Model Context Protocol (MCP) has emerged as a practical standard for connecting LLM-based agents with external tools and services through MCP servers. Driven by the open-source community, the MCP ecosystem is rapidly expanding, resulting in a large and growing collection of third-party MCP servers. Accurately selecting MCP servers that satisfy functional requirements from many candidates, therefore, becomes an increasingly important problem. However, MCP server documents are often unstructured and exhibit ambiguous function semantics, making it difficult to align user requirements with server capabilities during retrieval. To address this issue, we propose MCP-Focus, a function-oriented document enhancement framework that produces retrieval-ready MCP server documentation via a multi-stage agentic pipeline for white-box code analysis and document generation. Specifically, MCP-Focus first extracts a comprehensive tool inventory with metadata, then refines tool-level descriptions grounded in each extracted tool's implementation, and finally aggregates the refined tool descriptions into a structured server-level overview as the retrieval document. To better evaluate MCP server retrieval, we construct a benchmark comprising 3k+ open-source MCP servers and human-guided queries that vary in semantic ambiguity, input-output specificity, and the number of involved function points. Experiments across multiple dense retrievers show that fine-tuning with MCP-Focus-enhanced documents consistently improves retrieval effectiveness over baseline document methods on multiple benchmarks. Code and data: https://github.com/JingWC/MCP-Focus.

ABSTRACT Cross‐chain interoperability is a focal point in blockchain research. However, existing efforts predominantly concentrate on functional realization, such as asset transfer and message relaying, while often overlooking the critical dimension of performance. The effective implementation of these functions faces significant performance challenges, including data verification overhead, traceability delays, and inflexible contract execution. Precisely addressing this gap, this paper proposes a performance‐optimized, relay‐based architecture. Three dedicated core modules are introduced to address these performance bottlenecks: A Shared Data Life‐cycle Management Module for efficient data governance, a Real‐time Cross‐chain Traceability Module for low‐latency tracking, and a Dynamic Smart Contract Management Module for agile cross‐chain logic execution. Implemented on BitXHub, our system demonstrates superior performance, successfully processing 937 out of 1000 transactions and achieving a latency of 6.7 ms under 800 concurrent requests. The framework's practical effectiveness is further validated through deployments in a cross‐border seafood supply chain and a multi‐party Deoxyribonucleic Acid (DNA) data sharing network, proving its value as a high‐performance solution for complex real‐world applications.

Traditional search engines aggregate vast amount of data on the Internet to provide keyword search services. However, in some privacy-sensitive fields like healthcare and e-government, the personal data often contains a substantial amount of private or sensitive information across different stakeholders’ data repositories. Due to the presence of private or sensitive information within the raw data or its metadata, as well as the lack of unified local search models, employing the approach with global data aggregation and indexing is not feasible. To solve this problem, we propose MeDiC, a metasearch service for discovering globally distributed confidential data. By distributing search requests through a unified interface, MeDiC offers a metasearch service to users without aggregating distributed confidential data. Experiments demonstrate that the MeDiC exhibits strong extensibility and user friendliness to enable developers to select different models, algorithms and search parameters based on specific search scenarios.

IoT blockchain suffers heavy performance issues because of the massive transactions generated by various IoT nodes. By dividing nodes into different shards, sharding can produce blocks in parallel and hence improve the throughput of the blockchain system. Unlike the traditional blockchain system, IoT blockchain mainly consists of smart devices and the transactions are usually generated from the real world, such as the sensor data, photos taken by cameras, and so on. In IoT blockchain, closer nodes usually share a lower network latency and the transactions they generate are more related. Therefore, location-based sharding is an effective approach to improve the performance of IoT blockchain. Traditionally, IoT nodes are di-vided into different shards based on geographical locations or the connected edge server. However, the key challenge of sharding in IoT blockchain is how to guarantee the equality of shards division as to the unpredictable distribution and the dynamic behavior of IoT nodes. On one hand, shards can not be pre-divided because we can not predict the number or the distribution of the IoT nodes. On the other hand, nodes continuously joining or quitting shards will also break the equality of the shards division. In this paper, we propose Fission, a sharding mechanism designed for IoT blockchain. Fission divide shards based on the Voronoi diagram without any preknowledge about the nodes distribution, and support dynamic, autonomous sharding adjustment based on distributed Delaunay Triangulation. In addition, Fission uses a new diffusion-based consensus algorithm to achieve the linear scalability of throughput. The experimental results show that Fission can construct and adjust shards at a very low cost and can execute in a decentralized manner. The throughput can reach 1900tps in 500 nodes with only 5M bps bandwidth, and can scale linearly as the nodes increase.

Exploring and mining the explosive burst of "big data" has already generated a lot of innovative applications, especially the recent advances of AI applications, and thus produced big values to the human society and civilization. However, due to the centralized patterns of data governance activities, including creation, sharing, exchange, management, analytics, tracing, and accounting, the potential values of big data distributed on the Internet are far away from being adequately explored. The recent announcement of data protection policies/laws such as GDPR makes the problem even more challenging. We are now at a moment of truth where the data governance infrastructure should be reconsidered and redesigned. In this paper, we propose a software-defined infrastructure design in a decentralized fashion: data owners are able to implement and deploy their own rules to the application systems where the data are produced for further governance activities. Such a fashion is quite similar to the popular software-defined networking where users are allowed to deploy rules of switches and customize the use. Our principled infrastructure design can radically reform the current data governance activities into a decentralized topology. On the one hand, data can be separated from the application that generates the data, and data owners can have the full rights to decide where their data should be stored and how the data can be shared. On the other hand, data users can search, discover, integrate, and analyze the data from various data sources according to their application requirements and scenarios. As a result, we argue that our infrastructure can establish a new generation of responsive decentralized data governance that can promote the innovation of linking data to better adapt the open environment and diverse user requirements. With this perspective, we briefly discuss some key insights and enumerate several related new technologies and open challenges.