MCP Local Router

The MCP Local Router is a lightweight aggregation proxy designed to unify the capabilities of multiple MCP servers into a single, easy‑to‑consume interface. By running as an intermediary between downstream AI assistants and upstream MCP services, it eliminates the need for clients to maintain separate connections or manage individual server configurations. This consolidation is especially valuable in environments where an assistant must tap into diverse toolsets—such as file system access, search services, or custom data APIs—without exposing each service’s intricacies to the end user.

At its core, the router accepts a JSON configuration that maps server names to executable commands and environment variables. Each entry launches an independent MCP server process, which the router then monitors for tool surface data and response streams. Once all upstream servers are running, the router exposes a unified set of endpoints: an aggregated SSE stream () that broadcasts the combined tool surface area, and dedicated per‑server streams () that provide tool visibility for individual services. This dual‑layered approach allows developers to balance breadth and granularity—either let an assistant discover all available tools at once or restrict it to a specific domain.

Key capabilities include:

• Multi‑server orchestration: Launch and supervise any number of MCP servers, each with its own command line arguments and environment.
• Transport flexibility: Support for both standard input/output (stdio) and Server‑Sent Events (SSE), giving clients a choice between lightweight IPC or persistent HTTP streams.
• Dynamic endpoint discovery: Each SSE connection begins with an event that tells the client which POST URL to use for message exchange, simplifying integration.
• Environment injection: Pass custom variables into each upstream server, enabling secure token handling or configuration without hard‑coding secrets.

Typical use cases involve building an AI assistant that needs to interact with a filesystem, perform full‑text search across a workspace, or call a proprietary analytics service—all while keeping the assistant’s codebase agnostic to the underlying tool implementations. In a continuous‑integration pipeline, for example, the router could expose a single endpoint that aggregates linting, formatting, and test execution tools, allowing the assistant to orchestrate a complete build workflow with minimal configuration.

Because the router aggregates tool surfaces and normalizes transport, developers can focus on crafting higher‑level conversational logic rather than juggling multiple server connections. Its straightforward JSON configuration and support for common Rust async runtimes make it a drop‑in component in modern AI development stacks, providing a clean, scalable bridge between assistants and the rich ecosystem of MCP services.