RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2505.03275v1
• Date: Tue, 06 May 2025 08:05:35 GMT
• Title: RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
• Abstract summary: Large language models (LLMs) struggle to effectively utilize a growing number of external tools, such as those defined by the Model Context Protocol (MCP)citeIntroducingMCP.<n>We introduce RAG-MCP, a Retrieval-Augmented Generation framework that overcomes this challenge by offloading tool discovery.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large language models (LLMs) struggle to effectively utilize a growing number of external tools, such as those defined by the Model Context Protocol (MCP)\cite{IntroducingMCP}, due to prompt bloat and selection complexity. We introduce RAG-MCP, a Retrieval-Augmented Generation framework that overcomes this challenge by offloading tool discovery. RAG-MCP uses semantic retrieval to identify the most relevant MCP(s) for a given query from an external index before engaging the LLM. Only the selected tool descriptions are passed to the model, drastically reducing prompt size and simplifying decision-making. Experiments, including an MCP stress test, demonstrate RAG-MCP significantly cuts prompt tokens (e.g., by over 50%) and more than triples tool selection accuracy (43.13% vs 13.62% baseline) on benchmark tasks. RAG-MCP enables scalable and accurate tool integration for LLMs.

Related papers

• Joint Optimization of Tool Creation and Use for Large Language Model Agents [69.07066297088187]
  SMITH (-grounded Multi-task Iterative Tool Honing) trains tool creation and tool use inside a single policy.<n>A 4B Qwen3 trained with SMITH on 13 procedural reasoning tasks with exact verifiers reaches 79.8 macro-average accuracy on held-out tasks.<n>It also reaches 40.4 on TabMWP-Hard and 42.6 on out-of-domain GQA (+7.6 over the best same-backbone inference-time baseline)
  arXiv  Detail & Related papers  (2026-08-25T13:59:34Z)
• Semantic Tool Discovery for Large Language Models: A Vector-Based Approach to MCP Tool Selection [1.2633421050364024]
  Large Language Models (LLMs) with tool-calling capabilities have demonstrated remarkable potential in executing complex tasks through external tool integration.<n>The Model Context Protocol (MCP) has emerged as a standardized framework for connecting LLMs to diverse toolsets, with individual MCP servers potentially exposing dozens to hundreds of tools.<n>We present a semantic tool discovery architecture that addresses these challenges through vector-based retrieval.
  arXiv  Detail & Related papers  (2026-03-19T18:10:35Z)
• MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains [79.14584837105808]
  We present MC-Search, the first benchmark for agentic MM-RAG with long, step-wise annotated reasoning chains spanning five representative reasoning structures.<n>Beyond answer accuracy, MC-Search introduces new process-level metrics for reasoning quality, stepwise retrieval and planning accuracy.<n>By developing a unified agentic MM-RAG pipeline, we benchmark six leading MLLMs and reveal systematic issues such as over- and under-retrieval and modality-misaligned planning.
  arXiv  Detail & Related papers  (2026-03-01T02:25:57Z)
• Gecko: A Simulation Environment with Stateful Feedback for Refining Agent Tool Calls [56.407063247662336]
  We introduce Gecko, a comprehensive environment that simulates tool responses using a combination of rules and LLMs.<n>GATS consistently improves the tool calling performance of various LLMs including GPT-4o, GPT-5, and Gemini-3.0-pro.
  arXiv  Detail & Related papers  (2026-02-22T15:02:00Z)
• MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers [5.463884405989425]
  We introduce MCP-Atlas, a large-scale benchmark for evaluating tool-use competency.<n>It includes 1,000 tasks designed to assess tool-use competency in realistic, multi-step orchestrate.<n>We score tasks using a claims-based rubric that awards partial credit based on the factual claims satisfied in the model's final answer.
  arXiv  Detail & Related papers  (2026-01-31T23:19:39Z)
• MCPAgentBench: A Real-world Task Benchmark for Evaluating LLM Agent MCP Tool Use [12.220519951554133]
  MCPAgentBench is a benchmark based on real-world MCP definitions to evaluate the tool-use capabilities of agents.<n>The evaluation employs a dynamic sandbox environment that presents agents with candidate tool lists containing distractors.<n>Experiments conducted on various latest mainstream Large Language Models reveal significant performance differences in handling complex, multi-step tool invocations.
  arXiv  Detail & Related papers  (2025-12-31T02:09:48Z)
• ML-Tool-Bench: Tool-Augmented Planning for ML Tasks [23.54937738755734]
  We introduce a benchmark for evaluating tool-augmented machine learning agents.<n>Our benchmark goes beyond traditional tool-use evaluation by incorporating an in-memory named object management.<n>Our approach improves over ReAct by 16.52 percentile positions, taking the median across all Kaggle challenges.
  arXiv  Detail & Related papers  (2025-11-29T23:59:40Z)
• MCPVerse: An Expansive, Real-World Benchmark for Agentic Tool Use [72.53177559476704]
  We introduce MCPVerse, a real-world benchmark for evaluating agentic tool use.<n> MCPVerse integrates more than 550 real-world, executable tools to create an unprecedented action space exceeding 140k tokens.<n>We benchmarked the state-of-the-art LLMs across three modes (Oracle, Standard, and Max-Scale)
  arXiv  Detail & Related papers  (2025-08-22T09:47:53Z)
• HGMF: A Hierarchical Gaussian Mixture Framework for Scalable Tool Invocation within the Model Context Protocol [16.890180802987135]
  Invoking external tools enables Large Language Models (LLMs) to perform complex, real-world tasks.<n>But selecting the correct tool from large, hierarchically-structured libraries is a significant challenge.<n>We propose the Hierarchical Gaussian Mixture Framework (HGMF), a probabilistic pruning method for scalable tool invocation.
