Single-Agent MCP Architecture

The simplest pattern involves a single LLM-powered agent equipped with a suite of MCP-enabled tools. This architecture prioritizes simplicity and direct control flow.

Key characteristics:

• Direct invocation path between agent and MCP servers
• Centralized decision-making and reasoning
• Lower engineering complexity
• Easier debugging and observability

Implementation considerations:

• Tool selection logic resides within the agent
• Single point of failure risk
• Limited parallelism
• Potential for context window overload

Multi-Agent Collaborative Systems

This pattern distributes work across multiple specialized agents, each potentially leveraging different MCP tools appropriate to their domain.

Key characteristics:

• Specialized agents with distinct responsibilities
• Complex coordination mechanisms required
• Better task parallelization
• Increased system resilience

Implementation considerations:

• Inter-agent communication protocol design
• State synchronization challenges
• Role definition and boundary management
• Increased overall system complexity

Hierarchical Agent Architectures

Hierarchical systems employ a structured approach with controller agents delegating to specialized worker agents, each potentially using different MCP tools.

Key characteristics:

• Clear separation of planning and execution
• Improved task decomposition
• Better handling of complex, multi-stage workflows
• Structured information flow

Implementation considerations:

• Defining clear interfaces between hierarchy levels
• Balancing autonomy vs. control
• Handling failure propagation through the hierarchy
• Managing context sharing across levels

Event-Driven Agent Systems

Event-driven architectures respond to external events, with agents and MCP tools activating based on triggers rather than direct invocation.

Key characteristics:

• Reactive to external stimuli and state changes
• Highly decoupled components
• Better scaling for real-time applications
• Natural fit for asynchronous workflows

Implementation considerations:

• Event schema design and standardization
• Managing event ordering and reliability
• Handling event loops and recursion
• Implementing event filtering and routing

Direct Invocation

The agent directly calls MCP tools through the protocol interface.

// Direct MCP tool invocation example
const result = await agent.invoke("use_mcp_tool", {
  server_name: "data-processing",
  tool_name: "analyze_dataset",
  arguments: { dataset_id: "123", method: "clustering" }
});

Advantages:

• Simplest implementation
• Clear control flow
• Direct error handling

Limitations:

• Synchronous execution blocks agent
• Limited parallelism
• Context window consumption for results

Tool Registry Pattern

MCP tools are registered in a central registry with metadata, enabling dynamic discovery and selection.

// Tool registry pattern
toolRegistry.register({
  name: "analyze_dataset",
  server: "data-processing",
  description: "Analyzes datasets using various methods",
  parameters: {
    dataset_id: { type: "string", required: true },
    method: { type: "string", enum: ["clustering", "classification"] }
  }
});

// Agent can discover and select tools dynamically
const availableTools = toolRegistry.queryTools("data analysis");

Advantages:

• Dynamic tool discovery
• Better separation of concerns
• Improved metadata for tool selection

Limitations:

• Additional architectural complexity
• Registration process overhead
• Potential versioning challenges

Proxy/Adapter Pattern

A proxy layer sits between agents and MCP servers, handling protocol details, caching, and additional logic.

// Proxy/adapter pattern
class MCPToolProxy {
  constructor(private serverName: string) {}
  
  async callTool(toolName: string, args: any) {
    // Handle caching, retries, logging
    const cacheKey = this.generateCacheKey(toolName, args);
    if (this.cache.has(cacheKey)) return this.cache.get(cacheKey);
    
    try {
      const result = await mcpClient.callTool(this.serverName, toolName, args);
      this.cache.set(cacheKey, result);
      return result;
    } catch (error) {
      // Handle errors, retry logic
    }
  }
}

Advantages:

• Centralized error handling and retries
• Result caching opportunities
• Consistent logging and monitoring
• Abstraction of protocol details

Limitations:

• Additional indirection
• Potential performance overhead
• More complex debugging

Event-Sourcing Pattern

Results from MCP tool calls are treated as events in an event stream, providing history and enabling replay.

