by automation-ai-labs · MCP Server · ★ 603
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MCP Link - Convert Any OpenAPI V3 API to MCP Server 🧩 Architecture 🤔 Why MCP Link? There is a notable gap in the current AI Agent ecosystem: Most MCP Servers are simple wrappers around Web APIs Functionality interfaces may not be complete, depending on developer implementation Manual creation of MCP interfaces is time-consuming and error-prone Lack of standardized conversion processes MCP Link solves these issues through automation and standardization, allowing any API to easily join the AI-driven application ecosystem. 🌟 Key Features Automatic Conversion: Generate complete MCP Servers based on OpenAPI Schema Seamless Integration: Make existing RESTful APIs immediately compatible with AI Agent calling standards Complete Functionality: Ensure all API endpoints and features are correctly mapped Zero Code Modification: Obtain MCP compatibility without modifying the original API implementation Open Standard: Follow the MCP specification to ensure compatibility with various AI Agent frameworks 🌐 Online Version Try our hosted version at mcp-link.vercel.app to quickly convert and test your APIs without installation.
| Stars | 603 |
| Forks | 70 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 74.2808273997385/100 |
| Open Issues | 8 |
| Last Updated | 2025-04-03 |
| Created | 2025-03-29 |
| Platforms | go, mcp |
| Est. Tokens | ~30k |
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mcp-link is Convert Any OpenAPI V3 API to MCP Server. It is categorized as a MCP Server with 603 GitHub stars.
mcp-link is primarily written in Go. It covers topics such as agents, mcp, mcp-server.
You can find installation instructions and usage details in the mcp-link GitHub repository at github.com/automation-ai-labs/mcp-link. The project has 603 stars and 70 forks, indicating an active community.
mcp-link is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-link on Agent Skills Hub include npcpy, apify-mcp-server, awesome-hacking-lists. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.