Model Context Protocol TypeScript SDK: Build MCP Servers and Clients
This repository profile is provided by osrepos.com, an open source repository discovery platform.

Summary
The `modelcontextprotocol/typescript-sdk` is the official TypeScript SDK for interacting with Model Context Protocol (MCP) servers and clients. It provides a standardized way for applications to offer context to Large Language Models (LLMs), separating context provision from LLM interaction. Developers can use it to easily create MCP servers that expose resources, prompts, and tools, as well as build MCP clients to connect to any MCP server.
Repository Information
Topics
Click on any tag to explore related repositories
Use at your own risk
OSRepos shares public repositories for knowledge and discovery only. Any installation, execution, configuration, or use of code from these repositories is the user's own responsibility. Always review the repository, source code, dependencies, licenses, and security implications before running or installing anything. OSRepos is not responsible for issues, damages, or losses resulting from third-party repositories.
Introduction
The modelcontextprotocol/typescript-sdk is the official TypeScript SDK for the Model Context Protocol (MCP). This protocol enables applications to provide context for Large Language Models (LLMs) in a standardized manner, decoupling context provision from LLM interaction. The SDK implements the full MCP specification, simplifying the creation of MCP servers that expose resources, prompts, and tools, as well as the development of MCP clients that can connect to any MCP server using standard transports like stdio and Streamable HTTP.
Installation
To get started with the Model Context Protocol TypeScript SDK, install it via npm:
npm install @modelcontextprotocol/sdk zod
Note that zod is a required peer dependency for schema validation, ensuring robust data handling within your applications. The SDK is compatible with Zod v3.25 or later.
Examples
The SDK ships with a comprehensive set of runnable examples under the src/examples directory, designed to help you quickly understand and implement its capabilities. A quick start guide demonstrates an end-to-end interaction, featuring an example Streamable HTTP server and an interactive client.
You can explore various scenarios, including different server types (stateful, stateless, JSON response mode), client interactions (interactive, parallel tool calls, OAuth), and advanced capabilities like form and URL elicitation, sampling, and tasks. These examples provide practical insights into building sophisticated LLM-powered applications.
Why Use It
The Model Context Protocol TypeScript SDK offers a robust solution for integrating LLMs into applications by standardizing context provision. It simplifies the development process with clear APIs for creating servers and clients, supporting various transports. Its rich feature set, including tools for LLM actions, resources for data exposure, reusable prompts, and advanced capabilities like sampling and elicitation, empowers developers to build sophisticated and interactive LLM-powered applications efficiently. This standardization ensures better interoperability and maintainability for your LLM integrations.
Links
For more in-depth information and to explore the Model Context Protocol further, refer to the official documentation:
Related repositories
Similar repositories that may be relevant next.

lat.md: A Knowledge Graph for Your Codebase, Written in Markdown
September 26, 2026
lat.md is an innovative tool that transforms your codebase knowledge into an interconnected graph of markdown files. It helps both AI agents and human developers quickly understand project architecture, business logic, and design decisions. By integrating directly into your project, lat.md ensures documentation remains consistent and up-to-date.

TeamAI-CLI: Empowering Teams to Become AI Native with a Git-Based Foundation
September 24, 2026
TeamAI-CLI, developed by Tencent, is a powerful command-line interface aimed at making every team AI native. It offers a unified, Git-based platform for teams to collaborate, learn, and continuously improve with AI. This tool transforms individual AI capabilities into shared team assets, integrating AI agents, machines, and team members for enhanced efficiency.

LLM Wiki: Build a Self-Maintaining, Interlinked Knowledge Base with AI
September 23, 2026
LLM Wiki is a powerful cross-platform desktop application designed to transform your documents into an organized, interlinked knowledge base automatically. Unlike traditional RAG systems, it incrementally builds and maintains a persistent wiki from your sources, ensuring knowledge is compiled once and kept current. This innovative approach offers a dynamic and evolving personal knowledge management solution.

Maka: A High-Performance Agent Workspace for AI Tasks
September 21, 2026
Apache Maka (Incubating) is a high-performance agent workspace designed to maintain a complete, append-only record of all agent actions. It focuses on measurable performance, local-first operation, and robust recovery mechanisms. This project provides a unified execution authority for desktop, TUI, and CLI clients, ensuring consistent agent behavior across platforms.
Source repository
Open the original repository on GitHub.
14 counted GitHub visits