Building Easy-Cass-Mcp: An MCP Server for Cassandra Operations
I’ve started working on a new project that I’d like to share, easy-cass-mcp, an MCP (Model Context Protocol) server specifically designed to assist Apache Cassandra operators.
After spending over a decade optimizing Cassandra clusters in production environments, I’ve seen teams consistently struggle with how to interpret system metrics, configuration settings, schema design, and system configuration, and most importantly, how to understand how they all impact each other. While many teams have solid monitoring through JMX-based collectors, extracting and contextualizing specific operational metrics for troubleshooting or optimization can still be cumbersome. The good news is that we now have the infrastructure to make all this operational knowledge accessible through conversational AI.
What easy-cass-mcp Does
At a high level, this MCP server provides a mechanism for AI agents to directly interact with metrics from your Cassandra cluster without having to set up dashboards or complex monitoring infrastructure. That said, I still strongly recommend setting up proper metrics collection, log aggregation, and dashboards - this is just another tool to add to the toolbox.
The current implementation focuses on three core areas:
Cluster Metrics Access
The MCP server uses Cassandra’s virtual tables to access cluster metrics and operational data. Virtual tables, introduced in 4.0 and expanded in 5.0, expose the same information that teams traditionally pull via JMX, but they’re much easier to query programmatically. Instead of dealing with JMX complexity, the MCP server uses the standard Cassandra driver to extract metrics that used to be a pain to access (especially from a non-JVM language). The server presents this information in a structured format that AI can interpret and explain, making operational insights accessible through natural language queries.
Tuning Recommendations
Based on the metrics collected, the server provides specific recommendations for JVM settings, table configurations, cassandra.yaml. I’m encoding years of production experience into these recommendation engines, focusing on changes that can massively slash operational costs and improve performance.
Developer Assistance (Coming Soon)
The next phase will focus on helping developers work more effectively with Cassandra. This will include guidance on selecting the right driver for their specific language, assistance with creating prepared statements, and the ability to ask complex questions about their schema that they may have previously been unable to answer. This addresses one of the biggest pain points I encounter in consulting engagements - teams that understand their application requirements but struggle to translate them into efficient Cassandra implementations.
Why This Matters
In my experience, most Cassandra performance issues stem from relying on conservative defaults and lack of visibility into system behavior. Organizations often run clusters that are 200-400% over-provisioned simply because they don’t have the tools to understand their actual resource utilization patterns. This approach can save your organization huge sums of cash by providing the insights needed to right-size deployments and optimize configurations.
The MCP protocol creates a standardized way for AI assistants to interact with external systems, which means your operational knowledge becomes accessible through conversational interfaces. Instead of requiring deep Cassandra expertise from every team member, you can democratize access to cluster insights.
Next Steps
I’m actively working on expanding the recommendation engine and adding the developer assistance features. The objective is to create a comprehensive operational assistant that can handle everything from emergency troubleshooting to capacity planning discussions.
The project is still in early stages but is already functional for basic metric collection and analysis. It’s open source and available on GitHub.
I’m excited about the potential this represents for making Cassandra operations more accessible and reducing the expertise barrier that often prevents teams from fully leveraging their clusters’ capabilities.
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