Frontapp MCP Server

# Frontapp MCP Integration Plan This document outlines the high-level plan for integrating Frontapp with Large Language Models (LLMs) using the Model Context Protocol (MCP). ## I. Core Concepts - **MCP**: A standardized protocol that enables Large Language Models (LLMs) to interact with external data sources and applications. - **Frontapp**: A customer communication platform. - **MCP Server**: A server that implements the MCP protocol, acting as an intermediary between an LLM and Frontapp. ## II. Goals - Enable LLMs to access and manipulate Frontapp data (e.g., conversations, contacts, tags). - Automate Frontapp workflows using natural language commands. - Enhance Frontapp's functionality with AI-powered features. - Support real-time updates and event-driven automation using webhooks. ## III. Architecture The MCP server acts as a bridge between the LLM and Frontapp's API: ### LLM Interaction - The LLM sends requests to the MCP server in a structured format (defined by the MCP protocol). - Requests may include natural language commands and parameters. ### MCP Server - Receives requests from the LLM. - Receives webhooks from Frontapp. - Authenticates and authorizes the requests and webhooks. - Translates the requests into Frontapp API calls. - Sends requests to the Frontapp API. - Receives responses from the Frontapp API. - Formats the responses according to the MCP protocol. - Sends the formatted responses back to the LLM. - Processes webhooks to update LLM or trigger actions. ### Frontapp API - Provides access to Frontapp data and functionality. - The MCP server interacts with the Frontapp API using appropriate authentication methods (e.g., API keys, OAuth). - Sends webhooks to the MCP server for real-time event notifications. ## IV. Key Components ### API Gateway - Handles incoming requests from the LLM. - Handles incoming webhooks from Frontapp. - Performs authentication and authorization. - Routes requests and webhooks to the appropriate handler. ### Request Handlers - Specific modules responsible for processing different types of requests from the LLM. - Each handler interacts with the Frontapp API to perform the requested action. - Examples: - GetConversationsHandler: Retrieves a list of conversations from Frontapp. - SendMessageHandler: Sends a message to a conversation in Frontapp. - UpdateContactHandler: Updates contact information in Frontapp. - ApplyTagHandler: Applies a tag to a conversation. ### Webhook Handlers - Specific modules responsible for processing different types of webhooks from Frontapp. - Each handler processes the webhook payload and triggers appropriate actions. - Examples: - ConversationCreatedHandler: Handles conversation.created webhooks. - MessageReceivedHandler: Handles message.received webhooks. - ConversationUpdatedHandler: Handles conversation.updated webhooks. ### Frontapp API Client - A module that encapsulates the logic for interacting with the Frontapp API. - Handles API authentication, request formatting, and response parsing. - Provides a consistent interface for request handlers to interact with Frontapp. - Handles webhook subscription and unsubscription. ### Data Models - Define the structure of data exchanged between the LLM, MCP server, and Frontapp. - Ensure data consistency and facilitate data mapping. - Include models for webhook payloads. ### Configuration - Stores configuration settings for the MCP server, such as: - Frontapp API credentials. - Authentication settings. - Webhook subscription settings (events to subscribe to, webhook URL). - Logging levels. - Network settings. ### Logging and Monitoring - Logs server activity, errors, and performance metrics. - Logs webhook events and processing. - Provides insights into server behavior and helps with debugging and troubleshooting. - Can be integrated with monitoring tools for real-time alerts and analysis. ## V. Functionality The MCP server supports a range of functions, including but not limited to: ### Conversations - Retrieve a list of conversations (with filters: inbox, assignee, status, etc.). - Retrieve conversation details (messages, participants, etc.). - Send messages to a conversation. - Add comments to a conversation. - Archive/unarchive conversations. - Assign/unassign conversations. ### Contacts - Retrieve contact details. - Update contact information. - Create new contacts. ### Tags - Retrieve a list of tags. - Apply tags to conversations. - Remove tags from conversations. ### Inboxes - Retrieve a list of inboxes. - Retrieve inbox details. ### Users - Retrieve a list of users (teammates). - Retrieve user details. ### Accounts - Retrieve a list of accounts. - Retrieve account details. - Create new accounts. - Update account information. ### Webhooks - Receive and process webhooks from Frontapp for events such as: - Conversation creation/updates. - Message sending/receiving. - Tag application/removal. - Other relevant events. - Automatically update LLM context based on webhook events. - Trigger automated workflows in response to webhooks. ## VI. Security Considerations ### Authentication - Verify the identity of the LLM making requests to the MCP server. - Use secure authentication mechanisms (e.g., API keys, JWT). - Webhook Authentication: Verify that webhooks are