@agentics.org/agentic-mcp v1.0.4

@agentics.org/agentic-mcp

♾️ Agentic MCP

A powerful Model Context Protocol server with advanced AI capabilities by the Agentics Foundation. Built on the OpenAI Agents API/SDK using TypeScript, this package implements a comprehensive MCP server that enhances AI agents with sophisticated tools and orchestration capabilities:

🌟 Core Capabilities

• 🔍 Web Search Research: Generate comprehensive reports with up-to-date information from the web using gpt-4o-search-preview
• 📝 Smart Summarization: Create concise, well-structured summaries with key points and citations
• 🗄️ Database Integration: Query and analyze data from Supabase databases with structured results
• 👥 Customer Support: Handle inquiries and provide assistance with natural language understanding
• 🔄 Agent Orchestration: Seamlessly transfer control between specialized agents based on query needs
• 🔀 Multi-Agent Workflows: Create complex agent networks with parent-child relationships and shared context
• 🧠 Context Management: Sophisticated state tracking with memory, resources, and workflow management
• 🛡️ Guardrails System: Configurable input and output validation to ensure safe and appropriate responses
• 📊 Tracing & Debugging: Comprehensive logging and debugging capabilities for development
• 🔌 Edge Function Deployment: Ready for deployment as Supabase Edge Functions
• 🔄 Streaming Support: Real-time streaming responses for interactive applications

🚀 Installation

# Install globally
npm install -g @agentics.org/agentic-mcp

# Or as a project dependency
npm install @agentics.org/agentic-mcp

🏗️ Architecture

The Agentic MCP server is built on a modular architecture that enables seamless integration between AI agents and external tools:

Core Components

• MCP Server: The central server that registers and routes tool execution requests
• Tool Registry: Manages the available tools, ensuring each tool is validated and can be executed
• Context System: Sophisticated state management with parent-child relationships for complex workflows
• Agent Orchestration: Intelligent routing between specialized agents based on query requirements
• Guardrails System: Configurable input and output validation to ensure safe and appropriate responses

Agent Types

• Research Agent: Utilizes web search to gather and analyze information with citations
• Database Agent: Queries and analyzes data from Supabase databases
• Customer Support Agent: Handles user inquiries with natural language understanding
• Specialized Agents: Extensible framework for creating domain-specific agents

🔧 Configuration

Create a configuration file for the MCP server. Here's a sample configuration:

{
  "mcpServers": {
    "openai-agent": {
      "command": "node",
      "args": [
        "dist/index.js"
      ],
      "env": {
        "OPENAI_API_KEY": "YOUR_API_KEY_HERE",
        "SUPABASE_URL": "https://your-supabase-project.supabase.co",
        "SUPABASE_KEY": "YOUR_SUPABASE_KEY_HERE",
        "LLM_DEBUG": "true",
        "AGENT_LIFECYCLE": "true",
        "TOOL_DEBUG": "true"
      },
      "disabled": false,
      "autoApprove": [
        "research",
        "support",
        "customer_support",
        "database_query",
        "handoff_to_agent",
        "summarize"
      ]
    }
  }
}

🏃‍♂️ Usage

As a Command Line Tool

# Set required environment variables
export OPENAI_API_KEY=your_api_key_here
export SUPABASE_PROJECT_ID=your_project_id
export SUPABASE_ACCESS_TOKEN=your_access_token

# Run the MCP server
agentic-mcp

As a Library

import { OpenAIAgentMCPServer } from '@agentics.org/agentic-mcp';

const server = new OpenAIAgentMCPServer({
  name: 'openai-agent',
  version: '1.0.0',
  openai: {
    apiKey: process.env.OPENAI_API_KEY,
    defaultModel: 'gpt-4o-mini'
  },
  tracing: {
    enabled: true,
    level: 'debug'
  },
  tools: {
    enabled: ['research', 'database_query', 'customer_support', 'handoff_to_agent', 'summarize'],
    config: {
      database: {
        projectId: process.env.SUPABASE_PROJECT_ID,
        key: process.env.SUPABASE_ACCESS_TOKEN
      },
      openai: {
        apiKey: process.env.OPENAI_API_KEY
      }
    }
  },
  guardrails: {
    enabled: true,
    rules: []
  }
});

server.serve().catch(error => {
  console.error("❌ Server error:", error);
  process.exit(1);
});

