Glean: MCP Server for Search and Chat Integration

Overview: What is Glean MCP Server?

Glean is an advanced MCP (Model Context Protocol) server that integrates the powerful Glean API, offering native support for two crucial services: Search and Chat functions. This server enables seamless integration of AI applications, such as Claude Desktop, Continue, Cursor, and others, by providing a standardized protocol interface to access these essential functionalities through the Model Context Protocol.

🔧 Core Features & MCP Capabilities

Glean's core capabilities revolve around enhancing AI application workflows with robust search and chat mechanisms. By leveraging Glean's API, this server facilitates direct interaction between AI tools and data sources, ensuring that the integration process is both efficient and user-friendly.

Search Functionality

The Search feature allows users to query a wide range of datasets and retrieve relevant results. These results can include structured data, documents, or any textual information stored in compatible formats. This capability enhances the utility of Glean by making it an ideal tool for applications requiring comprehensive search capabilities.

Chat Functionality

Glean's Chat function enables natural language interaction between users and AI systems. By integrating a sophisticated chatbot, this server supports Q&A sessions that can handle complex queries with high accuracy and efficiency. The chat functionality is designed to provide context-aware responses, making it incredibly versatile for various use cases.

⚙️ MCP Architecture & Protocol Implementation

The Glean MCP Server implements the Model Context Protocol (MCP) framework, ensuring seamless communication between AI applications and data sources. This implementation involves several key components:

• Client Interface: The MCP client acts as a bridge between the AI application and the server, handling request routing and protocol compliance.
• Middleware Processing: A middleware component processes incoming requests from the MCP clients, translating them into appropriate commands or queries for the underlying data sources.
• Database & Data Sources: Glean connects to various databases and external tools, executing search and chat operations and returning results in a structured format.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph LR
    A[Search Query] --> B[Server Processor]
    B -->|Processed Request| C[Data Source/Tool]
    C --> D[Response]
    D --> E[MCP Client]
    E --> F[AI Application]

🚀 Getting Started with Installation

To get started with Glean, follow these steps to build and install the server:

1. Build the Docker Image:

   docker build -t glean-server:latest -f src/glean/Dockerfile .
   

2. Integrate into Your MCP Client Configuration: Add the following configuration to your claude_desktop_config.json file:

   {
     "mcpServers": {
       "glean-server": {
         "command": "docker",
         "args": [
           "run",
           "-i",
           "--rm",
           "-e",
           "GLEAN_API_KEY",
           "-e",
           "GLEAN_DOMAIN",
           "glean-server"
         ],
         "env": {
           "GLEAN_API_KEY": "YOUR_API_KEY_HERE",
           "GLEAN_DOMAIN": "YOUR_DOMAIN_HERE"
         }
       }
     }
   }
   

3. Prepare Your Environment: Ensure that all necessary environment variables, such as GLEAN_API_KEY and GLEAN_DOMAIN, are set correctly.

💡 Key Use Cases in AI Workflows

Use Case 1: Document Retrieval

Imagine a scenario where an AI application needs to retrieve technical documentation from various sources. Glean can be configured as a MCP server enabling fast, accurate retrieval of specific documents based on search queries. For example:

• Technical Support: Rapidly find user manuals or troubleshooting guides.
• Research Teams: Quickly access relevant articles and papers related to ongoing projects.

Use Case 2: Real-time Chat Integration

Glean can facilitate the integration of a chatbot into an AI application, such as a customer service platform. Here’s how it can be implemented:

• Customer Service: Handle chat interactions in real-time, providing quick and accurate answers.
• Tech Support: Offer live assistance to users by automating common issues with intelligent responses.

🔌 Integration with MCP Clients

Glean ensures compatibility with multiple AI applications through its adherence to the Model Context Protocol (MCP). The following table outlines the current integration status for various MCP clients:

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

📊 Performance & Compatibility Matrix

Glean is designed to handle a wide range of data sources and tools, ensuring compatibility across various AI applications. The performance varies based on the type and size of the dataset being queried.

Example:

• Search: Up to 10,000 documents can be returned within milliseconds.
• Chat: Average response time is less than 3 seconds for most queries.

🛠️ Advanced Configuration & Security

Configuring Glean involves setting up environment variables and customizing the API endpoints. Here's an example configuration snippet:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

Security Considerations

Ensure that API keys and sensitive information are securely managed. Use HTTPS when communicating with the server to protect data in transit.

❓ Frequently Asked Questions (FAQ)

Q1: How do I set up Glean for search functionality?

A1: You can build the Docker image using docker build -t glean-server:latest -f src/glean/Dockerfile ., and then integrate it into your MCP client configuration as shown.

Q2: Can Glean be integrated with other AI applications besides Claude Desktop?

A2: Yes, Glean supports integration with Continue and Cursor. However, you may need to adjust the configuration for compatibility with different clients.

Q3: How does Glean handle large datasets during search queries?

A3: Glean employs efficient indexing techniques and caching strategies to ensure that even large datasets can be searched rapidly without significant latency.

Q4: What are the limitations of using Glean for chat operations?

A4: While Glean supports basic chat functionalities, advanced features like context awareness might require additional development or customization based on specific use cases.

Q5: Is Glean compatible with all MCP clients?

A5: Currently, only Claude Desktop, Continue, and Cursor are fully supported. Other clients may have partial support or none at all.

👨‍💻 Development & Contribution Guidelines

Contributions to the Glean project are welcome. To contribute, follow these steps:

1. Clone the Repository: git clone https://github.com/your-repo/glean-server.git
2. Set Up Development Environment: make setup-dev
3. Run Tests: make test
4. Submit Pull Requests: Follow best practices for code submission.

🌐 MCP Ecosystem & Resources

Explore the wider MCP ecosystem and associated resources:

• MCP Documentation: Detailed guides on integrating with different clients.
• Community Forums: Engage with other developers and contribute to ongoing discussions.
• GitHub Repository: Access full source code, issues, and contributions at Glean GitHub.

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This comprehensive documentation highlights Glean's role as an essential MCP server for AI applications. By following the outlined steps and understanding its capabilities, developers can effectively integrate search and chat functionalities into their projects.