mcp-use/mcp-use
Mcp Use
mcp-use is a development framework designed for building, deploying, and managing servers, clients, and autonomous agents using the Model Context Protocol. It provides a comprehensive toolkit for creating servers that expose custom tools, data resources, and prompts to compatible AI agents.
The project distinguishes itself by offering a complete lifecycle for protocol-based applications, including a dedicated hosting platform for production servers and a compliance validator to ensure servers meet marketplace publishing requirements. It also features an observability suite for tracing protocol traffic and a set of tools for generating the assets and metadata required for app store submissions.
The framework covers broad capability areas including automated deployment pipelines with branch preview provisioning, comprehensive monitoring with latency and reliability analytics, and security via multi-tenant hosting and tool-level access control. It also includes UI integration for embedding customizable chat interfaces and widgets directly into applications.
Development is supported through software development kits for TypeScript and Python, along with a command-line interface for project scaffolding and server boilerplate generation.
Features
- Model Context Protocol - Implements a comprehensive framework for building and deploying servers, clients, and agents using the Model Context Protocol.
- AI Agent Tool Integrations - Connects autonomous AI agents to external APIs and databases using the standardized Model Context Protocol.
- MCP Servers - Implements spec-compliant servers that expose internal tools, APIs, and databases to AI agents.
- Protocol Validation Tools - Implements validation tools to verify that MCP server handshakes and tool definitions conform to protocol specifications.
- Managed Hosting Platforms - Provides a dedicated cloud environment for deploying and scaling Model Context Protocol servers with integrated authentication.
- AI Chat Surface Embedding - Integrates customizable chat surfaces directly into products to provide AI-driven user interfaces.
- Protocol - Provides Python and TypeScript SDKs to scaffold and implement Model Context Protocol tools and resources.
- Autonomous Agent Frameworks - Provides a framework for building autonomous agents that execute complex tasks via a standardized tool protocol.
- Resource Exposure - Enables the exposure of read-only data resources and custom prompts as context for AI models.
- Model Context Protocol Servers - Provides standardized server implementations and automated protocol checks to ensure compatibility before publishing.
- Observability Tools - Ships a monitoring and debugging suite for tracing protocol traffic and analyzing AI agent execution flow.
- Tool Schema Definitions - Defines tool capabilities and arguments using strict schemas for AI agent discovery and execution.
- Protocol Server Hosting - Provides a dedicated hosting platform for production servers with integrated authentication, multi-tenancy, and OAuth support.
- JSON-RPC Implementations - Uses a standardized JSON-RPC message protocol for communication between protocol clients and servers.
- Network Traffic Analyzers - Ships an observability suite for tracing and analyzing request payloads and tool execution between AI clients and servers.
- Software Development Kits - Offers dedicated TypeScript and Python SDKs for building MCP-compliant servers, clients, and AI agents.
- Access Control List Management - Provides allow-lists and deny-lists to restrict which specific tools are available to external visitors.
- API Request Authentication - Secures protocol requests using API keys, bearer tokens, and interactive command-line authentication flows.
- User Account Management - Manages user registration and login via email and third-party identity providers to secure platform access.
- Interactive Chat Interfaces - Displays a public-facing chat interface within a product for direct interaction with automated agents.
- Chat Stream Renderers - Creates components that register as tools and render as interactive widgets directly within chat streams.
- Agent Deployment Servers - Implements server-side deployment infrastructure for AI agents, including automatic triggers via repository webhooks.
- Compliance Validators - Provides a specialized tool to verify that servers meet Model Context Protocol specifications and marketplace requirements.
- Interactive AI Interfaces - Allows embedding of hosted chat windows and interactive UI widgets directly into applications for AI tool access.
- Configuration Validation - Validates server configurations against store and connector requirements to ensure marketplace compliance.
- Remote Connectivity - Supports programmatic access to tools and resources from remote protocol servers.
- Transport Layer Connectivity - Verifies connectivity and compatibility across various communication channels like standard I/O, HTTP, and SSE.
