A Model Context Protocol (MCP) server for interacting with Astra DB. MCP extends the capabilities of Large Language Models (LLMs) by allowing them to interact with external systems as agents.

You need to have a running Astra DB database. If you don't have one, you can create a free database here. From there, you can get two things you need:

1. An Astra DB Application Token
2. The Astra DB API Endpoint

To learn how to get these, please read the getting started docs.

Here's how you can add this server to your MCP client.

For IBM Bob IDE, follow the procedure at this documentation.

For IBM Bob Shell, follow the procedure at this documentation.

To add this to Claude Desktop, go to Preferences -> Developer -> Edit Config and add this JSON blob to claude_desktop_config.json:

{
  "mcpServers": {
    "astra-db-mcp": {
      "command": "npx",
      "args": ["-y", "@datastax/astra-db-mcp"],
      "env": {
        "ASTRA_DB_APPLICATION_TOKEN": "your_astra_db_token",
        "ASTRA_DB_API_ENDPOINT": "your_astra_db_endpoint"
      }
    }
  }
}

Optional Keyspace Configuration: By default, this server uses the keyspace configured in the underlying Astra DB library (typically default_keyspace). If you need to connect to a specific keyspace, you can add the ASTRA_DB_KEYSPACE variable to the env object above, like so:

"env": {
  "ASTRA_DB_APPLICATION_TOKEN": "your_astra_db_token",
  "ASTRA_DB_API_ENDPOINT": "your_astra_db_endpoint",
  "ASTRA_DB_KEYSPACE": "your_desired_keyspace"
}

Windows PowerShell Users: npx is a batch command so modify the JSON as follows:

  "command": "cmd",
  "args": ["/k", "npx", "-y", "@datastax/astra-db-mcp"],

To add this to Cursor, go to Settings -> Cursor Settings -> MCP

From there, you can add the server by clicking the "+ Add New MCP Server" button, where you should be brought to an mcp.json file.

Tip: there is a ~/.cursor/mcp.json that represents your Global MCP settings, and a project-specific .cursor/mcp.json file that is specific to the project. You probably want to install this MCP server into the project-specific file.

Add the same JSON as indiciated in the Claude Desktop instructions.

Alternatively you may be presented with a wizard, where you can enter the following values (for Unix-based systems):

• Name: Whatever you want
• Type: Command
• Command:

env ASTRA_DB_APPLICATION_TOKEN=your_astra_db_token ASTRA_DB_API_ENDPOINT=your_astra_db_endpoint npx -y @datastax/astra-db-mcp

Note: ASTRA_DB_KEYSPACE is optional. If omitted, the default keyspace configured in the Astra DB library will be used.

Once added, your editor will be fully connected to your Astra DB database.

The server provides a comprehensive suite of tools spanning Collections, Vector Search, Tables, Keyspaces, and Administration:

• GetCollections: Get all collections in the active keyspace
• GetCollectionInfo: Inspect collection options, vector settings, and default ID configuration
• CreateCollection: Create a collection with vector options, distance metrics (cosine, euclidean, dot_product), and auto-vectorize configurations
• UpdateCollection: Update or rename a collection
• DeleteCollection: Delete a collection
• EstimateDocumentCount: Get an approximate count of documents in a collection
• ListRecords: List records with optional sorting, pagination (skip), and projection
• GetRecord: Get a specific record by ID
• CreateRecord: Insert a single document
• UpdateRecord: Update a record
• DeleteRecord: Delete a record by ID
• FindRecord: Find records by exact field value match
• FindWithFilter: Rich querying with MongoDB-style filter operators ($and, $or, $gt, $in, $exists, etc.)
• FindDistinctValues: Find distinct values for a specific field in a collection
• BulkCreateRecords: Insert multiple documents in batch
• BulkUpdateRecords: Update multiple documents in batch
• BulkDeleteRecords: Delete multiple documents in batch

• FindWithVector: Dense vector similarity search with optional metadata filters, similarity scores, and projection
• FindWithVectorize: Natural language search query using Astra DB Vectorize serverless embeddings
• FindAndRerank: Hybrid search (lexical + dense vector / auto-vectorize) with server-side reranking scores
• VectorSearch: Vector similarity search with minScore threshold and projection
• HybridSearch: Combine dense vector similarity and text search with weighted scoring

• ListTables: List all structured tables in the keyspace
• CreateTable: Create a typed table with column definitions and primary keys
• AlterTable: Add or drop columns (including vector columns)
• DropTable: Drop a table
• QueryTable: Query table rows with filters, sorting, projection, and pagination
• InsertTableRow: Insert a single row or batch of rows into a table
• UpdateTableRow: Update matching rows in a table
• DeleteTableRow: Delete matching rows from a table
• CreateTableIndex: Create a secondary index on a table column
• CreateTableVectorIndex: Create a vector index on a table column with similarity metrics

• ListKeyspaces: List all keyspaces in the database
• CreateKeyspace: Create a new keyspace with optional automatic switching
• DropKeyspace: Drop a keyspace
• UseKeyspace: Switch the active working keyspace for subsequent operations
• GetCurrentKeyspace: Inspect the active keyspace name
• ListEmbeddingProviders: Discover supported embedding models and providers
• ListRerankingProviders: Discover supported reranking models
• GetDatabaseInfo: Retrieve environment metadata (ID, region, status, keyspaces)

• OpenBrowser: Open a browser for authentication/setup
• HelpAddToClient: Assistance with MCP client installation

All notable changes to this project will be documented in this file. The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

The evals package loads an mcp client that then runs the index.ts file, so there is no need to rebuild between tests. You can load environment variables by prefixing the npx command. Full documentation can be found here.

OPENAI_API_KEY=your-key  npx mcp-eval evals.ts tools.ts