From Language to Action: Enhancing LLM Task Efficiency with Task-Aware MCP Server Recommendation

2026-04-18

AI Summary

This work addresses the challenge developers face in efficiently matching tasks to appropriate Model Context Protocol (MCP) servers amid the rapid expansion of the MCP ecosystem. We formulate MCP server recommendation as a retrieval–ranking problem that integrates semantic relevance with engineering constraints. To this end, we introduce Task2MCP, the first dataset tailored to developer tasks, and propose T2MRec, a novel recommendation model that first filters candidates based on semantic and structural compatibility, then refines rankings via centroid-guided expansion and constraint-aware large language model reranking. Furthermore, we develop an interactive recommendation agent capable of dynamic tool selection within conversational environments. Experimental results demonstrate that our approach significantly improves both recommendation coverage and ranking quality, establishing the first systematic framework and reproducible benchmark for this emerging research area.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Learning Preferences or Rankings
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web search models and ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

The rapid expansion of the model context protocol (MCP) ecosystem enables large language model (LLM)-based agents to access a wide range of external tools via a standardized interface. However, identifying appropriate MCP servers for a specific development task remains challenging. Existing studies primarily focus on measuring the MCP ecosystem or optimizing tool invocation mechanisms, while systematic recommendation frameworks and reproducible benchmarks for real-world development tasks remain largely unexplored. To address this limitation, we formulate task-oriented MCP server recommendation as a structured retrieval-and-ranking problem that jointly considers semantic relevance and engineering constraints. We first construct Task2MCP, a task-centered dataset that systematically associates taxonomy-grounded development tasks with curated MCP servers. This dataset provides structured supervision and a reproducible evaluation environment for research on MCP tool recommendations. Building on this dataset, we propose T2MRec, a task-to-MCP server recommendation model. It models semantic relevance and structural compatibility to construct an initial candidate set. Then it improves coverage and ranking quality through centroid-based candidate expansion and constrained LLM-based re-ranking. In addition, we design and implement an interactive MCP server recommendation agent prototype that operates in conversational environments to support dynamic decision-making. The agent assists developers in efficiently evaluating and integrating tools by providing recommended MCP servers together with usage guidelines.

Problem

• MCP server recommendation
• large language models
• tool recommendation
• development tasks
• retrieval and ranking

Innovation

• MCP server recommendation
• task-oriented retrieval
• structured ranking
• LLM-based re-ranking
• interactive agent