Boilerplate MCP Server
by aashari
This project provides a foundation for developing custom Model Context Protocol (MCP) servers, connecting AI assistants to external data sources or APIs. It offers a complete architecture pattern, a working example tool, and development infrastructure ready for extension.
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What is Boilerplate MCP Server?
This boilerplate implements the MCP specification with a clean, layered architecture that can be extended to build custom MCP servers for any API or data source. MCP (Model Context Protocol) is an open standard that allows AI systems to securely and contextually connect with external tools and data sources.
How to use Boilerplate MCP Server?
To use this boilerplate, clone the repository, install dependencies using npm install, and run the development server with npm run dev:server. You can then test the example IP lookup tool using npm run dev:cli -- get-ip-details. Refer to the 'Building Custom Tools' section for extending the server with your own tools.
Key features of Boilerplate MCP Server
Production-Ready Architecture
Type Safety (TypeScript)
Working Example (IP lookup tool)
Testing Framework
Development Tooling (ESLint, Prettier, TypeScript)
Use cases of Boilerplate MCP Server
Connecting AI assistants to external data sources
Integrating AI with custom APIs
Building custom tools for AI systems
Creating MCP-compliant servers
Developing secure and contextual AI integrations
FAQ from Boilerplate MCP Server
What is MCP?
What is MCP?
Model Context Protocol is an open standard for AI systems to securely connect with external tools and data sources.
What are the prerequisites for using this boilerplate?
What are the prerequisites for using this boilerplate?
Node.js (>=18.x) and Git are required.
How do I run the development server?
How do I run the development server?
Use the command npm run dev:server.
How do I test the example tool?
How do I test the example tool?
Use the command npm run dev:cli -- get-ip-details.
How do I build custom tools?
How do I build custom tools?
Follow the steps outlined in the 'Building Custom Tools' section of the README, which includes defining a service layer, creating a controller, implementing an MCP tool, adding CLI support, and registering components.