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# MCP AI Agent Product Requirements Document (PRD) ## Document Information - **Product Name**: MCP AI Agent - **Version**: 1.0 - **Date**: January 2025 - **Status**: Draft - **Stakeholders**: Development Team, DevOps, Product Management ## Table of Contents 1. [Executive Summary](#executive-summary) 2. [Product Overview](#product-overview) 3. [Business Objectives](#business-objectives) 4. [User Personas](#user-personas) 5. [Functional Requirements](#functional-requirements) 6. [Non-Functional Requirements](#non-functional-requirements) 7. [User Stories](#user-stories) 8. [Technical Requirements](#technical-requirements) 9. [Success Metrics](#success-metrics) 10. [Risks and Mitigations](#risks-and-mitigations) 11. [Timeline and Milestones](#timeline-and-milestones) 12. [Appendix](#appendix) ## Executive Summary The MCP AI Agent is a command-line tool that enables users to interact with MCP (Model Context Protocol) servers through an intelligent conversational interface. By combining AI capabilities with MCP server functionality, the agent provides an intuitive way to access and utilize MCP tools without requiring deep technical knowledge of the underlying protocol. ### Key Value Propositions - **Simplified Access**: Natural language interface to complex MCP operations - **Intelligent Automation**: AI-driven tool selection and execution - **Developer Productivity**: Reduce time spent on manual MCP interactions - **Extensible Platform**: Plugin architecture for custom functionality ## Product Overview ### Problem Statement Currently, interacting with MCP servers requires: - Deep understanding of the MCP protocol - Manual crafting of JSON-RPC messages - Complex error handling and retry logic - Separate tools for different MCP operations This creates barriers for adoption and reduces productivity for developers and teams using MCP-based systems. ### Solution The MCP AI Agent solves these problems by providing: - Natural language interface for MCP interactions - Automatic protocol handling and error recovery - Intelligent tool selection based on user intent - Unified interface for all MCP operations ### Target Market - **Primary**: Software developers working with MCP servers - **Secondary**: DevOps engineers managing MCP infrastructure - **Tertiary**: Technical teams adopting MCP-based architectures ## Business Objectives ### Primary Objectives 1. **Increase MCP Adoption**: Lower barrier to entry for MCP usage 2. **Improve Developer Productivity**: Reduce time spent on MCP operations by 60% 3. **Enable Innovation**: Provide platform for building MCP-based solutions ### Success Criteria - 1,000+ active users within 6 months - 80% user satisfaction rating - 50% reduction in MCP-related support tickets - 10+ community-contributed plugins ## User Personas ### 1. Alex - Senior Developer - **Background**: 8 years experience, works with microservices - **Goals**: Quickly interact with MCP servers during development - **Pain Points**: Context switching between code and MCP tools - **Needs**: Fast, reliable CLI tool with good documentation ### 2. Jordan - DevOps Engineer - **Background**: 5 years experience, manages production infrastructure - **Goals**: Monitor and troubleshoot MCP server issues - **Pain Points**: Lack of unified tooling for MCP operations - **Needs**: Scriptable interface, logging, and monitoring capabilities ### 3. Sam - Junior Developer - **Background**: 1 year experience, learning MCP architecture - **Goals**: Understand and use MCP servers effectively - **Pain Points**: Steep learning curve for MCP protocol - **Needs**: Intuitive interface with helpful error messages ## Functional Requirements ### Core Features #### FR1: Connection Management - **FR1.1**: Connect to MCP servers via WebSocket or HTTP - **FR1.2**: Support authenticated and unauthenticated connections - **FR1.3**: Automatic reconnection with exponential backoff - **FR1.4**: Connection health monitoring and status display #### FR2: Conversational Interface - **FR2.1**: Natural language input processing - **FR2.2**: Context-aware responses - **FR2.3**: Multi-turn conversation support - **FR2.4**: Command history and session management #### FR3: MCP Tool Integration - **FR3.1**: Automatic tool discovery from MCP server - **FR3.2**: Intelligent tool selection based on user queries - **FR3.3**: Tool parameter validation and formatting - **FR3.4**: Tool execution with progress tracking #### FR4: CLI Commands - **FR4.1**: Interactive chat mode (`mcp-agent start`) - **FR4.2**: Tool listing (`mcp-agent list-tools`) - **FR4.3**: Configuration display (`mcp-agent config`) - **FR4.4**: Connection testing (`mcp-agent test-connection`) #### FR5: Session Management - **FR5.1**: Save and restore conversation history - **FR5.2**: Export conversations to various formats - **FR5.3**: Search through past conversations - **FR5.4**: Session statistics and analytics ### Advanced Features #### FR6: Plugin System - **FR6.1**: Dynamic plugin loading - **FR6.2**: Plugin marketplace/registry - **FR6.3**: Plugin development SDK - **FR6.4**: Plugin sandboxing for security #### FR7: Monitoring and Logging - **FR7.1**: Structured logging with configurable levels - **FR7.2**: Performance metrics collection - **FR7.3**: Error tracking and reporting - **FR7.4**: Audit trail for tool executions ## Non-Functional Requirements ### Performance Requirements - **NFR1**: Response time < 2 seconds for 95% of requests - **NFR2**: Support 100 concurrent connections per instance - **NFR3**: Memory usage < 500MB under normal operation - **NFR4**: Startup time < 3 seconds ### Reliability Requirements - **NFR5**: 99.9% uptime for core functionality - **NFR6**: Graceful degradation when MCP server unavailable - **NFR7**: Zero data loss for conversation history - **NFR8**: Automatic recovery from transient failures ### Security Requirements - **NFR9**: Secure storage of API keys and credentials - **NFR10**: TLS/SSL for all network communications - **NFR11**: Input sanitization to