RAG Quality Guard

Add an offline quality check to RAG/Agent queries to ensure complete and accurate retrieval

Problem

The developer of Limma notes that many companies deploying RAG and agents face unreliable retrieval, with key information often skipped, leading to wrong answers. Existing tools focus on retrieval speed, not quality.

Solution

An MCP server that automatically evaluates whether retrieval results cover necessary information after each query and suggests missing pieces. Runs offline without extra API calls, returns structured scores for easy integration into existing agent workflows.

Users

Small teams and indie developers using RAG/agents, AI app developers

MVP

Build a simple MCP server that takes query and retrieved results, evaluates completeness using rules + lightweight model; outputs score and missing content suggestions; provides local CLI test tool; supports integration with common agent frameworks

Revenue

Open-source core, paid hosted version with advanced evaluation models, monthly subscription

Why now

RAG and agent applications are exploding, but quality tools are scarce; MCP protocol is standardizing for easy integration; indie developers can build and validate quickly