- Prompt Template Testing - Validates prompt templates with various arguments to ensure they function correctly before being connected to an agent.
- Usage Analytics - Provides usage analytics and metrics for tracking AI-driven tool and resource consumption.
- Distribution Standards - Provides automated checks to ensure applications meet specific store and connector requirements for distribution.
- Protocol Inspectors - Provides a web-based and CLI inspector for testing tool execution and monitoring server health.
- Server Capability Inspection - Provides real-time inspection of available server resources and metadata to verify content delivery.
- Deployment Control CLIs - Ships a CLI for controlling local development, managing server deployments, and retrieving execution logs.
- Git-Driven Deployment Pipelines - Implements automated build and branch preview pipelines triggered by repository push and pull request events.
- Marketplace Publishing Tools - Provides utilities for generating readiness checklists and assets required to distribute servers to application marketplaces.
- Cloud Deployment Automation - Automates cloud infrastructure provisioning and deployment through repository integration, supporting custom domains and previews.
- Quality Gates - Blocks merges or publishes in the deployment pipeline when required regression tests fail.
- Deployment Pipelines - Provides automated delivery pipelines that use repository access and push webhooks to deploy code.
- Automated Service Reliability - Monitors usage, latency, and reliability data to ensure the health and stability of deployed protocol services.
- Production Deployment Tools - Includes tools for managing the scale, availability, and lifecycle of MCP servers in production environments.
- Rate Limiters - Implements rate limits and session handling rules to protect public-facing server capacity from abuse.
- Multi-Tenant Agent Hosting - Provides isolated server environments and session management for multi-tenant AI agent hosting.
- Preview Environments - Automatically creates unique temporary environments for each Git branch to test revisions before production.
- Submission Asset Generators - Generates icons, copy, and structured listing metadata required for submitting servers to application stores.
- Protocol Debugging - Provides tools to trace live protocol traffic and debug interactions between clients and servers in production.
- API Payload Inspectors - Pairs test runs with detailed traces and data payloads to pinpoint the cause of communication failures.
- Network Traffic Replay Tools - Implements tracing and replaying of live production traffic to resolve communication layer issues.
- Execution Payload Tracing - Automatically records tool calls, resource reads, and prompt requests to provide full payload visibility.
- RPC Traffic Debugging - Provides detailed logging of every JSON-RPC message exchanged between client and server to resolve communication errors.
- Encrypted Secret Management - Provides secure, encrypted storage for variables and tokens used across production and preview environments.
- Marketplace Compliance - Verifies that servers meet specific store and connector requirements before public publishing.
- Latency Monitoring - Tracks response percentiles and linked metrics to resolve root causes of slow tool calls.
- Service Uptime Monitors - Monitors error budgets and availability using metrics specifically tuned for protocol traffic patterns.
- Distributed Tracing and Execution Analysis - Captures detailed execution traces of tool calls and resource reads for performance and debugging.
- Performance Dashboards - Provides a centralized dashboard for monitoring usage, latency, and reliability metrics.
- Server Health Monitoring - Monitors usage, latency, and reliability to track the efficiency and availability of protocol connections.
- Structured Logging - Implements searchable JSON logging with severity filtering to record operational data as machine-readable key-value pairs.
- Cross-Client Compatibility Validation - Executes validation checks across multiple platforms to ensure consistent behavior across different AI model interfaces.
- Agent-to-Tool Simulations - Enables verification of tool selection and execution by connecting server tools to language models via chat.
- Parallel Client Testing - Runs test suites against multiple clients in parallel to verify consistent tool parsing and behavior.
- Tool Execution Validation - Executes tools with custom parameters and provides real-time data display for logic validation.
- Embedded - Provides hosted or iframe-based chat windows that can be embedded into applications to expose server logic to users.
- AI Agent Frameworks - Tool for interacting with MCP servers via custom agents.
- Development and Debugging Tools - Client library for connecting LLMs to MCP servers for agent building.
