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

TL;DR

This paper introduces a structured recommendation framework and dataset for selecting appropriate MCP servers for LLM-based development tasks, improving tool discovery efficiency.

Contribution

It formulates MCP server recommendation as a retrieval problem, creates the Task2MCP dataset, and develops T2MRec, a model for better MCP server ranking and selection.

Findings

The Task2MCP dataset systematically links tasks with MCP servers.

T2MRec improves MCP server recommendation accuracy.

An interactive agent prototype supports dynamic MCP server selection.

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.