modelcontextprotocol/modelcontextprotocol

Features

• AI Context Integration Protocols - Implements a standardized protocol for connecting AI models to diverse data sources and tools.
• Model Context Protocol - Implements a standardized protocol for connecting AI models to local data sources and external tools for improved context.
• External Tool Execution - Invokes functional tools provided by a server to perform actions or retrieve data.
• Large Language Model Integration - Connects large language models to external data sources and executable tools through a standardized protocol.
• Model Context Protocol Implementations - Implements the standardized client-side logic to discover and utilize external data sources and tools via the protocol.
• Agentic Workflow Orchestration - Implements a system for coordinating complex multi-turn sequences of tool calls and human-in-the-loop approvals for AI agents.
• Transport-Based Session Initiation - Starts communication sessions with external servers using either standard input/output or Server-Sent Events.
• Agentic Execution Loops - Provides a mechanism for executing multi-turn tool call sequences under user supervision.
• Tool Access Controls - Requires explicit user approval before a tool performs an action on the local system.
• User Input Elicitation - Requests additional information or clarification from the user to complete specific processes.
• User Information Collection - Collects structured data via forms or secure external redirects to satisfy tool and prompt requirements.
• Tool Orchestration - Coordinates the flow of sending tool definitions to models and executing the resulting calls.
• AI Tool Execution - Provides a framework enabling AI models to trigger external functions and interact with remote systems.
• AI Tool Definitions - Defines structured specifications for tool capabilities and parameters to ensure consistent AI agent interaction.
• Contextual Data Providers - Supplies external data such as file contents and database schemas to AI models to improve response accuracy.
• External Server Connectivity - Enables clients to interact with standardized servers to retrieve context and execute actions.
• Server Connection Managers - Establishes isolated, stateful sessions and routes bidirectional messages between the host and data source.
• Model Completion Requests - Allows servers to trigger model completions through the client to perform agentic behavior within tools.
• Resource Exposure - Exposes read-only data resources from a server to be utilized as background context for an AI model.
• Model Context Protocol Servers - Builds server interfaces that grant AI models access to external data sources and functional tools.
• Sampling Delegation Clients - Delegates model generation to the client to keep sensitive API keys secure from the server.
• Real-time Context Augmentation - Provides AI models with real-time access to local files, databases, and remote resources to improve response accuracy.
• Tool Schema Definitions - Uses JSON Schema 2020-12 to strictly define tool inputs and outputs for consistent validation.
• Protocol Compliance Layers - Enforces strict adherence to the protocol schema using type safety to prevent communication errors.
• Messaging UI - Facilitates bi-directional messaging between sandboxed UI elements and the host application using JSON-RPC.
• AI Tool Schemas - Uses JSON Schema 2020-12 to strictly define the arguments and return types of tools for AI execution.
• Data Source Connections - Provides a standardized interface for establishing secure connections between AI applications and local machine resources.
• Server Capability Discovery - Allows clients to query servers for supported protocol versions, capabilities, and usage instructions at runtime.
• Server Discovery APIs - Exposes a REST API and OpenAPI specification for programmatically retrieving server metadata.
• Tool Invocation Interfaces - Provides standardized interfaces that allow AI models to trigger external functions and retrieve diverse result types.
• URI Resource Identifiers - Identifies external data sources and content through a standardized URI-based addressing system.
• Resource Content Readers - Enables the retrieval of text or binary data for specific resources using unique URI identifiers.
• Local File Managers - Provides server-based interfaces for reading directory structures and organizing files on a local system.
• Server Capability Exposure - Defines the mechanism for servers to publish available tools, resources, and prompts to extending model functionality.
• Resource Discovery - Provides programmatic discovery of data resources using pagination and MIME types to identify useful content.
• Tool Discovery - Retrieves tool names, descriptions, and input schemas from connected servers for LLM tool-calling.
• Server Metadata APIs - Retrieves server identity and version details through a standardized REST API for discovery.
• Capability Negotiation Protocols - Negotiates supported features and protocol versions during the initial connection phase to ensure compatibility.