  arXiv  Detail & Related papers  (2025-08-11T04:13:06Z)
• Distilling a Small Utility-Based Passage Selector to Enhance Retrieval-Augmented Generation [77.07879255360342]
  Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating retrieved information.<n>In RAG, the emphasis has shifted to utility, which considers the usefulness of passages for generating accurate answers.<n>Our approach focuses on utility-based selection rather than ranking, enabling dynamic passage selection tailored to specific queries without the need for fixed thresholds.<n>Our experiments demonstrate that utility-based selection provides a flexible and cost-effective solution for RAG, significantly reducing computational costs while improving answer quality.
  arXiv  Detail & Related papers  (2025-07-25T09:32:29Z)
• ScaleMCP: Dynamic and Auto-Synchronizing Model Context Protocol Tools for LLM Agents [1.7217813564531652]
  ScaleMCP is a novel tool selection approach that dynamically equips agents with a MCP tool retriever.<n>It gives agents the autonomy to add tools into their memory, as well as an auto-synchronizing tool storage system pipeline.<n> Comprehensive evaluations conducted on a created dataset of 5,000 financial metric MCP servers, demonstrate substantial improvements in tool retrieval and agent invocation performance.
  arXiv  Detail & Related papers  (2025-05-09T20:30:37Z)
• LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs -- No Silver Bullet for LC or RAG Routing [70.35888047551643]
  We present LaRA, a novel benchmark specifically designed to rigorously compare RAG and LC LLMs.<n>LaRA encompasses 2326 test cases across four practical QA task categories and three types of naturally occurring long texts.<n>We find that the optimal choice between RAG and LC depends on a complex interplay of factors, including the model's parameter size, long-text capabilities, context length, task type, and the characteristics of the retrieved chunks.
  arXiv  Detail & Related papers  (2025-02-14T08:04:22Z)
• Efficient Multi-Agent Collaboration with Tool Use for Online Planning in Complex Table Question Answering [16.790216473975146]
  Complex table question answering (TQA) aims to answer questions that require complex reasoning, such as multi-step or multi-category reasoning.<n>Previous approaches demonstrated notable performance by leveraging either closed-source large language models (LLMs) or fine-tuned open-weight LLMs.<n>We propose Multi-Agent Collaboration with Tool use (MACT), a framework that requires neither closed-source models nor fine-tuning.
  arXiv  Detail & Related papers  (2024-12-28T13:13:33Z)
• Data-Efficient Massive Tool Retrieval: A Reinforcement Learning Approach for Query-Tool Alignment with Language Models [28.67532617021655]
  Large language models (LLMs) integrated with external tools and APIs have successfully addressed complex tasks by using in-context learning or fine-tuning. Despite this progress, the vast scale of tool retrieval remains challenging due to stringent input length constraints. We propose a pre-retrieval strategy from an extensive repository, effectively framing the problem as the massive tool retrieval (MTR) task.
  arXiv  Detail & Related papers  (2024-10-04T07:58:05Z)
• SMART-RAG: Selection using Determinantal Matrices for Augmented Retrieval [40.17823569905232]
  Retrieval-Augmented Generation (RAG) has greatly improved large language models (LLMs) by enabling them to generate accurate, contextually grounded responses. RAG approaches, which prioritize top-ranked documents based solely on query-context relevance, often introduce redundancy and conflicting information. We propose Selection using Matrices for Augmented Retrieval (RAG) in question answering tasks, a fully unsupervised and training-free framework designed to optimize context selection in RAG.
  arXiv  Detail & Related papers  (2024-09-21T03:03:09Z)
• Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
  Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models. Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions. We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
  arXiv  Detail & Related papers  (2024-05-25T06:41:23Z)
• Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning [57.523454568002144]
  Large language models (LLMs) have shown capabilities in commonsense reasoning and leveraging external tools. We introduce ToolRec, a framework for LLM-empowered recommendations via tool learning. We formulate the recommendation process as a process aimed at exploring user interests in attribute granularity. We consider two types of attribute-oriented tools: rank tools and retrieval tools.
  arXiv  Detail & Related papers  (2024-05-24T00:06:54Z)
• How to Prune Your Language Model: Recovering Accuracy on the "Sparsity May Cry'' Benchmark [60.72725673114168]
  We revisit the question of accurate BERT-pruning during fine-tuning on downstream datasets. We propose a set of general guidelines for successful pruning, even on the challenging SMC benchmark.
  arXiv  Detail & Related papers  (2023-12-21T03:11:30Z)
• MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use [79.87054552116443]
  Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities.<n>We introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools.<n>We conduct experiments involving eight popular LLMs and find that the majority of them still struggle to effectively select tools.
  arXiv  Detail & Related papers  (2023-10-04T19:39:26Z)
• ReWOO: Decoupling Reasoning from Observations for Efficient Augmented Language Models [32.95155349925248]
  We propose a modular paradigm ReWOO that detaches the reasoning process from external observations, thus significantly reducing token consumption. We show that ReWOO achieves 5x token efficiency and 4% accuracy improvement on HotpotQA, a multi-step reasoning benchmark. Our illustrative work offloads reasoning ability from 175B GPT3.5 into 7B LLaMA, demonstrating the significant potential for truly efficient and scalable ALM systems.
  arXiv  Detail & Related papers  (2023-05-23T00:16:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.