modelcontextprotocol/java-sdk

Features

• Model Context Protocol Integrations - Implements the Model Context Protocol to expose system data and functions to AI models in Java applications.
• MCP Client Implementations - Provides transport-agnostic client implementations to connect to MCP servers and consume AI tools.
• AI Tooling Protocols - Implements a standardized protocol for exchanging prompt templates, resources, and tool definitions between AI clients and servers.
• Tool Discovery and Invocation - Enables the discovery of available tools and their execution using structured output and schema validation.
• Resource Exposure - Exposes read-only data such as file contents and database records as context for AI models via URIs.
• Model Context Protocol Servers - Provides server-side implementations for exposing tools and context to AI models via the Model Context Protocol.
• Prompt Templates - Allows the creation of structured prompt templates with parameter substitution and context injection.
• AI Context Resource Management - Provides mechanisms to manage data resources accessed via unique URIs to serve as context for AI models.
• Tool Definition Frameworks - Provides a framework for creating executable functions and schemas that allow AI models to perform tasks.
• Dynamic Resource Templates - Maps URI templates to internal data handlers for providing dynamic context to AI models.
• AI Client-Server Communication Protocols - Establishes bidirectional data streams between AI clients and servers using standard protocol interfaces.
• Model Context Protocols - Standardizes communication and version negotiation between AI models and tool-providing servers.
• AI Tool Servers - Provides a framework for hosting servers that expose custom tools and resources to AI models via a standardized protocol.
• Context Data Resources - Retrieves structured, URI-addressable data from server-side sources to provide context for AI interactions.
• AI Prompt Engineering Templates - Facilitates developing structured and parameterized prompt templates for consistent AI model interactions.
• Content Generation Requests - Provides capabilities to send sampling requests to clients for generating text or images.
• Transport Layer Connectivity - Implements communication channels including standard I/O, HTTP, and SSE for protocol server connectivity.
• Sampling Delegation Clients - Implements the sampling delegation mechanism allowing servers to request AI completions from connected clients.
• Asynchronous Protocol Streams - Employs asynchronous pipelines to handle concurrent requests and real-time data flow.
• Strict Tool Argument Validators - Validates tool arguments against JSON schemas before routing them to server handlers.
• Data Streaming - Implements non-blocking pipelines for bidirectional data streaming and concurrent request handling.
• JSON-RPC Implementations - Uses the JSON-RPC protocol for structured request-response messaging to coordinate tool and resource access.
• Protocol-Agnostic Transport Layers - Implements a transport-agnostic layer supporting bidirectional communication over STDIO, HTTP, and Server-Sent Events.
• Protocol-Transport Decoupling Layers - Decouples protocol logic from the underlying communication layer to support multiple transport types.
• API Access Restrictions - Applies pluggable authorization hooks to restrict API endpoint access to authenticated clients.
• Filesystem Access Boundaries - Defines and updates the directory and file boundaries that the AI server is permitted to access.
• Transport Layer Security - Protects communication channels against DNS rebinding through header validation and transport-layer authorization hooks.
• Artificial Intelligence - Standardized interface for interacting with AI models and tools.

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