// Event-sourcing pattern
async function handleToolCall(agent, toolCall) {
  // Record the intent
  await eventStore.record({
    type: "TOOL_CALL_INITIATED",
    tool: toolCall.tool,
    args: toolCall.args,
    timestamp: Date.now()
  });
  
  try {
    const result = await mcpClient.callTool(
      toolCall.server, 
      toolCall.tool, 
      toolCall.args
    );
    
    // Record the result
    await eventStore.record({
      type: "TOOL_CALL_COMPLETED",
      tool: toolCall.tool,
      args: toolCall.args,
      result,
      timestamp: Date.now()
    });
    
    return result;
  } catch (error) {
    // Record failure
    await eventStore.record({
      type: "TOOL_CALL_FAILED",
      tool: toolCall.tool,
      args: toolCall.args,
      error: error.message,
      timestamp: Date.now()
    });
    throw error;
  }
}

Advantages:

• Complete audit trail of interactions
• Ability to replay sequences
• Advanced debugging capabilities
• State reconstruction

Limitations:

• Significant architectural complexity
• Storage overhead
• Processing overhead

Tool-Managed State

MCP tools maintain their own state, with each successive call potentially building on previous calls.

Implementations:

• Stateful MCP servers keeping session data
• Database-backed tools with client identification
• File-based state persistence per client

Advantages:

• Encapsulated state management
• Reduced context window usage
• Clearer separation of concerns

Limitations:

• State synchronization challenges
• Difficult to reason about global system state
• Potential for state inconsistency

Agent-Managed State

The agent maintains all state information in its context window, passing relevant state to MCP tools as needed.

Implementations:

• Context window state tracking
• Working memory patterns
• Explicit state serialization

Advantages:

• Centralized state visibility
• Simplified reasoning about system state
• Better control over state transitions

Limitations:

• Context window constraints
• Token usage inefficiency
• Potential state loss during agent restarts

Hybrid State Management

A balanced approach where critical state is shared across both agent and tools based on usage patterns.

Implementations:

• Long-term state in external stores
• Working state in context window
• State synchronization protocols

Advantages:

• Optimized token usage
• Better failure recovery
• Flexibility for different state types

Limitations:

• Increased system complexity
• State duplication risks
• Synchronization overhead

Consider an implementation of a hierarchical agent system that uses MCP for external tool integration:

// Architectural example - Hierarchical agents with MCP

interface AgentMessage {
  role: "system" | "user" | "assistant";
  content: string;
  toolResults?: ToolResult[];
}

interface ToolResult {
  toolName: string;
  serverName: string;
  args: any;
  result: any;
  timestamp: number;
}

// Controller agent coordinates overall task execution
class ControllerAgent {
  private history: AgentMessage[] = [];
  private workerAgents: Map<string, WorkerAgent> = new Map();
  
  constructor(private systemPrompt: string) {
    this.history.push({ role: "system", content: systemPrompt });
  }
  
  registerWorker(name: string, worker: WorkerAgent) {
    this.workerAgents.set(name, worker);
  }
  
  async processTask(task: string): Promise<string> {
    // 1. Plan the task execution
    const plan = await this.createPlan(task);
    
    // 2. Delegate subtasks to workers
    const results = await this.executeSubtasks(plan);
    
    // 3. Synthesize final result
    return this.synthesizeResults(results);
  }
  
  private async createPlan(task: string): Promise<SubTask[]> {
    // Use LLM to decompose task
    this.history.push({ role: "user", content: `Create a plan for: ${task}` });
    const planning = await llmClient.generateResponse(this.history);
    this.history.push({ role: "assistant", content: planning.content });
    
    // Parse plan from LLM response
    return this.extractSubtasksFromPlan(planning.content);
  }
  
  private async executeSubtasks(subtasks: SubTask[]): Promise<SubTaskResult[]> {
    const results: SubTaskResult[] = [];
    
    for (const subtask of subtasks) {
      const worker = this.workerAgents.get(subtask.assignedWorker);
      if (!worker) throw new Error(`No worker found: ${subtask.assignedWorker}`);
      
      const result = await worker.executeSubtask(subtask);
      results.push(result);
      
