MCP Bridge API

Overview

The MCP Bridge API is a lightweight, LLM‑agnostic RESTful proxy that unifies access to Model Context Protocol (MCP) servers. By exposing MCP capabilities—such as resources, tools, prompts, and sampling—through a simple HTTP interface, it removes the need for clients to run native MCP SDKs or manage complex STDIO transports. This design enables any platform—desktop, mobile, web, or edge device—to interact with MCP servers without the overhead of local process execution, making advanced LLM tooling accessible in environments where traditional server runtimes are impractical.

Developers benefit from a single, consistent API surface that abstracts away the underlying LLM backend. Whether the MCP server is powered by Gemini, GPT‑4, or a future model, the bridge presents the same endpoint structure and security controls. The server also offers optional risk‑based execution levels: from standard tool invocation to confirmation workflows and Docker isolation, allowing teams to enforce granular security policies without modifying client logic. This flexibility is especially valuable in regulated industries or multi‑tenant deployments where tool usage must be audited and constrained.

Key capabilities include:

• Unified tool execution – Clients send a JSON payload describing the desired tool, and the bridge forwards it to any connected MCP server, returning results in a standardized format.
• Risk management – Built‑in execution tiers let operators toggle between automatic, confirmation, and sandboxed runs, ensuring that sensitive operations are only performed when explicitly authorized.
• Multi‑client support – The bridge can serve dozens of concurrent clients, eliminating redundant server instances and reducing resource consumption.
• Extensibility – New MCP servers can be added behind the bridge with minimal configuration, allowing a single gateway to orchestrate diverse LLM backends.

Real‑world use cases span from mobile AI assistants that need to invoke external tools (e.g., calendar booking, data lookup) to web applications embedding LLM‑powered workflows without exposing the underlying server architecture. In research settings, the bridge facilitates rapid prototyping by allowing developers to swap LLM backends on the fly while keeping client code unchanged. For enterprise deployments, the risk tiers and Docker isolation provide a safety net that aligns with compliance requirements.

Integrating MCP Bridge into existing AI pipelines is straightforward: clients issue HTTP requests to the bridge’s endpoints, receive JSON responses, and can embed the results into conversational flows or downstream services. The accompanying Python MCP‑Gemini Agent and React Native MCP Agent demonstrate how natural language prompts can be translated into tool calls, leveraging the bridge’s unified interface. This combination of server‑side abstraction and intelligent client tooling creates a robust ecosystem for building sophisticated, secure, and platform‑agnostic LLM applications.