• AI Client-Server Communication Protocols - Establishes secure bidirectional messaging between AI applications and servers using JSON-RPC, HTTP, or Standard IO.
• API Client Integrations - Provides SDKs that abstract the discovery API to connect applications to the protocol registry.
• Connection Establishment Protocols - Implements mechanisms for initiating bidirectional network connections using standard I/O or Server-Sent Events.
• Protocol Version Verifiers - Agrees on a compatible communication version during initialization to maintain interoperability.
• JSON-RPC Implementations - Uses a bidirectional JSON-RPC message system to exchange tools, resources, and prompts.
• Connection and Session Management - Handles the entire lifecycle of network connections, including initialization, capability negotiation, and session control.
• Connection Lifecycle Managers - Coordinates the automated state transitions from initialization through operational communication to graceful shutdown.
• Interactive Input Requests - Prompts clients for missing information using multi-round-trip workflows to complete tasks.
• Protocol Negotiation Tools - Verifies supported extensions and settings during the client-server initialization handshake.
• Authorization Issuer Validators - Validates that authorization responses originate from the expected source by checking server metadata.
• Filesystem Access Boundaries - Specifies directory boundaries that servers should focus on to restrict the scope of operations.
• Operational Bounday Definitions - Sets filesystem or URI limits to constrain where a server is permitted to operate.
• Authorization Server Discovery Mechanisms - Identifies authorization server endpoints by querying protected resource metadata and discovery endpoints.
• Client Registration Protocols - Establishes trust between clients and servers through pre-registration or dynamic registration documents.
• Secure Generation Flows - Generates content via a client to ensure API keys remain secure and are not exposed to servers.
• Tool Permission Controllers - Controls which specific tools an AI model can access from a connected server.
• Asynchronous Task Execution - Handles long-running operations using durable handles that allow clients to poll for status and retrieve results.
• Tool Exposure Interfaces - Provides standardized interfaces that expose functional capabilities, such as API calls or file modifications, to AI agents.
• Model Tool Interfaces - Offers a consistent interface for AI models to invoke external utilities like search engines or calculators.
• Host Communication Interfaces - Implements a JSON-RPC communication layer over postMessage for bidirectional data exchange between sandboxed apps and their host.
• Model Tool Calls - Integrates tool calling into the model sampling process by specifying tool choices and parameters.
• Standardized Tool Execution - Implements a standardized protocol for executing external tools based on decisions made by an AI model.
• Long-term Memory Stores - Implements long-term memory systems using knowledge graphs to retain context across multiple AI sessions.
• Skill Discovery Metadata - Provides descriptive metadata and icons for tools and prompts to optimize discovery and context usage.
• Tool Metadata Annotations - Adds metadata to tools to define safety and efficiency properties like idempotency or read-only status.
• Task Input Elicitation - Enables servers to request and collect necessary structured data from clients mid-execution to allow an asynchronous task to proceed.
• Task Cancellation Handlers - Provides a mechanism to stop a running task and move it into a cancelled state.
• Chain of Thought Implementations - Guides models through reflective problem-solving using structured sequences of thought.
• Interactive AI Interfaces - Renders rich HTML components and dashboards within a chat interface to visualize tool outputs and data.
• Resource Embedding - Includes server-managed documentation and code samples directly into prompt messages using standardized URIs.
• Structured Tool Outputs - Provides tool outputs that conform to a predefined schema for strict validation and integration.
• UI-Triggered Tools - Registers server-side tools that automatically trigger the rendering of specific UI resources when invoked.
• Communication Interception - Validates, mutates, or logs data between models and external sources using lifecycle hooks.
• Prompt Templates - Provides reusable prompt structures and parameterized instruction sets to guide how language models use specific domain tools.
• Prompt Template Testing - Provides utilities to preview generated messages and validate prompt templates using custom inputs.
• Tool Change Notifications - Model Context Protocol sends asynchronous alerts to clients when the available tool list is updated.
• Incremental Result Streaming - Sends tool and resource outputs incrementally to avoid context pollution and support interactive media.
