", "emquant-docs-mcp"]
      }
    }
  }
}

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

{
  "mcpServers": {
    "emquant-docs": {
      "command": "npx",
      "args": ["-y", "emquant-docs-mcp"]
    }
  }
}

Cursor

Add in Cursor Settings → MCP Servers with the same configuration as above.

Usage Examples

After connecting the MCP server, you can ask your AI assistant:

  • "Show me the parameters for order_volume"
  • "How to get historical K-line data with Python?"
  • "How to subscribe to market data in C#?"
  • "List all available enum constants"
  • "How to configure backtesting mode?"
  • "What does error code 1003 mean?"

Recommended Workflow

  1. Browse — Call list_topics to discover available documents
  2. Search — Use search_docs for keyword search across all docs
  3. API Lookup — Use search_api to find function definitions
  4. Deep Read — Use get_document or get_section for detailed content
  5. Quick Reference — Use get_quickstart for common task step-by-step guides
  6. Helpersget_enum_constants / get_error_codes / get_faq

Development

# Clone the repository
git clone https://github.com/erwanjun/emquant-docs-mcp.git
cd emquant-docs-mcp

# Install dependencies
pip install mcp

# Run the server
python server.py

# Run tests
python test_changes.py

License

MIT