# MCP Bridge: A Lightweight, LLM-Agnostic RESTful Proxy for Model Context Protocol Servers

**Authors:** Arash Ahmadi, Sarah Sharif, Yaser M. Banad  
**arXiv:** [2504.08999](https://arxiv.org/abs/2504.08999)  
**Date:** 2025-04-11  
**Summary page:** https://www.summarizepaper.com/en/paper/2504.08999v1/

## Key points

- Large Language Models (LLMs) are being enhanced with external tools and data sources to overcome limitations in knowledge and capabilities
- Retrieval-augmented generation (RAG) combines a neural generator with non-parametric memory of retrieved documents to improve performance on knowledge-intensive tasks
- Models can link to live knowledge sources for dynamic context updates, addressing issues like stale knowledge more effectively than static LLMs
- Frameworks like ReAct and Toolformer enable LLMs to make step-by-step decisions about invoking external tools or APIs
- HuggingGPT orchestrates model routing by using a powerful LLM as a controller to direct user requests to specialized AI models
- Integration of tools with LLMs is advanced through frameworks like Visual ChatGPT, GPT-4Tools, Chameleon, and Gorilla for tool augmentation and real-world API invocation
- Standardization and integration of LLMs with external tools are facilitated through interfaces like the Model Context Protocol (MCP)
- MCP Bridge is introduced as a lightweight RESTful proxy that connects multiple MCP servers, providing enhanced security controls and cross-platform compatibility for sophisticated applications powered by Large Language Models

## Summary

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Large Language Models (LLMs) are being enhanced with external tools and data sources to overcome their inherent limitations in knowledge and capabilities. One popular approach is retrieval-augmented generation (RAG), which combines a neural generator with non-parametric memory of retrieved documents, improving performance on knowledge-intensive tasks. By linking to live knowledge sources, these models can update their context dynamically, addressing issues like stale knowledge more effectively than static LLMs. Another research direction focuses on enabling LLMs to invoke external tools or APIs. Frameworks like ReAct and Toolformer allow models to make step-by-step decisions about when to call a tool or continue thinking. Additionally, HuggingGPT demonstrates model orchestration by using a powerful LLM as a controller to route user requests to specialized AI models. Integration of tools with LLMs has been further advanced through frameworks like Visual ChatGPT and GPT-4Tools, which teach models new tool-use skills through self-instruction. Chameleon augments LLMs with modular tools and uses an LLM-based planner for tool composition, while Gorilla fine-tunes LLaMA for real-world API invocation from a catalog of machine learning APIs. In terms of standardization and integration, Large Language Models are increasingly being augmented with external tools through standardized interfaces like the Model Context Protocol (MCP). However, current MCP implementations face limitations such as requiring local process execution through STDIO transports, making them impractical for resource-constrained environments. To address this issue, MCP Bridge is introduced as a lightweight RESTful proxy that connects multiple MCP servers and exposes their capabilities through a unified API. Unlike existing solutions, MCP Bridge is fully agnostic to the backend used by the LLM, supporting any vendor. The system implements a risk-based execution model with three security levels - standard execution, confirmation workflow, and Docker isolation - while maintaining backward compatibility with standard MCP clients. Complementing this server-side infrastructure is the Python-based MCP Gemini Agent that facilitates natural language interaction with MCP tools. The evaluation shows that MCP Bridge successfully overcomes the constraints of direct MCP connections by providing enhanced security controls and cross-platform compatibility. This enables sophisticated applications powered by Large Language Models in previously inaccessible environments such as mobile devices, web browsers, and edge computing platforms.