• Composed Tool Sequences - Runs a sequence of tool calls in a sandbox to return only the final result.
• Tool File Inputs - Declares file input requirements and encodes content so models can process external documents.
• Workflow State Management - Manages application state across requests using unique identifiers for creation and subsequent calls.
• Resource-Oriented Data Access - Provides structured, read-only access to external information sources identified by unique URIs.
• Web Content Scrapers - Retrieves web pages and converts them into optimized formats for large language model processing.
• Resource Metadata Annotators - Attaches metadata hints regarding audience and priority to help AI clients filter and prioritize content.
• Workflow Result Retrieval - Fetches the final output of a completed background task once it reaches a terminal status.
• Server Metadata Schemas - Defines server identity and capabilities using standardized schemas and verified ownership flows.
• Resource Inspection Interfaces - Exposes available resources and metadata to verify content delivery and test subscription behaviors.
• Task Status Push Notifications - Model Context Protocol send push notifications to inform requestors immediately when a background task changes status.
• Git Repository Interaction - Interacts with Git repositories to provide comprehensive codebase context and search capabilities for AI models.
• Dynamic Resource Templates - Implements URI templates that allow clients to access dynamic resources through specific arguments.
• Tool Bindings - Defines how executable functions are mapped to tools that a model can call with user approval.
• Subprocess Message Exchange - Supports bidirectional message exchange with servers launched as local subprocesses via standard I/O streams.
• Server Metadata Registries - Registers server metadata and versioning in centralized directories to enable discovery by clients.
• Server Metadata Publishers - Publishes installation and configuration data for public servers to facilitate client discovery.
• Client Resource Root Definitions - Tracks root URIs provided by the client to map and understand the local filesystem structure.
• Capability Change Notifications - Model Context Protocol send asynchronous updates to clients when available tools or server capabilities change.
• Proactive Server Updates - Model Context Protocol sends proactive updates from a server to a client when state changes or new data arrives.
• Transport Protocols - Manages a variety of local and remote transport mechanisms to move data between communicating entities.
• HTTP Transports - Implements HTTP and Server-Sent Events for bidirectional communication between AI clients and servers.
• Process Communication Transports - Establishes direct communication channels between local clients and servers on the same machine.
• Custom Transport Protocols - Model Context Protocol supports any bidirectional communication channel that preserves JSON-RPC message formats and lifecycle requirements.
• Event Notifications - Model Context Protocol push proactive server-side state changes to clients via a standardized callback mechanism.
• Message Exchange Implementations - Model Context Protocol connects clients to a standalone server using HTTP POST, GET, and Server-Sent Events.
• Scheduled Task Cancellation - Allows the client to signal the server to stop a running asynchronous task before completion.
• Multi-Round-Trip Request Orchestration - Coordinates complex interactions requiring sequential exchanges between client and server to complete tasks.
• Resource Update Subscriptions - Model Context Protocol opens a long-lived channel to receive asynchronous updates regarding tools, prompts, or resources.
• Protocol-Agnostic Transport Layers - Decouples protocol logic from the physical layer, supporting both standard I/O and HTTP with SSE.
• Protocol Communication Monitoring - Logs server notifications and messages in real-time to audit communication and troubleshoot error responses.
• Session State Management - Tracks communication state and connection lifecycles using unique identifiers across stateless transports.
• Scoped Token Issuance - Restricts tokens to the smallest possible set of permissions to minimize the impact of credential compromise.
• Isolated Execution Sandboxes - Runs UI components inside sandboxed iframes to prevent unauthorized access to the host.
• Partial Consent Flows - Handles incremental permission requests through authentication headers to grant access to specific tools or data.
• Enterprise AI Security - Implements OAuth and SSO to manage permissions and identity for AI tools across organizational boundaries.
• Policy Enforcement - Integrates with corporate identity providers to restrict which external tools are accessible to users.
• HTTP Authentication Schemes - Manages identity and access control using standard HTTP authentication schemes for remote connections.
