UBOS Asset Marketplace: Unleash the Power of AI with the Apache CouchDB MCP Server

In today’s data-driven landscape, seamlessly integrating your data sources with powerful AI models is paramount. The UBOS platform understands this need, offering a comprehensive suite of tools to empower businesses with AI-driven insights and automation. One key component in this ecosystem is the Model Context Protocol (MCP) Server, and specifically, the Apache CouchDB MCP Server.

What is an MCP Server and Why Does it Matter?

Before diving into the specifics of the Apache CouchDB MCP Server, it’s crucial to understand the fundamental role of an MCP Server. MCP stands for Model Context Protocol. It’s an emerging standard that acts as a bridge, facilitating communication between Large Language Models (LLMs) like Claude and external data sources. Think of it as a translator, enabling AI models to understand and interact with diverse data formats and APIs.

Without an MCP Server, LLMs operate in a vacuum, limited to their pre-trained knowledge. They lack real-time access to critical business data residing in databases, CRMs, and other systems. An MCP Server unlocks this potential, allowing LLMs to:

• Access Live Data: Retrieve up-to-date information directly from your data sources.
• Perform Complex Queries: Execute sophisticated data analysis tasks using natural language.
• Automate Workflows: Trigger actions and processes based on AI-driven insights.
• Enhance Decision-Making: Provide AI models with the context needed to make informed recommendations.

The Apache CouchDB MCP Server: Connecting AI to Your NoSQL Data

Apache CouchDB is a popular open-source NoSQL database known for its document-oriented approach and scalability. Many organizations rely on CouchDB to store and manage critical business data. The Apache CouchDB MCP Server, available through the UBOS Asset Marketplace, provides a seamless way to connect your CouchDB data to AI models.

This particular MCP Server leverages the CData JDBC Driver for Apache CouchDB. This driver exposes CouchDB data as relational SQL models, enabling standard SQL queries to be executed against your NoSQL data. The MCP Server then wraps this driver, presenting a simplified interface for LLMs to retrieve live information through natural language questions – eliminating the need for complex SQL coding.

Key Features and Benefits:

• Seamless Integration: Connects Apache CouchDB data to AI models like Claude with minimal configuration.
• Natural Language Queries: Enables users to retrieve data using plain English, eliminating the need for SQL expertise.
• Real-Time Data Access: Provides AI models with up-to-date information directly from your CouchDB database.
• Simplified Setup: Streamlined installation process, making it easy to get started.
• Leverages CData JDBC Driver: Utilizes a robust and reliable driver for connecting to Apache CouchDB.
• Read-Only Access (This Version): Ensures data integrity by providing read-only access to your CouchDB data.

Use Cases:

• AI-Powered Reporting: Generate insightful reports from CouchDB data using natural language queries.
• Intelligent Data Exploration: Explore your CouchDB data and uncover hidden patterns and trends using AI.
• Automated Data Validation: Use AI to validate data integrity in your CouchDB database.
• Personalized Customer Experiences: Leverage CouchDB data to personalize customer interactions based on AI-driven insights.
• Enhanced Decision Support: Provide AI models with the context needed to make informed recommendations based on CouchDB data.

Setting Up the Apache CouchDB MCP Server

The following is a detailed step-by-step guide to setting up the Apache CouchDB MCP Server:

Prerequisites:

• Java Development Kit (JDK) installed on your system.
• Apache CouchDB database instance.
• Claude Desktop or another compatible AI client.

Installation Steps:

1. Clone the Repository:

   bash git clone https://github.com/cdatasoftware/apache-couchdb-mcp-server-by-cdata.git cd apache-couchdb-mcp-server-by-cdata

2. Build the Server:

   bash mvn clean install

   This command creates the JAR file: CDataMCP-jar-with-dependencies.jar

3. Download and Install the CData JDBC Driver for Apache CouchDB:

   Download the driver from: https://www.cdata.com/drivers/couchdb/download/jdbc Follow the installation instructions provided by CData.

4. License the CData JDBC Driver:

   • Navigate to the lib folder in the installation directory (e.g., C:Program FilesCDataCData JDBC Driver for Apache CouchDB on Windows or /Applications/CData JDBC Driver for Apache CouchDB/ on Mac/Linux).
   • Run the command: java -jar cdata.jdbc.couchdb.jar --license
   • Enter your name, email, and “TRIAL” (or your license key).

5. Configure the Connection to Apache CouchDB:

   • Run the command: java -jar cdata.jdbc.couchdb.jar to open the Connection String utility.
   • Configure the connection string with the necessary details to connect to your Apache CouchDB instance (e.g., Server, Database, User, Password).
   • Click “Test Connection” to verify the connection.
   • Copy the connection string for use later.

6. Create a .prp file for your JDBC connection (e.g., apache-couchdb.prp):

   This file contains the configuration properties for the MCP Server. Create a text file with the following content, replacing the placeholders with your actual values:

   env Prefix=couchdb ServerName=CDataCouchDB ServerVersion=1.0 DriverPath=/PATH/TO/cdata.jdbc.couchdb.jar DriverClass=cdata.jdbc.couchdb.CouchDBDriver JdbcUrl=jdbc:couchdb:Server=your_couchdb_server;Database=your_database;User=your_user;Password=your_password; Tables=

   • Prefix: A prefix to be used for the tools exposed (e.g., couchdb).
   • ServerName: A name for your server (e.g., CDataCouchDB).
   • ServerVersion: A version for your server (e.g., 1.0).
   • DriverPath: The full path to the JAR file for your JDBC driver (e.g., /Applications/CData JDBC Driver for Apache CouchDB/lib/cdata.jdbc.couchdb.jar).
   • DriverClass: The name of the JDBC Driver Class (e.g., cdata.jdbc.couchdb.CouchDBDriver).
   • JdbcUrl: The JDBC connection string to use with the CData JDBC Driver to connect to your data (copied from the Connection String utility).
   • Tables: Leave blank to access all data, otherwise, you can explicitly declare the tables you wish to create access for.

