MCP Servers for Pyserini and RankLLM: Enabling Agentic Retrieval-Augmented Generation
Abstract
Large language models (LLMs) increasingly rely on retrieval-augmented generation (RAG), where search results are provided as external context to improve grounding and factuality. The Model Context Protocol (MCP) is an emerging industry standard for connecting LLMs to external tools. We present MCP servers for Pyserini and RankLLM, two widely used information retrieval research toolkits, enabling their retrieval and reranking capabilities to be easily accessed by LLM agents. Our system exposes sparse and dense retrieval over prebuilt indexes, multimodal search, LLM-based reranking, and evaluation based on relevance judgments, all as tools callable by LLMs. This integration supports rapid experimentation with agentic RAG workflows and facilitates comparisons across models and clients. We demonstrate end-to-end retrieval, reranking, RAG, and multimodal search using both open-source and proprietary LLMs, bridging IR research infrastructure with LLM tool ecosystems.
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