MCProxy

Mcproxy is a lightweight MCP (Model Context Protocol) proxy that bridges the gap between MCP clients and servers, enabling developers to enrich standard AI workflows with custom functionality without modifying existing agents. By acting as both a client and a server, Mcproxy allows any MCP‑enabled assistant to interact with multiple downstream services while preserving the original request–response contract. This duality gives developers a powerful tool for building modular, composable AI pipelines.

The core problem Mcproxy solves is the rigidity of one‑to‑one MCP connections. In typical setups, a client must know every server it will contact, and each server is isolated from others. Mcproxy introduces a middle layer that can route requests to any number of servers, apply cross‑server logic, and even chain multiple proxies together. This flexibility is especially valuable when an AI assistant needs to access diverse data sources—such as a database, a web API, or a custom inference engine—while maintaining a single, coherent interface.

Key features of Mcproxy are:

• Transparent proxying – Clients send requests to Mcproxy as if it were a normal server; Mcproxy forwards them to the appropriate downstream servers and returns responses unchanged.
• Feature injection – Built‑in internal features (e.g., request validation, logging, or rate limiting) can be applied automatically to every message passing through the proxy.
• Plug‑in architecture – External modules can be loaded dynamically to add new capabilities, such as caching, authentication, or custom response transformations.
• Multi‑server orchestration – A single Mcproxy instance can manage dozens of server connections, routing traffic based on request attributes or custom policies.
• Chaining support – Proxies can be linked in series, allowing complex workflows where the output of one proxy becomes the input to another.

Real‑world scenarios that benefit from Mcproxy include:

• Data aggregation – An AI assistant aggregates information from several databases and APIs, with Mcproxy orchestrating the calls and normalizing responses before returning them to the user.
• Hybrid inference – A conversation model delegates specialized reasoning tasks (e.g., math, code generation) to dedicated servers, while Mcproxy maintains a seamless dialogue experience.
• Security and compliance – By inserting authentication or audit logging modules, Mcproxy can enforce enterprise policies without touching the core AI logic.
• Testing and sandboxing – Developers can spin up a Mcproxy instance that routes requests to mock servers, enabling end‑to‑end testing of AI agents in isolation.

Integrating Mcproxy into an existing MCP workflow is straightforward: replace the direct client‑server link with a Mcproxy endpoint, configure the desired downstream servers and modules in its configuration file, and start the proxy. From the client’s perspective nothing changes; from the server side, Mcproxy presents a single, unified interface that can be extended on demand. This design gives developers the agility to evolve AI applications incrementally, adding new services or policies without rewriting agents or compromising stability.