Understanding the Hierarchical Structure and Functional Landscape of the Model Context Protocol Ecosystem

Tingxuan Tang; Zilong Chen; Yue; Xiao

Home/Archive/arXiv

arXiv · 2610.05319

Understanding the Hierarchical Structure and Functional Landscape of the Model Context Protocol Ecosystem

Tingxuan Tang·Zilong Chen·Yue·Xiao

View original source ↗Read source document ↗Export metadata ↓

Abstract

AI agents increasingly rely on tools exposed through the Model Context Protocol (MCP) to complete user tasks. Hundreds of thousands of MCP servers are listed across marketplaces, yet they are organized only by coarse, marketplace-specific server categories. This makes it difficult for agents and users to identify tools for a given operation, find functional alternatives, and assess how those alternatives differ. We present MCPacific, the largest tool-level, cross-marketplace map of the MCP ecosystem. MCPacific collects 368,754 MCP server listings corresponding to 124,267 unique servers across 17 marketplaces, statically extracts 1,328,233 tool specifications from these servers in seven languages, and organizes them into a hierarchical functional taxonomy of 58,915 capabilities. We construct the taxonomy through an iterative LLM-driven design-test-refine process and map the full corpus to it using calibrated embedding routing. Our study reveals that MCP extends well beyond developer tooling, with 85% of tools serving other domains. Functional alternatives are widespread but unevenly distributed: 98.5% of tools have at least one alternative, yet nearly a quarter of capabilities are supported by only one tool. Functionally comparable tools also differ in security alerts, code complexity, and project maintenance, with complexity differing by more than 2.5x in 41% of comparable tool pairs. Finally, presenting candidate tools through the taxonomy rather than a flat list improves task completion rate across all four evaluated models, with gains of up to 12 percentage points in Pass@0.75 for crowded candidate sets.

Explore related subjects

cs.SE

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Tingxuan Tang, Zilong Chen, Yue, Xiao. 2026-10-04. Understanding the Hierarchical Structure and Functional Landscape of the Model Context Protocol Ecosystem. https://arxiv.org/abs/2610.05319

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Can we find bugs using LLM-generated oracles?

Unit testing is vital in software development. Typically, a unit test consists of a test prefix and a test oracle which captures the developer's intended behaviour. Traditional test generation tools (e.g. Randoop and Evosuite) often produce oracles that mirror the program's actual behavior rather than the expected one, limiting their ability to automatically detect bugs as users must manually verify if the generated assertions are correct. Recent approaches leverage Large Language Models (LLMs), trained on vast datasets, to generate developer-like code and test cases. Although successful in generating tests, the question of whether such LLM-generated oracles can automatically find bugs, i.e., expected software behavior, remains unanswered. We conduct a controlled experiment to answer this question, by studying LLMs on two tasks, namely, test oracle classification and generation, and assessing whether LLM oracles capture the actual or the expected behavior. The study includes test cases and oracles written by developers and automatically generated for 24 Java repositories. Our findings show that LLM-based test generation approaches mainly capture the actual program behavior making bug detection difficult. We also find that LLMs are better at generating oracles than classifying them. Notably, LLM-generated oracles have a higher fault detection potential than the Evosuite ones.

cs.SE↗

arXiv2026

SELU: A Software Engineering Language Understanding Benchmark

Large Language Models (LLMs) have demonstrated remarkable capabilities in code understanding and generation. However, their effectiveness on non-code Software Engineering (SE) tasks remains underexplored. We present 'Software Engineering Language Understanding' (SELU), the first comprehensive benchmark for evaluating LLMs on 22 SE textual artifacts NLU tasks, spanning from identifying whether a requirement is functional or non-functional to estimating the effort required to implement a development task. SELU covers classification, regression, Named Entity Recognition (NER), and Masked Language Modeling (MLM) tasks, with data drawn from diverse sources such as issue tracking systems and developer forums. We fine-tune 22 open-source LLMs, both generalist and domain-adapted; and prompt two proprietary alternatives using zero-shot a 3-shot prompting strategies. Performance is measured using metrics such as F1-macro, SMAPE, F1-micro, and accuracy, and compared via the Bayesian signed-rank test. Our results show that fine-tuned models across various sizes and architectures perform best, exhibiting high mean performance and low across-task variance. Furthermore, domain adaptation via code-focused pre-training does not yield significant improvements and might even be counterproductive for developer communication tasks.

cs.SE↗

arXiv2026

Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports

Mutation-based fuzzing is effective for uncovering compiler bugs, but designing high-quality mutators for modern languages with complex constructs (e.g., templates, macros) remains challenging. Existing methods rely heavily on manual design or human-in-the-loop correction, limiting scalability and cross-language generalizability. We present Mut4All, a fully automated, language-agnostic framework that synthesizes mutators using Large Language Models (LLMs) and compiler-specific knowledge from bug reports. It consists of three agents: (1) a mutator invention agent that identifies mutation targets and generates mutator metadata using compiler-related insights; (2) a mutator implementation synthesis agent, fine-tuned to produce initial implementations; and (3) a mutator refinement agent that verifies and corrects the mutators via unit-test feedback. Mut4All processes 1400 bug reports (700 Rust, 700 C++), yielding 444 Rust and 561 C++ mutators at ~$0.08 each via GPT-4o. Our customized fuzzer, using these mutators, finds 62 bugs in Rust compilers (44 new, 32 fixed) and 38 bugs in C++ compilers (17 new, 3 fixed). Mut4All outperforms existing methods in both unique crash detection and coverage, ranking first on Rust and second on C++.

cs.SE↗