MCP Tools

The MCP Tools library is a high‑level abstraction that simplifies the integration of the Model Context Protocol (MCP) into both MCP clients and servers. By wrapping the underlying TypeScript SDK, it handles the intricacies of authentication flows, metadata generation, and resource exposure so developers can focus on delivering richer AI experiences. The core problem it solves is the friction that exists when an AI assistant—such as Claude, ChatGPT, or Cursor—needs to securely access private data or perform actions on behalf of a user. MCP provides a standardized, permission‑based channel for such interactions, and this library removes the boilerplate required to implement that channel.

At its heart, MCP Tools offers two complementary sets of utilities: one for clients that request access to external services, and another for servers that expose protected resources. For servers, the library automatically generates the RFC 9728‑compliant protected resource metadata file and serves it at the well‑known endpoint. This metadata describes what resources are available, how they can be authenticated, and which scopes the client may request. For clients, MCP Tools streamlines the OAuth‑style authorization flow, handling token exchange, scope negotiation, and secure storage of access tokens. By encapsulating these details, the library ensures that developers can deploy MCP‑enabled services without deep knowledge of the protocol’s low‑level mechanics.

Key capabilities include:

• Automatic metadata generation for resource servers, guaranteeing compliance with the latest MCP spec.
• Framework‑specific integration guides for Express.js and Next.js, enabling rapid setup in common web stacks.
• Unified authentication handling that supports both client‑side and server‑side flows, making it straightforward to build end‑to‑end MCP solutions.
• Scalable permission management that lets users grant fine‑grained access to their private data, such as specific GitHub repositories or email folders.

Real‑world use cases abound. A developer could build an AI assistant that reads a user’s private codebase to suggest refactorings, or a customer‑support bot that drafts and sends emails after user approval. In data‑science pipelines, MCP can expose secure datasets to AI models for analysis without exposing credentials. For SaaS products, adding MCP support turns a regular API into a first‑class AI‑friendly interface, allowing third‑party assistants to automate workflows on behalf of users.

Integrating MCP Tools into an AI workflow is as simple as adding the appropriate package and following the framework guide. Once set up, the AI client can request a token, the server validates and issues it, and the assistant can then safely interact with protected resources. This tight coupling between permissioned access and AI capabilities unlocks a new class of applications where privacy, security, and automation coexist seamlessly.