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Enterprise MCP Server & Client

@sanjay-si

代码仓库

基本信息

分类

其他

传输方式

stdio

作者

sanjay-si

关于 Enterprise MCP Server & Client

MCP Client & Server

#enterprise-mcp#model-context-protocol#llm-integration

Enterprise MCP Server & Client 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "enterprise_mcp": {
      "command": "python",
      "args": [
        "-m",
        "src.mcp_server.main"
      ]
    }
  }
}

Enterprise MCP Server & Client 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Enterprise Model Context Protocol (MCP) Server & Client?

A comprehensive enterprise-grade implementation of the Model Context Protocol (MCP) for connecting LLMs with enterprise tools and data sources. It is cost-optimized for production, allows zero-cost testing without any LLM API keys, and supports either OpenAI GPT or Anthropic Claude with a single API key.

How to use Enterprise Model Context Protocol (MCP) Server & Client?

Clone the repository, then start all services with Docker Compose or manually install server and client components. Set environment variables (choose one LLM API key or none for zero-cost testing) and run the server (e.g., python -m src.mcp_server.main) and client (e.g., python examples/demo.py or python examples/demo_no_llm.py). Connect the client to the server via WebSocket and use the provided Python examples to invoke enterprise tools.

Key features of Enterprise Model Context Protocol (MCP) Server & Client

  • Full JSON‑RPC 2.0 MCP specification compliance
  • Enterprise tools: database queries, file operations, API integrations
  • JWT authentication and role‑based access control
  • Prometheus metrics and structured logging
  • Multi‑LLM support (OpenAI GPT, Anthropic Claude)
  • Async/await architecture and robust error handling
  • Zero‑cost testing mode with no API keys required
  • Optimized models (gpt‑4o‑mini, claude‑3.5‑haiku) for 97% cost savings

Use cases of Enterprise Model Context Protocol (MCP) Server & Client

  • Query an enterprise database and generate a report file via one LLM request
  • Perform sandboxed file system operations (read, write, create) through MCP tools
  • Make authenticated HTTP requests to external enterprise APIs
  • Enable secure, monitored access to enterprise data for AI assistants

常见问题

1

What runtime dependencies are required?

Python 3.11+ is required. PostgreSQL and Redis are optional – only needed for enterprise database features and caching/session management, respectively.

2

Can I test the system without any LLM API keys?

Yes. The implementation supports zero‑cost testing that works without any LLM API keys. You can run `python examples/demo_no_llm.py` to try enterprise tools without connecting to an LLM.

3

How do I configure authentication and security?

Set `JWT_SECRET_KEY`, `JWT_ALGORITHM`, and `JWT_EXPIRATION_HOURS` in your environment. The server enforces JWT authentication and role‑based access control, with SQL injection protection and sandboxed file access.

4

What transport does the MCP server use?

The server communicates over WebSocket. The client connects to `ws://localhost:8000/mcp` by default. HTTPS/WSS is supported for encrypted communications.

5

What monitoring is available?

Prometheus metrics are exposed at `GET /metrics`, including request counts, request duration, active connections, and tool execution statistics. A basic health check endpoint is also provided at `GET /health`.

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