coming from Frontapp using the method Frontapp provides (e.g., signed payloads, shared secrets). ### Authorization - Ensure that the LLM is authorized to perform the requested action on the specified Frontapp resource. - Implement fine-grained access control based on user roles or permissions. ### Data Validation - Validate all incoming data from the LLM to prevent injection attacks and other security vulnerabilities. - Validate data before sending it to the Frontapp API. - Webhook Validation: Validate the integrity and authenticity of webhook payloads. ### Secure Communication - Use HTTPS for all communication between the LLM, MCP server, and Frontapp API to protect data in transit. ### Credential Management - Store Frontapp API credentials securely (e.g., using environment variables, secure configuration files, or a secrets management system). - Avoid hardcoding credentials in the code. - Webhook Secret Management: Store any shared secrets used to verify webhooks securely. ### Rate Limiting - Implement rate limiting to prevent abuse and protect the Frontapp API from being overwhelmed. - Webhook Handling Rate Limiting: Be prepared to handle a high volume of webhooks and prevent them from overwhelming the server. ### Error Handling - Handle errors gracefully and avoid exposing sensitive information in error messages. - Webhook Error Handling: Implement retry mechanisms and error logging for failed webhook processing. ## VII. Implementation Details ### Technology Stack - **Programming Language**: TypeScript - **Framework**: Express.js for handling HTTP requests and webhooks - **MCP SDK**: @modelcontextprotocol/sdk for implementing the MCP protocol - **HTTP Client**: Axios for making requests to the Frontapp API - **Environment Management**: dotenv for managing environment variables - **Logging**: Winston for structured logging ### Project Structure ``` frontapp-mcp/ ├── src/ │ ├── config/ # Configuration files │ ├── api/ # API Gateway implementation │ ├── models/ # Data models │ ├── handlers/ # Request and webhook handlers │ │ ├── requests/ # LLM request handlers │ │ └── webhooks/ # Frontapp webhook handlers │ ├── clients/ # API clients │ │ └── frontapp/ # Frontapp API client │ ├── middleware/ # Middleware components │ ├── utils/ # Utility functions │ └── index.ts # Entry point ├── tests/ # Test files ├── .env.example # Example environment variables ├── tsconfig.json # TypeScript configuration ├── package.json # Project dependencies └── README.md # Project documentation ``` ### Deployment Considerations - Deploy the MCP server to a suitable hosting environment (e.g., cloud platform, on-premises server). - Consider scalability and high availability requirements, especially for handling webhooks. - Webhook Delivery Reliability: Consider the deployment environment's ability to handle reliable webhook delivery (e.g., retries, queueing). ## VIII. Example Workflows ### Request Workflow 1. An end-user in Frontapp uses an LLM-powered feature (e.g., a custom command in a chat interface) to request a list of conversations in a specific inbox. 2. The LLM translates the user's request into an MCP request and sends it to the MCP server. 3. The MCP server receives the request, authenticates it, and routes it to the GetConversationsHandler. 4. The GetConversationsHandler uses the Frontapp API client to call the Frontapp API's "List conversations" endpoint with the specified inbox ID. 5. The Frontapp API returns a list of conversations to the MCP server. 6. The GetConversationsHandler formats the list of conversations according to the MCP protocol. 7. The MCP server sends the formatted response back to the LLM. 8. The LLM processes the response and presents the list of conversations to the end-user in Frontapp. ### Webhook Workflow 1. A conversation is created in Frontapp. 2. Frontapp sends a conversation.created webhook to the MCP server. 3. The MCP server receives the webhook, authenticates it, and routes it to the ConversationCreatedHandler. 4. The ConversationCreatedHandler processes the webhook payload (e.g., extracts conversation ID, participant details). 5. The ConversationCreatedHandler may update the LLM's context with the new conversation information, or trigger an automated workflow (e.g., send a welcome message to the new conversation). ## IX. Future Enhancements ### Advanced Webhook Handling - Filtering webhooks based on specific criteria. - Batching webhook events for more efficient processing. - Using a queueing system to ensure reliable webhook delivery and processing. ### Performance Optimizations - Implement caching to improve performance and reduce the load on the Frontapp API. - Optimize request handling for high-volume operations. ### Advanced Features - Support more complex search queries for conversations, contacts, and other Frontapp data. - Integration with other LLMs: Make the MCP server compatible with other LLMs beyond the initial one. - Plugin System: Allow developers to create plugins to extend the functionality of the MCP server and add support for new Frontapp features. ### Monitoring and Analytics - Implement advanced monitoring and analytics to track usage patterns and performance metrics. - Create dashboards for visualizing MCP server activity and Frontapp integration metrics.