MCP-Agent Requirements#
Requirements Overview#
This document outlines the key requirements for the MCP-Agent framework. Each requirement is categorized and linked to related architectural components.
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Core Requirements#
Protocol Implementation#
The system must implement the Model Context Protocol (MCP) specification to enable standardized communication between AI assistants and software components. |
The MCP protocol implementation must support WebSocket and HTTP transport layers for message exchange, with appropriate connection management and error handling. |
The JSON-RPC implementation must support batch requests and responses as per the JSON-RPC 2.0 specification to improve throughput and reduce latency. |
The MCP protocol implementation must support authentication mechanisms such as API keys, OAuth, or JWT tokens, along with transport-level encryption to ensure secure communications. |
Agent Patterns#
The framework must support all patterns described in the Building Effective Agents paper, including composable pattern chaining. |
The system must implement OpenAI’s Swarm pattern for multi-agent orchestration in a model-agnostic way. |
MCP Primitives#
Quality Assurance#
Type System#
The system must maintain strict type safety through comprehensive type hints in Python and Rust’s type system in the migrated version. |
The Rust implementation must leverage the ownership system to provide memory safety guarantees without runtime overhead. |
The system must validate all data using Pydantic in Python and Serde in Rust with runtime type checking. |
Error Handling#
The system must implement comprehensive error handling with proper propagation and logging in both Python and Rust. |
Testing and Verification#
The system must maintain comprehensive test coverage including unit tests, integration tests, and performance benchmarks. |
Critical components of the system must be formally verified using Rust’s verification tools (such as KLEE or Creusot) to ensure correctness and safety properties. |
Performance#
Concurrency#
The system must provide robust async/await support for concurrent operations in both Python and Rust implementations. |
API#
The system must maintain low latency API endpoints with response times under 100ms for 95th percentile of requests. |
Integrations#
Monitoring#
The system must integrate with OpenTelemetry for comprehensive monitoring and metrics collection. |
Models#
The system must support integration with major AI models (Anthropic, OpenAI, Cohere) with proper error handling and retries. |
Workflow#
The system must support workflow orchestration with proper error recovery and state management. |
The system must provide a mechanism for human input during workflow execution, including interactive prompts and timeouts. |
User Interface#
The system must provide a user-friendly CLI interface with comprehensive command options and help documentation. |
Maintainability#
Documentation#
The system must maintain comprehensive documentation including API references, examples, and migration guides. |
Dependency Management#
The system must use modern dependency management tools (uv for Python, Cargo for Rust) with proper version pinning. |
Code Quality#
The system must enforce code quality through linting (Ruff for Python, clippy for Rust) and pre-commit hooks. |
Migration#
The system must provide a clear migration path from Python to Rust while maintaining backward compatibility. |
Security#
The system must implement proper security measures including secure API key handling and input sanitization. |
Architecture#
The system must be designed for extensibility, allowing easy addition of new components, models, and tools. |