MCP 服务器

开放的 Model Context Protocol 服务器注册表。按类别、传输方式或使用场景,为 AI 智能体寻找合适的工具、资源和提示词。

服务器

177

工具

471

类别

11

贡献者

155

5 个服务器

排序方式
Dagster MCP ServerDagster

Dagster's MCP integration enables agents to inspect and operate data assets, jobs, schedules, and runs in modern data platforms.

0
Figma MCP ServerFigma

Official Figma MCP server that brings design context directly into AI coding workflows. Provides tools for extracting design information, generating code from Figma selections, taking screenshots, creating and editing Figma files, generating diagrams from Mermaid syntax, searching design systems, managing Code Connect mappings, and uploading assets. Supports both remote (OAuth) and local (desktop app) server modes.

5.2k
Cloudinary MCP ServersCloudinary

Cloudinary's official MCP servers for managing media through conversational AI. Cover the full media workflow: uploading and transforming images and videos, organizing assets with structured metadata, configuring processing pipelines, and running AI-powered content analysis. Available as remote OAuth endpoints or local npx processes across several focused servers (asset management, environment config, structured metadata, and analysis).

0
Contentful MCP ServerContentful

Contentful's official MCP server for the Content Management API. Gives AI agents tools to create, edit, organize, and publish content, manage content models and assets, and work across spaces and environments. Useful for automating editorial workflows and content operations in a Contentful-backed headless CMS.

0
MCP for UnityCoplay

MCP server that bridges AI assistants with the Unity Editor. Gives an LLM tools to create and modify GameObjects, edit scripts, manage assets and scenes, read the console, and run tests, enabling AI-driven game development workflows directly inside Unity. Works with clients such as Claude, Cursor, VS Code, and other MCP-compatible tools.

0

Skills 与 MCP 服务器

有什么区别?

Skills定义“做什么”

Skill 将说明、示例提示词和推荐模型组织在一起,让智能体稳定地完成任务。Skills 增加的是知识,而不是新的连接。

MCP 服务器定义“如何连接”

MCP 服务器通过连接数据库、API 和文件等真实系统,为智能体增加新能力。MCP 提供连接和操作,而不是任务说明。

简单来说:当模型需要把某项任务做好时选择 Skill;当模型需要访问工具或系统时选择 MCP 服务器。两者可以组合使用,Skill 可以依赖 MCP 服务器提供的工具。

开发了 MCP 服务器?

将它提交到由社区维护的开源注册表。

提交到 GitHub