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MCP AI Chatbot Examples

This section provides practical examples of how to integrate the Tapti Model Content Protocol (MCP) server with AI chatbots to create powerful social media management assistants and tools.

Start with the Overview to understand the key concepts and benefits of integrating MCP with AI chatbots.

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Available Examples

Performance Metrics Analysis

Learn how to build AI assistants that retrieve, analyze, and provide insights about social media performance metrics.

Content Publishing & Scheduling

Implement AI chatbots that help users create, publish, and schedule content across multiple platforms.

Comment Monitoring & Response

Build assistants that monitor, analyze, and help users respond to comments across social platforms.

Audience Demographic Analysis

Create AI tools that analyze audience demographics and provide strategic content recommendations.

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Example Structure

Each example follows a consistent pattern:

  1. Example Conversation - A realistic chat scenario demonstrating the feature
  2. Implementation Details - Explanation of key components and MCP tools needed
  3. Sample Code - JavaScript implementation examples for the core functionality
  4. Prompt Engineering Tips - Guidance for designing effective prompts
  5. Advanced Features - Suggestions for enhancing the basic implementation

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Common Patterns

Across all examples, you’ll notice these common implementation patterns:

  • User account retrieval - Most examples start by getting user information and connected accounts
  • Intent extraction - Identifying what the user wants to accomplish from natural language
  • Multi-step conversations - Breaking complex tasks into manageable dialogue flows
  • Data formatting - Converting API responses into user-friendly natural language
  • Error handling - Gracefully handling missing accounts or permissions

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Getting Started

To start implementing these examples:

  1. Set up the MCP server as described in the MCP Overview
  2. Create an API key with appropriate permissions
  3. Choose an LLM framework (OpenAI, Anthropic, etc.) for your chatbot
  4. Implement the basic patterns from these examples
  5. Customize the functionality to fit your specific use case

These examples use a hypothetical mcpClient.invoke() method for calling MCP tools. In your implementation, you’ll need to adapt this to your specific framework and API client.