prevent injection attacks - **NFR12**: Role-based access control for multi-user scenarios ### Usability Requirements - **NFR13**: Intuitive CLI with helpful error messages - **NFR14**: Comprehensive documentation and examples - **NFR15**: Context-sensitive help system - **NFR16**: Support for common terminal emulators ### Compatibility Requirements - **NFR17**: Python 3.8+ compatibility - **NFR18**: Cross-platform support (Windows, macOS, Linux) - **NFR19**: MCP protocol version compatibility - **NFR20**: AI model provider agnostic ## User Stories ### Epic 1: Basic MCP Interaction **US1.1**: As a developer, I want to connect to my MCP server using simple commands so that I can start working quickly. ``` GIVEN I have the MCP server URL and credentials WHEN I run `mcp-agent start` THEN I should be connected and see a confirmation message ``` **US1.2**: As a developer, I want to ask questions in natural language so that I don't need to learn MCP protocol details. ``` GIVEN I am connected to an MCP server WHEN I type "search for user documentation" THEN the agent should use the appropriate search tool and display results ``` ### Epic 2: Tool Management **US2.1**: As a developer, I want to see all available tools so that I know what capabilities are available. ``` GIVEN I am connected to an MCP server WHEN I run `/tools` or `mcp-agent list-tools` THEN I should see a formatted list of all available tools with descriptions ``` **US2.2**: As a power user, I want to explicitly use specific tools so that I have fine-grained control. ``` GIVEN I know a specific tool exists WHEN I type `/use search query="specific term"` THEN the agent should execute that exact tool with the provided parameters ``` ### Epic 3: Session Management **US3.1**: As a user, I want my conversation history saved so that I can reference previous interactions. ``` GIVEN I have been using the agent WHEN I restart the application THEN I should be able to access my previous conversations ``` **US3.2**: As a team lead, I want to export conversations so that I can share knowledge with my team. ``` GIVEN I have a conversation with useful information WHEN I run `/export markdown` THEN I should get a markdown file with the formatted conversation ``` ## Technical Requirements ### System Architecture - **Microservices-compatible**: Designed for distributed systems - **Event-driven**: Async operations throughout - **Pluggable**: Extension points for customization - **Observable**: Built-in monitoring and tracing ### Development Requirements - **Language**: Python 3.8+ - **Framework**: Click for CLI, Rich for terminal UI - **Testing**: Pytest with 80% coverage minimum - **Documentation**: Sphinx-generated API docs ### Integration Requirements - **MCP Protocol**: Full compliance with MCP specification - **AI Models**: Support for OpenAI, Anthropic, and local models - **Authentication**: OAuth2, API keys, and custom auth - **Monitoring**: Prometheus metrics, OpenTelemetry traces ### Deployment Requirements - **Containerization**: Docker images with multi-stage builds - **Package Management**: PyPI distribution - **Configuration**: Environment-based configuration - **Platforms**: Windows, macOS, Linux support ## Success Metrics ### User Adoption Metrics - **MAU** (Monthly Active Users): Target 1,000 within 6 months - **User Retention**: 60% 30-day retention rate - **Session Duration**: Average 15 minutes per session - **Feature Adoption**: 80% of users using AI features ### Performance Metrics - **Response Time**: P95 < 2 seconds - **Error Rate**: < 1% of requests fail - **Availability**: 99.9% uptime - **Throughput**: 100 requests/second per instance ### Business Impact Metrics - **Productivity Gain**: 60% reduction in MCP task time - **Support Tickets**: 50% reduction in MCP-related issues - **Community Growth**: 50+ GitHub stars per month - **Plugin Ecosystem**: 10+ community plugins ## Risks and Mitigations ### Technical Risks | Risk | Impact | Probability | Mitigation | |------|--------|-------------|------------| | MCP Protocol Changes | High | Medium | Version detection and compatibility layer | | AI Model Unavailability | High | Low | Fallback to basic mode, local model support | | Performance Degradation | Medium | Medium | Caching, connection pooling, monitoring | | Security Vulnerabilities | High | Low | Security audits, dependency scanning | ### Business Risks | Risk | Impact | Probability | Mitigation | |------|--------|-------------|------------| | Low User Adoption | High | Medium | User feedback loops, documentation | | Competition | Medium | High | Unique features, plugin ecosystem | | Maintenance Burden | Medium | Medium | Automated testing, CI/CD | ## Timeline and Milestones ### Phase 1: MVP (Weeks 1-4) - Basic MCP connection - Simple CLI interface - Core chat functionality - Basic error handling ### Phase 2: Core Features (Weeks 5-8) - AI integration - Tool discovery and execution - Session management - Comprehensive testing ### Phase 3: Advanced Features (Weeks 9-12) - Plugin system - Performance optimization - Monitoring integration - Documentation ### Phase 4: Beta Release (Weeks 13-16) - Beta testing program - Bug fixes and improvements - Performance tuning - Launch preparation ### Phase 5: GA Release (Week 17+) - Public release - Marketing campaign - Community building - Ongoing maintenance ## Appendix ### A. Glossary - **MCP**: Model Context Protocol - Protocol for AI model communication - **CLI**: Command Line Interface - **WebSocket**: Protocol for bidirectional communication - **JSON-RPC**: JSON Remote Procedure Call protocol ### B. References - [MCP Protocol Specification](https://github.com/modelcontextprotocol/specification) - [Click Documentation](https://click.palletsprojects.com/) - [Rich Documentation](https://rich.readthedocs.io/) - [Pydantic Documentation](https://docs.pydantic.dev/) ### C. Mockups and Wireframes #### CLI Interface Example ``` $ mcp-agent start 🤖 MCP AI Agent v1.0 📡 Connecting to mcp://localhost:3000... ✅ Connected successfully! You: What tools are available?