mcp-server-requirements-template.md

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"]