YAMCP

YAMCP, short for Yet Another MCP Workspace Manager, addresses a common friction point in AI‑assisted development: the scattered landscape of Model Context Protocol (MCP) servers. In modern workflows, a single AI assistant may need to tap into multiple specialized services—one for code generation, another for design assets, and yet another for research data. Traditionally each of these services runs its own MCP server, requiring developers to configure and maintain separate connections in every client application. YAMCP consolidates these disparate servers into logical workspaces (or YAMs), presenting them as a single unified gateway. This means an AI assistant can connect once and automatically access all the tools it needs, dramatically simplifying configuration and reducing runtime overhead.

At its core, YAMCP manages three layers of abstraction. First, it keeps a registry of MCP servers, whether they are local or remote, and offers CRUD operations to add, list, or remove them. Second, it lets developers group these servers into workspaces that reflect either a functional domain (e.g., “coding” or “design”) or an application boundary (e.g., a workspace tailored for Cursor, Claude, or GitHub Copilot). Third, the gateway component acts as a local MCP server that forwards requests to the appropriate bundled servers based on the active workspace. The gateway exposes a single endpoint to AI clients, hiding the complexity of multiple back‑ends while preserving full protocol fidelity.

Key capabilities include:

• Unified configuration – one command spins up a gateway that aggregates all servers in the chosen workspace.
• Centralized logging – every request and response across all bundled servers is captured in a single log store, making debugging a breeze without hunting through client‑side logs.
• Interactive management – the CLI provides intuitive, interactive prompts for adding servers or creating workspaces, while also supporting bulk imports via JSON.
• Scalable architecture – because the gateway simply forwards MCP traffic, adding new servers or expanding a workspace does not require code changes in client applications.

Typical use cases abound. A data‑science team might create a “Data Science” workspace that bundles an MCP server exposing Pandas, NumPy, and SQL connectors, enabling an AI assistant to fetch, transform, and analyze data in one go. A design studio could assemble a “Design” workspace that connects to Figma, Adobe Creative Cloud, and image‑generation services, allowing an assistant to pull assets, suggest layouts, or even generate mockups on demand. For developers working with multiple AI assistants—Cursor, Claude, Windsurf—the ability to switch between dedicated workspaces ensures each tool has access to the precise set of resources it requires, without manual reconfiguration.

In essence, YAMCP turns a fragmented collection of MCP servers into a coherent, developer‑friendly ecosystem. By abstracting server management behind workspaces and a gateway, it frees AI assistants to focus on delivering intelligent responses while developers enjoy streamlined setup, robust monitoring, and effortless scalability.