Every LLM tool call is structurally an RPC -- a function name, JSON arguments, and a serialized result -- yet each protocol (native Python, MCP, OpenAPI, LangChain) is integrated from scratch. We present ToolRegistry, a system that makes this RPC nature explicit: a single Tool object acts as a universal stub regardless of transport, while the registry serves as the RPC client runtime for dispatch, schema generation, and execution. The system ships as three packages -- a core registry, a server exposing tools over MCP and OpenAPI, and a hub of production-ready implementations -- and invokes tools through pluggable thread or process backends. The system now also provides tag-based permission policies, BM25F-powered progressive tool disclosure for large registries, think-augmented function calling, multi-provider schema support (OpenAI, Anthropic, Gemini), declarative JSONC/YAML configuration, and a near-zero-dependency core built on stdlib-only vendored modules. In our benchmarks the library cuts integration code by 60-80%, and choosing the right concurrency mode (thread vs. process) yields up to 3.1x throughput over the alternative for a given workload. ToolRegistry is open-source at https://github.com/Oaklight/ToolRegistry; documentation lives at https://toolregistry.readthedocs.io/.

翻译：[translated abstract in Chinese] 每个大语言模型工具调用在结构上都是一个RPC——包含函数名、JSON参数与序列化结果——然而每种协议（原生Python、MCP、OpenAPI、LangChain）都需要从头进行集成。我们提出ToolRegistry系统，该框架将这种RPC本质显式化：单个Tool对象作为独立于传输层的通用存根，而注册表则充当RPC客户端运行时，负责调度、模式生成与执行。系统以三个软件包形式发布——核心注册表、通过MCP和OpenAPI暴露工具的服务器、以及生产级实现的中心枢纽——并通过可插拔的线程或进程后端调用工具。该系统现还提供基于标签的权限策略、面向大型注册表的BM25F渐进式工具披露、增强思考的函数调用、多供应商模式支持（OpenAI、Anthropic、Gemini）、声明式JSONC/YAML配置，以及基于仅标准库封装模块的近零依赖核心。在基准测试中，该库减少了60-80%的集成代码，且针对特定工作负载选用正确的并发模式（线程vs.进程）可带来最高达3.1倍的吞吐量提升。ToolRegistry以开源形式发布在https://github.com/Oaklight/ToolRegistry，文档位于https://toolregistry.readthedocs.io/。