      // Update controller's understanding of current state
      this.history.push({
        role: "user",
        content: `Worker ${subtask.assignedWorker} completed subtask: ${subtask.description}\nResult: ${result.output}`,
        toolResults: result.toolResults
      });
    }
    
    return results;
  }
  
  private async synthesizeResults(results: SubTaskResult[]): Promise<string> {
    this.history.push({
      role: "user",
      content: `Synthesize the final result based on all completed subtasks.`
    });
    
    const synthesis = await llmClient.generateResponse(this.history);
    this.history.push({ role: "assistant", content: synthesis.content });
    
    return synthesis.content;
  }
}

// Worker agent handles specific subtasks with MCP tools
class WorkerAgent {
  private history: AgentMessage[] = [];
  private availableTools: MCPTool[] = [];
  
  constructor(
    private name: string,
    private systemPrompt: string,
    private mcpClient: MCPClient
  ) {
    this.history.push({ role: "system", content: systemPrompt });
  }
  
  registerTool(tool: MCPTool) {
    this.availableTools.push(tool);
  }
  
  async executeSubtask(subtask: SubTask): Promise<SubTaskResult> {
    this.history.push({
      role: "user",
      content: `Execute subtask: ${subtask.description}\nCurrent context: ${subtask.context}`
    });
    
    const toolResults: ToolResult[] = [];
    let currentResponse = await llmClient.generateResponse(this.history);
    this.history.push({ role: "assistant", content: currentResponse.content });
    
    // Extract and execute tool calls
    while (true) {
      const toolCall = this.extractToolCall(currentResponse.content);
      if (!toolCall) break;
      
      // Execute the MCP tool call
      const result = await this.mcpClient.callTool(
        toolCall.serverName,
        toolCall.toolName,
        toolCall.args
      );
      
      toolResults.push({
        toolName: toolCall.toolName,
        serverName: toolCall.serverName,
        args: toolCall.args,
        result,
        timestamp: Date.now()
      });
      
      // Continue agent reasoning with tool result
      this.history.push({
        role: "user",
        content: `Tool result: ${JSON.stringify(result)}`,
      });
      
      currentResponse = await llmClient.generateResponse(this.history);
      this.history.push({ role: "assistant", content: currentResponse.content });
    }
    
    return {
      subtaskId: subtask.id,
      output: currentResponse.content,
      toolResults
    };
  }
  
  private extractToolCall(content: string): ToolCall | null {
    // Implementation to extract tool call from LLM response
    // Returns null if no tool call is present
  }
}

This architecture demonstrates several key implementation patterns:

• Clear separation between controller and worker agents
• Explicit task decomposition and delegation
• Structured history tracking
• Tool result integration into context
• Multi-level reasoning

Reactive Agent Systems

Inspired by reactive programming principles, these systems represent agent interactions as observable streams of events.

Key characteristics:

• Asynchronous processing of agent and tool interactions
• Declarative composition of agent behaviors
• Better handling of concurrent operations
• Natural fit for real-time applications

Implementation approaches:

• RxJS-based agent coordination
• Event stream processing
• Reactive state management

Tool-First Architectures

Rather than starting with agent capabilities, these systems design around tool interfaces first, then build agent interaction patterns.

Key characteristics:

• Tool interface definition as foundation
• Protocol-driven development
• Clear separation between tool implementation and agent reasoning
• Better interoperability between different agent systems

Implementation approaches:

• Schema-first tool definition
• Contract-driven development
• Microservices-inspired architecture

Memory-Augmented Agent Systems

These architectures explicitly separate reasoning from memory, using specialized systems for different types of memory.

Key characteristics:

• Separation of working, episodic, and semantic memory
• Explicit memory operations (store, retrieve, forget)
• Context window optimization
• Better handling of long-running tasks

Implementation approaches:

• Vector databases for semantic memory
• Time-series storage for episodic memory
• Cache systems for working memory
• Memory scheduling and retrieval strategies