mcp-rlm-proxy

# MCP-RLM-Proxy: Intelligent Middleware for MCP Servers > **Production-ready middleware** implementing Recursive Language Model principles ([arXiv:2512.24601](https://arxiv.org/abs/2512.24601)) for efficient multi-server management, automatic large-response handling, and first-class proxy tools for recursive data exploration. **100% compatible with the MCP specification** - works with any existing MCP server without modification. ## Quick Start for Current MCP Users **Already using MCP servers?** Add this as middleware in 5 minutes: ```bash # 1. Install pip install mcp-rlm-proxy # 2. Create a config in your working directory (mcp.json) mcp-rlm-proxy --init-config ./mcp.json # 3. Edit mcp.json to add your servers $EDITOR ./mcp.json # 2. Configure your existing servers cat > mcp.json << EOF { "mcpServers": { "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/your/path"] } } } EOF # 3. Run the proxy mcp-rlm-proxy --config ./mcp.json ``` **That's it!** Your servers now have automatic large-response handling and three powerful proxy tools for recursive exploration. --- ## Why Use This as Middleware? ### The Problem with Direct MCP Connections When AI agents connect directly to MCP servers: - **Token waste**: 85-95% of returned data is often unnecessary - **Context pollution**: Irrelevant data dilutes important information - **No multi-server aggregation**: Must connect to each server separately - **Performance degradation**: Large responses slow everything down - **Cost explosion**: Every unnecessary token costs money ### The Solution: Intelligent Middleware ``` +---------------+ | MCP Client | (Claude Desktop, Cursor, Custom Client) +-------+-------+ | ONE connection v +---------------+ | MCP-RLM | <-- THIS MIDDLEWARE | Proxy | - Connects to N servers | | - Auto-truncates large responses | | - Caches + provides proxy_filter…