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.

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.