Integrating with Claude Desktop

1. Create/Modify the Claude Desktop Config File (claude_desktop_config.json):

   Add the new MCP server configuration to the mcpServers section of the config file. The location of this file depends on your operating system:

   • Windows: %APPDATA%Claudeclaude_desktop_config.json
   • Linux/Mac: /Users/{user}/Library/Application Support/Claude/claude_desktop_config.json

   The configuration should look like this:

   { “mcpServers”: { “apache_couchdb”: { “command”: “/PATH/TO/java”, “args”: [ “-jar”, “/PATH/TO/CDataMCP-jar-with-dependencies.jar”, “/PATH/TO/apache-couchdb.prp” ] } } }

   Replace /PATH/TO/java, /PATH/TO/CDataMCP-jar-with-dependencies.jar, and /PATH/TO/apache-couchdb.prp with the actual paths to the respective files.

2. Restart Claude Desktop:

   Fully exit and restart Claude Desktop for the MCP Server to be recognized. You may need to use Task Manager (Windows) or Activity Monitor (Mac) to ensure the application is completely closed.

Running the MCP Server Independently

You can also run the MCP Server directly from the command line using the following command:

bash java -jar /PATH/TO/CDataMCP-jar-with-dependencies.jar /PATH/TO/apache-couchdb.prp

Note: This method uses stdio and is only suitable for clients running on the same machine as the server.

Interacting with the MCP Server

Once configured, you can interact with the MCP Server through your AI client (e.g., Claude Desktop) using natural language. You generally don’t need to call specific tools explicitly. Simply ask questions about your CouchDB data, such as:

• “What are the most common document types in my CouchDB database?”
• “How many documents were created in the last month?”
• “Show me the average size of documents in the ‘users’ database.”

Available Tools

The MCP Server exposes the following tools for interacting with your CouchDB data:

• {servername}_get_tables: Retrieves a list of available tables in the CouchDB data source. Use {servername}_get_columns to list available columns on a table. The output is returned in CSV format.
• {servername}_get_columns: Retrieves a list of columns for a specific table. Use {servername}_get_tables to get a list of available tables. The output is returned in CSV format.
• {servername}_run_query: Executes a SQL SELECT query against your CouchDB data.

Note: Replace {servername} with the name of your MCP Server in the configuration file (e.g., apache_couchdb).

JSON-RPC Request Examples

For scripting purposes, you can use JSON-RPC 2.0 requests to interact with the MCP Server directly. Here are some examples:

apache_couchdb_get_tables

{ “jsonrpc”: “2.0”, “id”: 1, “method”: “tools/call”, “params”: { “name”: “apache_couchdb_get_tables”, “arguments”: {} } }

apache_couchdb_get_columns

{ “jsonrpc”: “2.0”, “id”: 2, “method”: “tools/call”, “params”: { “name”: “apache_couchdb_get_columns”, “arguments”: { “table”: “your_table_name” } } }

apache_couchdb_run_query

{ “jsonrpc”: “2.0”, “id”: 3, “method”: “tools/call”, “params”: { “name”: “apache_couchdb_run_query”, “arguments”: { “sql”: “SELECT * FROM your_table_name WHERE your_condition” } } }

Troubleshooting

• MCP Server Not Visible in Claude Desktop: Ensure Claude Desktop is fully restarted (use Task Manager/Activity Monitor).
• Data Retrieval Issues: Verify your connection configuration and JDBC connection string in the .prp file.
• Connection Problems: Contact the CData Support Team for assistance with data source connectivity.
• MCP Server Issues: Join the CData Community for support and feedback.

UBOS: Your Full-Stack AI Agent Development Platform

The Apache CouchDB MCP Server is just one piece of the puzzle. UBOS provides a comprehensive platform for building and deploying AI Agents across your organization. With UBOS, you can:

• Orchestrate AI Agents: Design and manage complex AI Agent workflows.
• Connect to Enterprise Data: Integrate AI Agents with your existing data sources, including databases, CRMs, and cloud services.
• Build Custom AI Agents: Create tailored AI Agents using your preferred LLMs and models.
• Develop Multi-Agent Systems: Build collaborative AI systems that can tackle complex tasks.

UBOS empowers you to unlock the full potential of AI, transforming your business processes and driving innovation. By seamlessly integrating data sources like Apache CouchDB with powerful AI models, UBOS helps you gain a competitive edge in today’s rapidly evolving landscape.

By leveraging the Apache CouchDB MCP Server within the UBOS platform, businesses can effectively tap into their NoSQL data, enabling AI-driven insights and automation that were previously unattainable. This integration streamlines data access, simplifies querying processes, and ultimately empowers organizations to make more informed decisions based on comprehensive data analysis.

Furthermore, the flexibility of the UBOS platform allows for the creation of custom AI Agents tailored to specific business needs. Whether it’s generating reports, validating data, or personalizing customer experiences, UBOS provides the tools and infrastructure necessary to build and deploy AI solutions that directly address organizational challenges. This level of customization ensures that businesses can maximize the value of their AI investments and achieve measurable results.

In conclusion, the Apache CouchDB MCP Server, when combined with the capabilities of the UBOS platform, represents a significant advancement in the field of AI-driven data analysis. By providing seamless integration, simplifying querying processes, and enabling the creation of custom AI Agents, UBOS empowers businesses to unlock the full potential of their data and gain a competitive edge in today’s rapidly evolving landscape. This integration streamlines data access, simplifies querying processes, and ultimately empowers organizations to make more informed decisions based on comprehensive data analysis.