🧩 Advanced Features

Context Management System

The Agentic MCP includes a sophisticated context management system that enables:

• Hierarchical Context: Parent-child relationships for complex agent workflows
• State Tracking: Persistent state across multiple interactions
• Memory Management: Store and retrieve information across agent calls
• Resource Sharing: Share resources between agents in a workflow
• Action Tracking: Monitor and log agent actions for auditing and debugging

// Example of context management
const context = new Context();
context.initializeWorkflow();
context.remember('user_preference', { theme: 'dark' });
context.trackAction('research_initiated');

Agent Orchestration

The system supports sophisticated agent orchestration patterns:

• Dynamic Routing: Automatically route queries to the most appropriate agent
• Handoff Protocol: Seamlessly transfer control between specialized agents
• Workflow Tracking: Maintain context and state across agent transitions
• Multi-Agent Collaboration: Enable multiple agents to work together on complex tasks

// Example of agent handoff
const handoffTool = {
  name: "handoff_to_agent",
  description: "Transfer the conversation to another specialized agent",
  parameters: {
    type: "object",
    properties: {
      agent_name: {
        type: "string",
        enum: ["researcher", "database_expert", "customer_support"]
      },
      reason: {
        type: "string"
      }
    },
    required: ["agent_name", "reason"]
  },
  execute: async (params) => {
    // Handoff logic
  }
};

Guardrails System

The Agentic MCP includes a configurable guardrails system for ensuring safe and appropriate responses:

• Input Validation: Filter and validate user inputs before processing
• Output Validation: Ensure agent responses meet safety and quality standards
• Custom Rules: Define custom validation rules for specific use cases
• Failure Handling: Graceful handling of guardrail violations

// Example guardrail implementation
const customGuardrail = {
  async check(msgs, context) {
    // Validation logic
    return true;
  },
  onFailure(msgs, context) {
    // Failure handling
  }
};

Streaming Support

The system supports real-time streaming responses for interactive applications:

• Partial Results: Stream partial results as they become available
• Tool Call Events: Stream tool call events for visibility into agent actions
• Progress Indicators: Provide progress updates during long-running operations

// Example of streaming usage
const streamIterator = AgentRunner.run_streamed(agent, [input]);
for await (const event of streamIterator) {
  // Process streaming event
  console.log(event.delta);
}

🛠️ Creating and Using Tools

Creating a New Tool

1. Create a New Module: Add a new TypeScript file in the src/mcp/tools/ directory
2. Implement the Interface: Your tool should implement the MCPTool interface
3. Register the Tool: Update the tool registration logic in src/mcp/server.ts to include the new tool

// Example tool implementation
export class CustomTool implements MCPTool {
  name = 'custom_tool';
  description = 'Description of your custom tool';
  inputSchema = {
    type: 'object',
    properties: {
      param1: {
        type: 'string',
        description: 'Description of parameter 1'
      }
    },
    required: ['param1']
  };

  async execute(params: any, context: Context): Promise<any> {
    // Tool implementation
    return { result: 'Success' };
  }
}

Using the Tools

• Tool Requests: MCP clients issue requests by specifying the tool name and parameters
• Web Search Integration: The research tool utilizes the gpt-4o-search-preview model with web search enabled
• Approval: Tools must be listed in the autoApprove section of the MCP settings

🔍 Troubleshooting and Debugging

• Logging: The server is configured with tracing options (LLM_DEBUG, AGENT_LIFECYCLE, TOOL_DEBUG)
• Registry Debugging: The ToolRegistry class logs tool registration and execution details
• Web Search: For the research tool, ensure that the model gpt-4o-search-preview is used
• Context Inspection: Examine the context state for debugging complex workflows
• Action Tracking: Review tracked actions to understand agent behavior

📄 License

MIT

👥 Contributors

• Agentics Foundation (@agenticsorg)
• rUv (@ruvnet)

Created by the Agentics Foundation