Email Processing MCP Server: Semantic Search and Data Management

Overview: What is Email Processing MCP Server?

The Email Processing MCP Server is a specialized tool designed to integrate email processing capabilities into AI applications through Model Context Protocol (MCP). It leverages modern database technologies—SQLite for efficient local storage and retrieval, and MongoDB for vector embeddings and semantic search—to provide robust and scalable solutions. This server supports multiple mailboxes and can handle various email classifications such as incoming emails from the Inbox, Sent Items, and optionally Deleted Items folders.

🔧 Core Features & MCP Capabilities

The Email Processing MCP Server offers a comprehensive set of features that align with the core capabilities of the Model Context Protocol (MCP):

Email Extraction: It processes emails from Outlook based on specified date ranges.
Data Storage Management: Emails are stored in SQLite databases, ensuring efficient and secure handling.
Embedding Generation: Generates vector embeddings using Ollama for semantic search capabilities.
Multi-Mailbox Support: Supports multiple mailboxes including Inbox, Sent Items, and optionally Deleted Items folders.
Database Integration: Utilizes MongoDB for storing and retrieving vector embeddings based on semantic content.
AI Workflow Enhancement: Integrates seamlessly into AI applications such as Claude Desktop, Continue, and Cursor to enhance their data processing capabilities.

MCP Protocol Implementation Details

The Email Processing MCP Server adheres to the standards defined by Model Context Protocol (MCP), ensuring compatibility and reliability with a range of AI clients. The protocol specifies how data is transmitted between the client application (e.g., Claude Desktop) and the server, enabling seamless integration without requiring any manual configuration or coding.

⚙️ MCP Architecture & Protocol Implementation

The architecture of this MCP Server incorporates both SQLite and MongoDB databases to manage and process email data efficiently:

Data Storage using SQLite

Local Storage: Emails are stored in an optimized SQLite database.
Efficient Retrieval: Implements full-text search capabilities for quick retrieval.
Processing Status Management: Stores processing status metadata to ensure smooth operations.

Embedding Generation with MongoDB

Vector Embeddings: Uses MongoDB to store generated embeddings for semantic similarity searches.
Metadata Filtering: Efficiently retrieves relevant emails based on keywords and metadata.
Connection Management: Ensures proper connection handling to prevent database locking issues.

The server design also includes robust error handling mechanisms, such as automatic retries for embedding generation and database connections. These features enhance the overall reliability of the system in real-world applications.

🚀 Getting Started with Installation

To set up the Email Processing MCP Server, follow these steps:

Install Dependencies: Ensure you have Python 3.10 or higher installed.
Ollama Setup: Install Ollama and pull required models using the following command:
```
ollama pull nomic-embed-text
```

Create Virtual Environment:

Windows:
```
uv venv .venv
```
```
.venv\Scripts\activate
```
macOS/Linux:
```
source .venv/bin/activate
```

Install Dependencies: Install both uv and the package itself using:
```
uv pip install -e .
uv pip install fastmcp
```
Configure MCP Client (Claude Desktop):
- Locate your configuration file:
  - Windows: %APPDATA%\Claude\claude_desktop_config.json
  - macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
- Add the server to your configurations as specified in the README.

💡 Key Use Cases in AI Workflows

Real-World Implementation 1: Email Analysis for Business Intelligence

In a business setting, this MCP Server can be used to periodically analyze incoming emails across multiple mailboxes. For example, an HR department might use it to monitor and categorize job applications or to track the status of candidate communications over time. The system processes these emails into a structured format that can be queried for insights using semantic search.

Real-World Implementation 2: Customer Support Email Analysis

For customer support teams, this MCP Server enables them to efficiently manage incoming inquiries by storing and retrieving relevant email history. By integrating with popular AI chatbots like Claude Desktop, the system can provide instant responses or categorizations based on previous interactions, thereby enhancing customer satisfaction.

🔌 Integration with MCP Clients

Compatibility Matrix

The following table outlines the compatibility matrix between various MCP clients and this Email Processing MCP Server:

MCP Client	Resources	Tools	Prompts	Status
Claude Desktop	✅	✅	✅	Full Support
Continue	✅	✅	✅	Full Support
Cursor	❌	✅	❌	Tools Only

MCP Protocol Flow Diagram

Here is the detailed flow of data across the protocol:

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

📊 Performance & Compatibility Matrix

System Requirements & Performance Metrics

System Requirements: Python 3.10 or higher, Windows OS for Outlook integration.
Performance Metrics:
- Storage Efficiency: SQLite and MongoDB optimize data storage and retrieval.
- Processing Time: Efficient algorithms ensure quick email processing times.

🛠️ Advanced Configuration & Security

Configuration Details

The server configuration is detailed in the README. Key environment variables include MONGODB_URI, SQLITE_DB_PATH, and EMBEDDING_BASE_URL. These must be set correctly to avoid errors during runtime.

Security Notes

Data Privacy: Ensures all sensitive data is handled securely.
Connection Safety: Automatic handling of MongoDB and SQLite connections prevents database locking issues.
Error Handling: Detailed error messages assist users in troubleshooting common issues.

❓ Frequently Asked Questions (FAQ)

Q: How do I install the necessary dependencies?
- A: Follow the installation instructions provided in the README, including setting up Ollama and Python dependencies.
Q: Can this server handle large volumes of emails?
- A: Yes, it is optimized for handling thousands of emails efficiently using SQLite and MongoDB.
Q: How does the server ensure data security?
- A: Data privacy is ensured through secure configuration settings and robust error handling mechanisms.
Q: Are there any known compatibility issues with specific AI applications?
- A: While this server supports Claude Desktop, Continue, and Cursor, it currently lacks full support for Cursor's prompt generation capabilities.
Q: How can I troubleshoot common issues when setting up the server?
- A: Detailed error messages provide insight into common problems such as incorrect date formats or database connection issues.

👨‍💻 Development & Contribution Guidelines

Contributors are welcome to join in extending and improving this MCP Server by submitting pull requests on GitHub. Key areas include enhancing error handling, optimizing queries, and expanding support for new AI clients.

🌐 MCP Ecosystem & Resources

For more information and resources related to Model Context Protocol (MCP), visit the official documentation and community forums.

Mermaid Diagrams

graph LR
    A[AI Application] -->|MCP Client| B[MCP Server]
    B --> C[SQLite Database]
    B --> D[MongoDB Storage]

---

graph TD
    A[Data Request from AI App] --> B[MCP Client Sends Request to MCP Server]
    B --> C[MCP Protocol]
    C --> D[MCP Server Processes Data/Calls External Tools]
    D --> E[Tool/Database Response]
    E --> F[Send Results Back to AI Application via MCP Protocol]

By following these guidelines and utilizing the Email Processing MCP Server, developers can enhance their AI applications with robust email processing capabilities. This tool provides both the technical foundation and user-friendly interface necessary for seamless integration into diverse workflows.

Email Processing MCP Server