Operators Developers Contributors Reference
Sidebar updates per section — preview limitation
Apache Cassandra

Cassandra MCP Server

Preview | Unofficial | For review only

The Model Context Protocol (MCP) allows AI coding assistants to interact with external tools and data sources. A Cassandra MCP server gives AI tools direct access to your schema, query execution, and database introspection — enabling AI-assisted database development workflows without copy-pasting schema definitions or query results by hand.

What Is MCP?

MCP is an open protocol for connecting AI assistants to external tools and services. An MCP server exposes a set of named "tools" that AI assistants can call on your behalf during a conversation. The AI describes what it wants to do, calls the appropriate tool, and incorporates the results into its response.

For Cassandra, an MCP server typically exposes tools for:

  • Schema introspection — list keyspaces, describe tables, inspect indexes

  • Query execution — run CQL statements and return results

  • Performance analysis — trace queries, inspect table statistics, check index build status

MCP is supported by Claude Code, Cursor, Windsurf, and other AI development tools that implement the MCP specification.

Setting Up a Cassandra MCP Server

Start from a concrete implementation rather than a generic search. Two examples at the time of writing are the Amazon Keyspaces MCP Server for Amazon Keyspaces workloads and the mcp-cassandra-server open-source project for local Cassandra workflows. Because the ecosystem is changing quickly, prefer the official documentation for the specific server you plan to run rather than relying on generic setup steps alone.

Basic Setup Steps

  1. Install the MCP server package.
    Follow the installation instructions for your chosen implementation. Most are distributed as npm packages, Python packages, or standalone binaries.

  2. Configure the connection to your Cassandra cluster.
    At minimum, provide the contact point host and native transport port. Most implementations also accept keyspace, credentials, and TLS settings.

  3. Register the server with your AI tool.
    AI tools that support MCP read a configuration file listing which servers to start and how to connect to them.

Example Configuration

The following shows a representative MCP server configuration block. Exact field names vary by implementation — consult your MCP server’s documentation.

{
  "mcpServers": {
    "cassandra": {
      "command": "cassandra-mcp-server",
      "args": ["--host", "127.0.0.1", "--port", "9042"],
      "env": {
        "CASSANDRA_KEYSPACE": "my_app"
      }
    }
  }
}

For Claude Code, this configuration block belongs in your .claude/settings.json or the global ~/.claude/settings.json. For Cursor and Windsurf, consult their MCP setup documentation for the correct config file location.

Start with a single keyspace scoped to your development environment rather than connecting to the full cluster. This limits what the AI can see and execute, and makes it easier to reason about what the AI is doing.

Available Tools

A Cassandra MCP server typically exposes tools in three categories.

Schema Introspection

These tools let the AI understand your data model without you having to paste DESCRIBE TABLE output into the conversation.

  • List all keyspaces in the cluster

  • List tables within a keyspace

  • Describe a table — columns, types, primary key definition, clustering order

  • Show the CREATE TABLE statement for a table

  • List indexes on a table, including SAI indexes and their target columns

Query Execution

These tools let the AI run CQL statements and return results directly.

  • Execute SELECT statements and return rows as structured data

  • Execute DML statements — INSERT, UPDATE, DELETE

  • Execute DDL statements — CREATE TABLE, ALTER TABLE, DROP TABLE

  • Run parameterized queries with typed bind variables

DDL and DML execution tools give the AI the ability to modify your schema and data. Grant only the permissions your workflow actually requires. A read-only exploration session does not need INSERT or CREATE TABLE access.

Performance Analysis

These tools surface Cassandra’s built-in observability features to the AI.

  • Query tracing — execute a statement with tracing enabled and return the execution plan, replica coordination events, and per-stage latencies

  • Table statistics — partition count estimates, row estimates, space amplification

  • SAI index status — build progress and whether an index is queryable

Use Cases

AI-Assisted Schema Design

When designing a new table, the AI can inspect your existing schema to check for naming conventions, identify related tables, and validate that its proposed primary key avoids hotspots. Without MCP, you would need to paste DESCRIBE KEYSPACE output into every conversation.

Example prompt: "Design a table to store per-user activity events, following the conventions in my existing schema."

The AI calls the schema introspection tools, reviews your existing tables, and proposes a design consistent with what it finds.

Natural Language to CQL

Translate questions about your data into CQL queries, execute them, and return formatted results — without writing CQL manually.

Example prompt: "Show me the 10 most recent orders for customer ID abc-123."

The AI inspects the schema to find the relevant table and partition key structure, writes the appropriate SELECT statement, executes it, and presents the results.

For exploratory data work on development datasets, natural-language-to-CQL can substantially reduce the time spent learning a new schema. Use it alongside the CQL reference to verify the generated queries are correct.

Automated Query Optimization

The AI can trace a slow query, examine the execution plan, and suggest index additions or schema changes to reduce coordinator overhead.

Example workflow:

  1. Provide the slow query to the AI

  2. The AI executes it with tracing enabled via the MCP server

  3. The AI reads the trace — replica count, coordinator fanout, per-stage latency

  4. The AI inspects existing indexes and suggests whether an SAI index would help

  5. You review and approve before running any CREATE INDEX statement

Data Exploration

Explore an unfamiliar keyspace through natural language rather than iterating through DESCRIBE statements manually. Useful when onboarding to an existing codebase or debugging a schema you did not write.

Security Considerations

An MCP server gives AI tools direct database access. Treat it like any other database client — apply the same access controls, auditing, and principle of least privilege you would apply to an application service account.

Follow these practices to limit exposure:

Use a restricted role

Create a dedicated Cassandra role for MCP with only the permissions your workflow requires. Do not use a superuser or administrative role.

CREATE ROLE mcp_reader
  WITH PASSWORD = 'secure_password'
  AND LOGIN = true;

GRANT SELECT ON KEYSPACE my_app TO mcp_reader;

For workflows that also require DDL or DML, grant only the specific permissions needed:

-- Read and write, but no schema changes
GRANT SELECT ON KEYSPACE my_app TO mcp_writer;
GRANT MODIFY ON KEYSPACE my_app TO mcp_writer;
Never connect to production without access controls

Use MCP servers against development or staging databases. If production access is required, apply the same change-control process you use for any production database operation — do not allow the AI to execute DDL or unrestricted DML against production data.

Restrict DDL permissions to prevent accidental schema changes

A schema change executed by an AI tool is a schema change. If your workflow does not require CREATE TABLE or ALTER TABLE, do not grant CREATE or ALTER permissions to the MCP role.

Use keyspace-level isolation

Configure the MCP server to connect to a specific keyspace rather than the full cluster. This limits schema visibility and query scope to the keyspace you are actively working on.

Audit MCP query logs

Most MCP server implementations log the CQL statements they execute. Review those logs to understand what the AI is running, especially after exploratory sessions where you used natural-language queries.

See CQL Security for Cassandra role and permission management reference.

Related Pages

  • Client Drivers — connect to Cassandra from your application code

  • CQL Security — roles, permissions, and authentication

  • SAI Concepts — how Storage-Attached Indexes work, including the indexes an MCP server can inspect and suggest

  • Application Patterns — broader patterns for building on Cassandra