# MCP Server Requirements Document

You are an expert AI systems architect specializing in Model Context Protocol (MCP) server development (https://modelcontextprotocol.io/). Create a comprehensive, detailed implementation plan for the following MCP server:

## Project Overview
[Provide a concise description of your MCP server concept - what it does and the primary value it delivers]

- Example: "This MCP server enables real-time context sharing between AI models and client applications, improving response accuracy and user interaction efficiency."

## Target Users & Use Cases
- Who will interact with this MCP server?  
  [Types of users, e.g., developers building AI applications, AI agents, end-users via apps]  
- What are their primary goals?  
  [User objectives, e.g., seamless model context integration, reduced latency in AI responses]  
- What problems does this solve for them?  
  [Issues addressed, e.g., lack of standardized context management, inefficient data exchange]

## Core Functionality
Provide complete details for ALL required capabilities of the MCP server:  
- Capability 1: [Description with complete requirements, e.g., "Context ingestion from client inputs with support for text and metadata"]  
- Capability 2: [Description with complete requirements, e.g., "Real-time context synchronization across connected models"]  
- Capability 3: [Description with complete requirements, e.g., "Context retrieval API for external applications"]  
- [Continue with all needed capabilities]

## Interaction Interface Requirements
- Interaction style:  
  [e.g., REST API, WebSocket, GraphQL - specify how clients connect to the MCP server]  
- Protocol requirements:  
  [Specific protocols or standards, e.g., JSON-RPC over HTTPS, WebSocket with custom framing]  
- Key components needed:  
  [e.g., API endpoints like `/context/create`, message formats like `{ "context_id": "xyz", "data": {...} }`]

## Data Model
- Describe all entities and their relationships:  
  [Entities and connections, e.g., "Context (ID, data, timestamp) linked to Session (user_id, session_id)"]  
- Specify data types and validation rules:  
  [Data specifications, e.g., "Context data: JSON object, max size 1MB; Timestamp: ISO 8601 string"]  
- Include authentication/authorization model if applicable:  
  [Security model, e.g., "OAuth 2.0 tokens required for all requests"]

## User Flows
Detail the complete user journey when interacting with the MCP server:  
1. Entry point: [How users or agents connect, e.g., "Client sends POST to `/context/init`"]  
2. Authentication: [If applicable, e.g., "Server validates JWT in Authorization header"]  
3. Main interaction: [Step-by-step flow, e.g., "Client submits context data, server processes and assigns context_id"]  
4. Edge cases: [How to handle failures/exceptions, e.g., "Invalid data returns 400 Bad Request"]  
5. Success states: [What completion looks like, e.g., "Server returns 200 OK with context_id"]  

## Technical Requirements
- MCP Framework:  
  [Specify the MCP framework to use, e.g., Agno, JADE, SPADE, MESA, or a custom implementation based on modelcontextprotocol.io]  
- Backend requirements:  
  [If applicable, e.g., "Node.js with Express, hosted on AWS EC2"]  
- APIs/Integrations needed:  
  [List all external services, e.g., "Integration with Redis for caching"]  
- Performance expectations:  
  [Throughput/Response times, e.g., "Handle 1000 requests/sec with <50ms latency"]  
- Security considerations:  
  [User authentication/Data protection, e.g., "TLS 1.3 encryption, rate limiting"]

## Accessibility & Compliance
- Required accessibility standards:  
  [Interaction standards, if applicable, e.g., "API documentation compliant with OpenAPI 3.0"]  
- Compliance requirements:  
  [GDPR/CCPA/etc if applicable, e.g., "User data anonymization per GDPR"]  

## Examples & Inspiration
- Similar MCP servers:  
  [Descriptions or references, e.g., "Context management in Hugging Face’s inference server"]  
- Specific elements to emulate:  
  [Capabilities/Interactions, e.g., "Low-latency context updates like Google’s Realtime API"]  

## Implementation Priorities
- Must-have capabilities:  
  [Critical functionality, e.g., "Context ingestion and retrieval"]  
- Nice-to-have capabilities:  
  [Secondary importance, e.g., "Support for multi-language contexts"]  
- Future enhancements:  
  [Post-initial release, e.g., "Context versioning system"]  

## Additional Context
[Any other information that would help create the best possible implementation, e.g., "Target deployment by Q1 2025 with a team of 3 developers"]