• Identity and Access Management - Delegates access control and permission management to enterprise identity providers.
• Access Control - Integrates with organizational identity providers to centralize access control and enforce corporate security policies.
• Access Control and Authorization - Provides mechanisms for enforcing permissions and access control by integrating with enterprise identity providers.
• API and Machine Authentication - Uses client credentials flows to grant programmatic access to background services without user interaction.
• Machine Identity Authentication - Handles security between services using client credentials to enable non-interactive machine authentication.
• Resource Access Restrictions - Restricts access to sensitive endpoints using OAuth 2.1 flows to ensure permitted interaction.
• OAuth 2.0 Authorization Flows - Uses client credentials and JWT assertions to authenticate external tools and data sources.
• Enterprise SSO Authentication - Facilitates authentication flows integrated with enterprise identity providers and cloud-based single sign-on.
• Minimum Privilege Scopes - Selects the minimum necessary permissions for operations based on the metadata of the requested resource.
• UI Content Isolation - Sandboxes remote UI content in iframes and requires auditable communication for all messages.
• Remote Service Authentication - Secures access to remote resources using a variety of authentication methods including OAuth and API keys.
• Delegated Server Authorization - Uses OAuth 2.1 flows to obtain access tokens for requests to protected servers on behalf of owners.
• Token Validation - Verifies that access tokens are valid, not expired, and issued for the correct target server.
• Single Sign-On - Allows users to authenticate once with corporate credentials to access multiple approved servers.
• Stateless Session Management - Maintains context without persistent server-side sessions by using explicit state handles.
• Asynchronous Progress Tracking - Sends incremental status updates and progress percentages for asynchronous tasks using unique tokens.
• Asynchronous Task Managers - Provides a standardized interface for executing and tracking the progress of concurrent background operations.
• Background Task Management - Provides capabilities to list, retrieve status, and cancel long-running asynchronous operations.
• Asynchronous Tool Invocations - Processes tool calls asynchronously by returning a task handle for result retrieval through polling.
• Server Versioning - Assigns unique version strings to publications to track releases and ensure immutable metadata.
• Remote Server Registrations - Exposes remote server endpoints via HTTP or SSE to make tools accessible over the network.
• Visual Metadata Annotators - Associates visual assets, such as icons and URLs, with tools and resources to enable rich UI representation.
• Version Precedence Resolution - Resolves the most recent server version by parsing version strings and publication timestamps.
• Schema Validation Tools - Enforces data structure consistency for tool inputs and outputs using a standardized JSON Schema dialect.
• Resource Change Notifications - Notifies clients in real-time when the list of available resources changes or subscribed resources are updated.
• Execution Audit Trails - Generates visibility of requests and actions for integration into enterprise logging and compliance pipelines.
• Embedded Interface Components - Allows interactive HTML interfaces such as dashboards and visualizations to be embedded directly within chat conversations.
• Integrated AI Applications - Enables the creation of interactive HTML-based applications that run directly within AI clients.
• Interactive UI Delivery - Provides a standardized URI scheme for servers to deliver interactive HTML-based UI resources to clients.
• Interactive Widgets - Integrates rich interactive elements such as pickers, charts, and dashboards into chat interfaces.
• Interactive Model Output Rendering - Translates raw model responses into rich, interactive UI components like charts, forms, and video players.
• UI Asset Serving - Provides bundled HTML and assets via a secure URI scheme for rendering within a host application.
• Visual Identifiers - Supplies a standardized mechanism for servers to provide visual identifiers and icons for tools and resources.
• UI-to-Host Integrations - Model Context Protocol connects a rendered UI to the host to handle tool results and trigger server-side tools.
• Inline Conversation Rendering - Enables the display of HTML user interfaces inline within AI conversations for a richer user experience.
• Response Streaming - Provides incremental delivery of results via SSE streams for real-time notifications and progress updates.
• LLM Development Frameworks - Standard for connecting AI models to external tools and data.
• Development and Debugging Tools - Core official codebase and foundational examples.
• Model Context Protocol - Core specification and SDKs for the context protocol.

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