MCP-Agent Architecture#
Overview#
The MCP-Agent is a Rust implementation of the Model Context Protocol (MCP), providing a framework for building AI agents with standardized communication patterns. This document describes the architecture of the system, including component structure, communication patterns, and key workflows.
System Components#
The MCP-Agent architecture consists of the following major components:
Provides the core protocol implementation with JSON-RPC 2.0 specification, handling message serialization, deserialization, and transport protocols. |
Implements agent patterns and lifecycle management for both single agents and multi-agent orchestration. |
Provides comprehensive error handling with proper propagation, logging, and recovery mechanisms across all components. |
Implements async/await patterns for concurrent operations, leveraging Rust’s async runtime for efficient resource utilization. |
Provides monitoring, metrics, and alerting through OpenTelemetry integration for comprehensive system observability. |
Orchestrates tasks and manages workflows with proper error recovery and state management capabilities. |
Handles user interaction during workflow execution, including prompts, validation, and timeout handling. |
Provides extension points for adding new components, models, and tools with minimal changes to the core system. |
Manages connections to various LLM providers with standardized interfaces and proper error handling. |
Graph Visualization System#
Component Diagram#
Module Dependencies#
The Model Context Protocol specification defines the standards for communication between AI models and software components through a JSON-RPC interface. |
Core Communication Flow#
The MCP protocol enables communication between AI models and software components through a standardized JSON-RPC interface. The sequence diagram below illustrates the basic communication pattern:
Workflow Execution#
The workflow engine orchestrates the execution of complex tasks with the following sequence:
Agent Patterns#
The MCP-Agent framework supports various agent patterns as described in the “Building Effective Agents” paper:
Error Handling#
The error handling architecture provides a consistent approach to error management:
Sequence Diagrams#
Event-Driven Workflow#
Human Input Workflow#
JSON-RPC Communication#
Data Flow Architecture#
The data flow between components follows a consistent pattern:
Deployment Architecture#
The MCP-Agent can be deployed in various configurations:
Security Architecture#
The security architecture ensures proper data protection and access control:
Extension Points#
The MCP-Agent architecture provides several extension points:
Terminal Interface Architecture#
The dual terminal system allows for both local console and remote web-based interaction with the MCP-Agent:
Terminal I/O Flow#
Architecture-Requirements Traceability#
ID |
Title |
Status |
Links |
|---|---|---|---|
MCP Protocol Layer |
implemented |
||
Agent System |
implemented |
||
Error Handling System |
implemented |
||
Concurrency Management |
implemented |
||
Telemetry System |
implemented |
||
Workflow Engine |
implemented |
||
Human Input Module |
implemented |
||
Extension System |
implemented |
||
LLM Client |
implemented |
MCP Primitives#
The MCP-Agent implements several core primitives defined in the Model Context Protocol specification. These primitives provide standardized interfaces for language models to interact with various capabilities of the system.
Tools System#
The Tools System enables language models to perform actions through a standardized interface. It consists of the following key components:
Tools Provider Interface: Defines a trait for implementing tool providers that can register and execute tools.
Basic Tool Provider: A concrete implementation of the ToolsProvider trait that handles registration and invocation of tools.
Tool Model: Represents a tool with a name, description, and JSON Schema for validating input parameters.
Tool Results: Provides structured output from tool execution with support for various content types including text, images, audio, and resources.
JSON-RPC Handler: Integrates tools with the MCP protocol’s JSON-RPC interface, allowing clients to discover and call tools.
The Tools System allows the registration of custom tools with proper validation of input parameters through JSON Schema. It supports various return types and proper error handling during tool execution.
Resources System#
The Resources System provides structured access to content and data sources. Its key components include:
Resource Provider Interface: Defines a trait for implementing resource providers that can manage various types of resources.
File System Provider: A concrete implementation that manages resources stored in the file system.
Resource Model: Represents a resource with metadata and versioned content.
Template System: Supports parameterized resource templates that can be instantiated with specific values.
JSON-RPC Handler: Integrates resources with the MCP protocol’s JSON-RPC interface.
The Resources System supports various content types (text, binary), resource versioning, and template-based content generation. It provides a unified interface for language models to access and manipulate external data sources.
Conclusion#
The MCP-Agent architecture provides a robust, extensible framework for implementing AI agents using the Model Context Protocol. The modular design allows for customization of various components while maintaining a consistent interface for developers.
The Rust implementation leverages the language’s strengths in performance, safety, and concurrency, providing a solid foundation for building production-ready AI agent systems.