MCP Auto Tool Switcher

Overview

MCP Auto Tool Switcher addresses a common pain point for developers building AI‑powered applications: the fragmentation of tools, prompts, and resources across multiple MCP servers. When an assistant needs to invoke a function—such as querying a database, calling an external API, or rendering a custom prompt—it must first discover which server hosts that capability and then route the request accordingly. Managing this manually can become tedious, especially in environments where servers are added or removed frequently. Auto Tool Switcher centralizes this process by acting as a unified gateway that automatically discovers, caches, and forwards tool calls to the appropriate underlying MCP server.

At its core, the switcher is a modular MCP server that exposes a tool discovery interface and a passthrough mechanism. Developers can list all available servers, enable or disable them on the fly, and refresh tool catalogs with simple commands (, , etc.). When a tool is invoked, the switcher consults its cache and forwards the request to the correct server without the client needing to know the target address. This decouples AI assistants from hard‑coded server endpoints, enabling dynamic reconfiguration and scaling.

Key capabilities include:

• Cross‑platform support: runs natively on Windows, macOS, and Linux.
• Server lifecycle management: add, remove, enable, or disable servers via MCP tools, with automatic startup of enabled instances at launch.
• Intelligent caching: retains tool metadata locally to reduce discovery latency and network overhead.
• Tool forwarding: seamlessly proxies calls from the switcher to any registered MCP server, preserving request and response semantics.
• Extensible configuration: JSON files (, ) allow declarative server definitions and custom tool scopes.

Typical use cases involve:

• Micro‑service orchestration: a single assistant can invoke tools spread across specialized services (e.g., image generation, data analysis) without hard‑coding URLs.
• Rapid prototyping: developers can spin up new MCP servers locally and immediately expose their tools to the assistant through the switcher.
• Continuous integration pipelines: automated tests can toggle servers on demand, ensuring that only relevant tools are active during a run.

Integrating Auto Tool Switcher into an AI workflow is straightforward: the assistant treats it as a single MCP endpoint. All tool calls are routed through the switcher, which internally handles discovery and forwarding. This abstraction reduces boilerplate in client code and eliminates the need for manual endpoint management.

The standout advantage of this server is its automatic discovery and caching combined with a transparent passthrough layer, which together provide a robust, developer‑friendly interface for managing heterogeneous tool ecosystems. As the project matures—particularly with the planned Electron tray client—it will offer a graphical management layer, further simplifying server administration for teams of all sizes.