Astra DB MCP Server

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.

Prerequisites

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:

An Astra DB Application Token
The Astra DB API Endpoint

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

Adding to an MCP client

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

Claude Desktop

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"],

Cursor

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.

Available Tools

The server provides the following tools for interacting with Astra DB:

Collection Management

GetCollections: Get all collections in the database
CreateCollection: Create a new collection in the database (with vector support)
UpdateCollection: Update an existing collection in the database
DeleteCollection: Delete a collection from the database
EstimateDocumentCount: Get estimate of the number of documents in a collection

Record Operations

ListRecords: List records from a collection in the database
GetRecord: Get a specific record from a collection by ID
CreateRecord: Create a new record in a collection
UpdateRecord: Update an existing record in a collection
DeleteRecord: Delete a record from a collection
FindRecord: Find records in a collection by field value
FindDistinctValues: Find distinct values for a specific field in a collection

Bulk Operations

BulkCreateRecords: Create multiple records in a collection at once
BulkUpdateRecords: Update multiple records in a collection at once
BulkDeleteRecords: Delete multiple records from a collection at once

Vector Search

VectorSearch: Perform vector similarity search on vector embeddings
HybridSearch: Combine vector similarity search with text search

Utility

OpenBrowser: Open a web browser for authentication and setup
HelpAddToClient: Get assistance with adding Astra DB client to your MCP client

New Features and Capabilities

Vector Search Capabilities

The Astra DB MCP server now includes powerful vector search capabilities for AI applications:

VectorSearch

Perform similarity search on vector embeddings:

// Example usage
const results = await VectorSearch({
  collectionName: "my_vector_collection",
  queryVector: [0.1, 0.2, 0.3, ...], // Your embedding vector
  limit: 5,                          // Optional: Number of results to return (default: 10)
  minScore: 0.7,                     // Optional: Minimum similarity score threshold
  filter: { category: "article" }    // Optional: Additional filter criteria
});

HybridSearch

Combine vector similarity search with text search for more accurate results:

// Example usage
const results = await HybridSearch({
  collectionName: "my_vector_collection",
  queryVector: [0.1, 0.2, 0.3, ...], // Your embedding vector
  textQuery: "climate change",        // Text query to search for
  weights: {                          // Optional: Weights for hybrid search
    vector: 0.7,                      // Weight for vector similarity (0.0-1.0)
    text: 0.3                         // Weight for text relevance (0.0-1.0)
  },
  limit: 5,                           // Optional: Number of results to return
  fields: ["title", "content"]        // Optional: Fields to search in for text query
});

Enhanced Collection Creation

The CreateCollection tool now supports more vector configuration options:

// Example usage
const result = await CreateCollection({
  collectionName: "my_vector_collection",
  vector: true,                       // Enable vector search
  dimension: 1536,                    // Vector dimension (e.g., 1536 for OpenAI embeddings)
  metric: "cosine"                    // Similarity metric: "cosine", "euclidean", or "dot_product"
});

Finding Distinct Values

The new FindDistinctValues tool allows you to find unique values for a field:

// Example usage
const distinctValues = await FindDistinctValues({
  collectionName: "my_collection",
  field: "category",                  // Field to find distinct values for
  filter: { active: true }            // Optional: Filter to apply
});

Optimized Bulk Operations

Bulk operations now use native batch processing for better performance:

// Example: Bulk create records
const result = await BulkCreateRecords({
  collectionName: "my_collection",
  records: [
    { title: "Record 1", content: "Content 1" },
    { title: "Record 2", content: "Content 2" },
    // ... more records
  ]
});

// Example: Bulk update records
const updateResult = await BulkUpdateRecords({
  collectionName: "my_collection",
  records: [
    { id: "record1", record: { title: "Updated Title 1" } },
    { id: "record2", record: { title: "Updated Title 2" } },
    // ... more records
  ]
});

// Example: Bulk delete records
const deleteResult = await BulkDeleteRecords({
  collectionName: "my_collection",
  recordIds: ["record1", "record2", "record3"]
});

Improved Error Handling

The server now provides more detailed error messages with error codes to help diagnose issues more easily.

Changelog

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.

Running evals

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

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