mcp-proxy

Overview

The MCP Proxy Monorepo delivers a lightweight, extensible Model Control Protocol (MCP) server that bridges AI assistants with external services. By exposing a single, well‑defined endpoint (), the server lets clients like Claude or other AI agents discover, invoke, and stream data from integrated back‑ends without custom adapters. This solves a common pain point in AI tooling: the need for each assistant to implement its own connector logic for every third‑party service. With MCP, developers can focus on building business logic while the server handles protocol compliance and connection management.

At its core, the repository ships a dedicated package that implements the MCP specification for Bitte AI integrations. The server supports all standard MCP capabilities—resource discovery, tool invocation, prompt management, and streaming sampling—through a single Server‑Sent Events (SSE) endpoint. The design emphasizes simplicity: a minimal configuration block in the cursor settings points the AI client to the MCP URI, after which the assistant can request resources or execute tools just by referencing their names. This pattern eliminates hard‑coded URLs and enables dynamic discovery of new services as they are added to the monorepo.

Key features include:

• Unified SSE endpoint that delivers real‑time responses and streaming data.
• Modular package structure (via Turborepo) that allows adding new MCP servers without touching existing code.
• Built‑in tooling for linting, formatting, and type checking (Biome) to keep the codebase healthy.
• Fast development workflow with hot‑reload and isolated service execution ().

Developers can integrate this server into their AI pipelines by simply updating the cursor configuration. Once connected, an assistant can call any of the exposed resources—such as fetching user data, executing domain‑specific tools, or retrieving curated prompts—without needing to understand the underlying implementation. This is particularly valuable for scenarios that require secure, authenticated access to proprietary APIs or real‑time data streams.

Real‑world use cases span from customer support bots that need to query a ticketing system, to data‑analysis assistants that pull metrics from monitoring services, to creative tools that fetch external knowledge bases. By centralizing MCP logic in a single, versioned repository, teams can iterate quickly on new integrations while maintaining consistent behavior across all AI agents. The monorepo’s structure also encourages community contributions: adding a new service involves creating a package and updating the root configuration, making it straightforward to expand the ecosystem.

In summary, the MCP Proxy Monorepo offers a clean, protocol‑centric gateway that simplifies AI assistant development, promotes reuse of integration logic, and scales effortlessly as new services are introduced.