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MCPez - 微服务命令代理管理平台 MCP Server

@Veallym0n

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Veallym0n

About MCPez - 微服务命令代理管理平台 MCP Server

micro unified mcp server

MCPez - 微服务命令代理管理平台 MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "mcpez": {
      "command": "docker",
      "args": [
        "build",
        "-t",
        "MCPez",
        "."
      ]
    }
  }
}

MCPez - 微服务命令代理管理平台 MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is MCPez - 微服务命令代理管理平台?

MCPez is a Web-based management platform that simplifies defining, configuring, managing, and monitoring backend services such as AI models, local scripts, or remote APIs. It exposes these services through standardized proxy interfaces (SSE or STDIO), making them easy to integrate by applications—especially AI agents that need to call tools.

How to use MCPez - 微服务命令代理管理平台?

Deploy using Docker: build the image with docker build -t MCPez and run the container with docker run -d -p 8088:80 --name MCPez-instance -v MCPez_data:/data MCPez. Access the Web UI at http://localhost:8088. Use the service management page to create and manage applications, and the AI Playground to test tool/function calling with AI models.

Key features of MCPez - 微服务命令代理管理平台?

  • Web UI for managing applications and services.
  • Supports SSE and STDIO proxy service types.
  • Import/export application configurations as JSON.
  • Save service configurations as reusable tool templates.
  • AI Playground for testing tool calling with AI models.
  • Docker support for easy containerized deployment.

Use cases of MCPez - 微服务命令代理管理平台?

  • Unify multiple MCP services under a single management platform.
  • Expose local scripts or remote APIs as tools for AI agents.
  • Quickly prototype and debug AI agent tool integrations via the AI Playground.
  • Share validated service configurations as reusable templates across teams.

Frequently asked questions

1

What runtime dependencies does it require?

Requires Docker and Docker Compose for containerized deployment. The container bundles Nginx and a Python environment.

2

How are service configurations persisted?

Data is stored in a SQLite database. Use a Docker volume (`MCPez_data`) mounted at `/data` to persist configurations.

3

Does it support STDIO (local command) services?

Yes. You can configure STDIO services with command, arguments, and environment variables, and start/stop them from the Web UI.

4

How do I integrate an AI model with the platform?

Use the AI Playground (`chat.html`) to configure API keys, base URL, and model name. Set the MCP server address (e.g., `http://localhost:8088/mcp/<app_id>/sse`) to enable tool calling.

5

Can I share tool configurations with others?

Yes. Export an application's entire configuration as JSON or save individual service configurations as tool templates for reuse and sharing within your team or community.

Comments

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