MCPez

MCPez is a web‑based command‑proxy management platform that turns diverse backend services—ranging from local scripts to remote APIs—into uniform, machine‑readable tools. By exposing each service through a standardized SSE or STDIO proxy, MCPez eliminates the friction that normally accompanies integrating disparate micro‑services into AI agents. Developers can now treat a collection of utilities as a single, well‑documented endpoint that any LLM‑powered assistant can invoke without custom adapters.

The core value of MCPez lies in unifying management and standardization. A typical AI agent needs to call many specialized tools, but each tool often has its own authentication scheme, data format, and deployment quirks. MCPez bundles these details behind a simple web UI where users create applications that bundle multiple services. Each service can be configured with headers, base URLs, command arguments, or environment variables, and then exposed as a stable proxy address. Once registered, an agent can reference the proxy by name or ID, confident that the underlying implementation will remain stable and that any required credentials are handled locally.

Key capabilities include:

• Web UI for creation, configuration, and monitoring of applications and their constituent services.
• Support for both SSE (remote HTTP streams) and STDIO (local command‑line processes)**, allowing almost any executable or API to be turned into an MCP tool.
• Import/export of application JSON and reusable tool templates, fostering rapid prototyping and sharing.
• Real‑time status dashboards that show running service IDs, addresses, health, and logs.
• Integrated AI Playground where an LLM can be coupled with the defined tools for function‑calling experiments.

Real‑world scenarios that benefit from MCPez include:

• A data‑science team exposing a suite of Python scripts (data cleaning, model training) as tools for a conversational agent that assists analysts.
• A customer‑support bot that needs to query both an internal ticketing API (SSE) and a local script that scrapes logs (STDIO).
• A rapid‑prototype environment where new services can be added, tested in the playground, and then shared with teammates via exported configuration packs.

MCPez’s Docker support guarantees that the platform can be deployed consistently across development, staging, and production environments. By centralizing service definitions and exposing them through a clean protocol, MCPez removes the need for bespoke client libraries, reduces integration overhead, and helps teams avoid the “MCP island” problem where single services become isolated silos. This makes it an indispensable tool for developers building sophisticated, multi‑tool AI assistants that rely on reliable, well‑managed backend capabilities.