[论文解读] Semantic Tool Discovery for Large Language Models: A Vector-Based Approach to MCP Tool Selection

Sarat Mudunuri, Jian Wan+2 more|arXiv (Cornell University)|Mar 19, 2026

Topic Modeling被引用 0

一句话总结

论文提出一个语义、向量化的 MCP 工具发现层，按查询动态选取三个顶级工具，以在最小化 token 使用的同时保持高工具调用准确性。

ABSTRACT

Large Language Models (LLMs) with tool-calling capabilities have demonstrated remarkable potential in executing complex tasks through external tool integration. 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. However, current implementations face a critical scalability challenge: providing all available tools to the LLM context results in substantial token overhead, increased costs, reduced accuracy, and context window constraints. We present a semantic tool discovery architecture that addresses these challenges through vector-based retrieval. Our approach indexes MCP tools using dense embeddings that capture semantic relationships between tool capabilities and user intent, dynamically selecting only the most relevant tools (typically 3-5) rather than exposing the entire tool catalog (50-100+). Experimental results demonstrate a 99.6% reduction in tool-related token consumption with a hit rate of 97.1% at K=3 and an MRR of 0.91 on a benchmark of 140 queries across 121 tools from 5 MCP servers, with sub-100ms retrieval latency. Contributions include: (1) a semantic indexing framework for MCP tools, (2) a dynamic tool selection algorithm based on query-tool similarity, (3) comprehensive evaluation demonstrating significant efficiency and accuracy improvements, and (4) extensibility to multi-agent and cross-organizational tool discovery.

研究动机与目标

• Motivate the scalability challenge of MCP tool provisioning in LLMs due to token overhead and context-window limits.
• Propose a semantic indexing architecture that uses dense embeddings to map tool capabilities to user intent.
• Evaluate token efficiency, tool-selection accuracy, latency, and cost across multiple MCP servers.
• Demonstrate open-source implementation and discuss extensions to multi-agent systems and cross-organizational tool discovery.

提出的方法

• Index MCP tools by extracting schemas and constructing semantic tool documents.
• Generate query and tool embeddings with text-embedding-ada-002 and store in Milvus vector store.
• Retrieve top-K tools by cosine/dot-product similarity and optionally apply thresholding and re-ranking.
• Inject selected tools into the LLM context for tool calls and aggregate results for response generation.
• Provide a feedback loop for refining embeddings and retrieval parameters.

实验结果

研究问题

• RQ1 Can semantic similarity between user queries and tool descriptions enable effective dynamic tool selection in MCP systems?
• RQ2 What is the quantitative impact of semantic tool filtering on token efficiency, cost, and system performance?
• RQ3 How does semantic tool selection affect LLM accuracy in tool calling compared to providing all available tools?
• RQ4 What are the optimal parameters (number of tools retrieved, similarity threshold, embedding model) for balancing recall and precision?

主要发现

K	Precision@K	Recall@K	F1@K	Hit Rate@K	MRR	Token Reduction	Latency (ms)
1	92.1%	31.5%	46.9%	85.0%	0.8500	99.6%	87.1
2	70.0%	48.3%	57.0%	95.7%	0.9036	99.6%	90.2
3	57.6%	59.6%	58.4%	97.1%	0.9083	99.6%	87.8
5	42.1%	72.5%	53.2%	97.1%	0.9083	99.6%	87.0
10	26.5%	90.6%	40.9%	98.6%	0.9107	99.6%	88.1

• Semantic similarity enables effective dynamic tool selection with a hit rate of 97.1% at K=3.
• Token reduction is 99.6% across all K values and servers.
• MRR remains around 0.91 for K≥3, indicating early correct tool surface within top results.
• Optimal operating point is K=3, balancing precision and recall (F1=58.4%).
• Retrieval latency stays below 91 ms across configurations.
• Per-server performance varies with catalog distinctiveness, e.g., MySQL and GitHub showing higher precision at low K.