MCP Wrapper

Overview

The MCP Wrapper is a lightweight, transparent proxy that sits between an AI assistant (such as Claude) and the underlying Model Context Protocol server. By intercepting every request that an MCP client sends, it forwards those calls to the real MCP server and relays the responses back. This indirection allows developers to inject custom behavior—logging, authentication, rate limiting, or request/response transformations—without touching the core MCP implementation. The wrapper is designed to be launched automatically by tools like Claude or Cursor, making it a drop‑in component in existing AI workflows.

At its core, the wrapper performs three essential functions: (1) it starts a child process that runs the real MCP server; (2) it listens for incoming connections from MCP clients and forwards all protocol operations—including tool listings, resource queries, prompt handling, and sampling—to the child process; (3) it captures every operation, writing detailed logs to a configurable file. The environment variables for the child process are inherited from the wrapper, ensuring that any configuration required by the real server (API keys, database URLs, etc.) is preserved automatically.

Key capabilities of the MCP Wrapper include:

• Transparent forwarding: All standard MCP operations pass through unchanged, so existing clients and servers require no modification.
• Custom request handling: Developers can hook into the proxy to modify requests or responses, enabling features such as content filtering or dynamic tool injection.
• Robust logging: Every operation is timestamped and recorded in a log file, providing audit trails for debugging or compliance.
• Error resilience: The wrapper catches and reports errors from the underlying server, preventing a single failure from cascading to the client.
• Environment management: By passing environment variables directly to the child process, it eliminates configuration drift between the wrapper and the real MCP server.

Typical use cases for this proxy layer include:

• Security hardening: Adding authentication or token validation before requests reach the MCP server.
• Rate limiting and caching: Throttling tool calls or caching expensive operations to improve throughput.
• A/B testing: Routing a subset of requests to an experimental MCP server while keeping the rest on the production instance.
• Compliance monitoring: Capturing all tool usage logs for audit purposes without modifying the server codebase.

Integration into AI workflows is straightforward: an MCP client (e.g., a Claude agent) connects to the wrapper’s listening port, which then spawns and manages the real MCP server behind the scenes. The wrapper’s design ensures zero downtime during upgrades, as the underlying server can be replaced or restarted independently while the client continues to communicate through the same proxy endpoint. This makes it an ideal component for production deployments where reliability, observability, and extensibility are paramount.