本文围绕大语言模型（LLMs）智能应用中的工具与数据接入问题，系统介绍了两种主流方案：基于 Agent + Function Call 的动态调度机制与基于 MCP（Model Context Protocol）的标准化接入框架。通过梳理各自的工作原理、应用流程及典型实践，分析了不同场景下的适用性选择。

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MCP（模型上下文协议）、Function Calling 和 AI Agents 是三种重要的技术手段，它们在实现 AI 模型与外部系统交互方面各有特点。本文将详细对比这三种技术，并深入探讨 MCP 的多项显著优势。 MCP、Function Calling 和 AI Agents 的区别 Function Calling:平台依赖的函数调用机制 ...

内容丨特工少女 编辑丨特工小鹏 特工十五 众所周知，目前 DeepSeek R1 有一个很大的痛点是不支持 Function Call 的。GitHub 上有 ...

Berkeley Function-Calling Leaderboard（BFCL）是由加州大学伯克利分校开发的专门评估大语言模型（LLM）工具调用能力的基准测试平台。该平台通过2000对问答对，重点考察模型在API调用和实用工具使用方面的表现，是衡量LLM从理论到实际应用能力的重要指标。近期榜单 ...

The introduction of Google’s Gemini API marks a significant step forward for those who develop software and create digital content. The API allows you to harness the power of Google’s latest ...

Function calling is a feature that allows you to describe specific functions to ChatGPT models within an API call. The model, in turn, intelligently decides whether to generate a JSON object, ...

As OpenAI has gained more popularity than ever thanks to the launch of ChatGPT last fall, its application programming interface (API) has also become a sought-after tool for developers. Different ...