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MCP extends the capabilities of Large Language Models (LLMs) by allowing them to interact with external systems as agents.","maintainers":[{"name":"philnash","email":"philnash@gmail.com"},{"name":"sysadmin-ds","email":"sysadmin@datastax.com"},{"name":"alexleventer","email":"alexleventer@gmail.com"},{"name":"toptobes","email":"kavinpg@gmail.com"},{"name":"crabasa","email":"carter.rabasa@gmail.com"},{"name":"mfortman11","email":"mfortman11@gmail.com"},{"name":"tejasbutatdatastaxsoontobeibm","email":"tejas.kumar@datastax.com"},{"name":"msmygit","email":"madhavan_5k@yahoo.com"},{"name":"stargateio","email":"stargate-eng-team@datastax.com"}],"readme":"# Astra DB MCP Server\n\nA 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.\n\n## Prerequisites\n\nYou need to have a running Astra DB database. If you don't have one, you can create a free database [here](https://astra.datastax.com/register). From there, you can get two things you need:\n\n1. An Astra DB Application Token\n2. The Astra DB API Endpoint\n\nTo learn how to get these, please [read the getting started docs](https://docs.datastax.com/en/astra-db-serverless/api-reference/dataapiclient.html#set-environment-variables).\n\n## Adding to an MCP client\n\nHere's how you can add this server to your MCP client.\n\n### Claude Desktop\n\n![Claude Desktop](https://github.com/datastax/astra-db-mcp/raw/main/docs/img/claude-settings.png)\n\nTo add this to [Claude Desktop](https://claude.ai/download), go to Preferences -> Developer -> Edit Config and add this JSON blob to `claude_desktop_config.json`:\n\n```json\n{\n  \"mcpServers\": {\n    \"astra-db-mcp\": {\n      \"command\": \"npx\",\n      \"args\": [\"-y\", \"@datastax/astra-db-mcp\"],\n      \"env\": {\n        \"ASTRA_DB_APPLICATION_TOKEN\": \"your_astra_db_token\",\n        \"ASTRA_DB_API_ENDPOINT\": \"your_astra_db_endpoint\"\n      }\n    }\n  }\n}\n```\n\n**Optional Keyspace Configuration:**\nBy 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:\n\n```json\n\"env\": {\n  \"ASTRA_DB_APPLICATION_TOKEN\": \"your_astra_db_token\",\n  \"ASTRA_DB_API_ENDPOINT\": \"your_astra_db_endpoint\",\n  \"ASTRA_DB_KEYSPACE\": \"your_desired_keyspace\"\n}\n```\n\n**Windows PowerShell Users:**\n`npx` is a batch command so modify the JSON as follows:\n\n```json\n  \"command\": \"cmd\",\n  \"args\": [\"/k\", \"npx\", \"-y\", \"@datastax/astra-db-mcp\"],\n```\n\n### Cursor\n\n![Cursor](https://github.com/datastax/astra-db-mcp/raw/main/docs/img/cursor-settings.png)\n\nTo add this to [Cursor](https://www.cursor.com/), go to Settings -> Cursor Settings -> MCP\n\nFrom there, you can add the server by clicking the \"+ Add New MCP Server\" button, where you should be brought to an `mcp.json` file.\n\n> **Tip**: there is a `~/.cursor/mcp.json` that represents your Global MCP settings, and a project-specific `.cursor/mcp.json` file\n> that is specific to the project. You probably want to install this MCP server into the project-specific file.\n\nAdd the same JSON as indiciated in the Claude Desktop instructions.\n\nAlternatively you may be presented with a wizard, where you can enter the following values (for Unix-based systems):\n\n- Name: Whatever you want\n- Type: Command\n- Command:\n\n```sh\nenv ASTRA_DB_APPLICATION_TOKEN=your_astra_db_token ASTRA_DB_API_ENDPOINT=your_astra_db_endpoint npx -y @datastax/astra-db-mcp\n```\n\n*Note: `ASTRA_DB_KEYSPACE` is optional. If omitted, the default keyspace configured in the Astra DB library will be used.*\n\nOnce added, your editor will be fully connected to your Astra DB database.\n\n## Available Tools\n\nThe server provides the following tools for interacting with Astra DB:\n\n### Collection Management\n- `GetCollections`: Get all collections in the database\n- `CreateCollection`: Create a new collection in the database (with vector support)\n- `UpdateCollection`: Update an existing collection in the database\n- `DeleteCollection`: Delete a collection from the database\n- `EstimateDocumentCount`: Get estimate of the number of documents in a collection\n\n### Record Operations\n- `ListRecords`: List records from a collection in the database\n- `GetRecord`: Get a specific record from a collection by ID\n- `CreateRecord`: Create a new record in a collection\n- `UpdateRecord`: Update an existing record in a collection\n- `DeleteRecord`: Delete a record from a collection\n- `FindRecord`: Find records in a collection by field value\n- `FindDistinctValues`: Find distinct values for a specific field in a collection\n\n### Bulk Operations\n- `BulkCreateRecords`: Create multiple records in a collection at once\n- `BulkUpdateRecords`: Update multiple records in a collection at once\n- `BulkDeleteRecords`: Delete multiple records from a collection at once\n\n### Vector Search\n- `VectorSearch`: Perform vector similarity search on vector embeddings\n- `HybridSearch`: Combine vector similarity search with text search\n\n### Utility\n- `OpenBrowser`: Open a web browser for authentication and setup\n- `HelpAddToClient`: Get assistance with adding Astra DB client to your MCP client\n## New Features and Capabilities\n\n### Vector Search Capabilities\n\nThe Astra DB MCP server now includes powerful vector search capabilities for AI applications:\n\n#### VectorSearch\n\nPerform similarity search on vector embeddings:\n\n```javascript\n// Example usage\nconst results = await VectorSearch({\n  collectionName: \"my_vector_collection\",\n  queryVector: [0.1, 0.2, 0.3, ...], // Your embedding vector\n  limit: 5,                          // Optional: Number of results to return (default: 10)\n  minScore: 0.7,                     // Optional: Minimum similarity score threshold\n  filter: { category: \"article\" }    // Optional: Additional filter criteria\n});\n```\n\n#### HybridSearch\n\nCombine vector similarity search with text search for more accurate results:\n\n```javascript\n// Example usage\nconst results = await HybridSearch({\n  collectionName: \"my_vector_collection\",\n  queryVector: [0.1, 0.2, 0.3, ...], // Your embedding vector\n  textQuery: \"climate change\",        // Text query to search for\n  weights: {                          // Optional: Weights for hybrid search\n    vector: 0.7,                      // Weight for vector similarity (0.0-1.0)\n    text: 0.3                         // Weight for text relevance (0.0-1.0)\n  },\n  limit: 5,                           // Optional: Number of results to return\n  fields: [\"title\", \"content\"]        // Optional: Fields to search in for text query\n});\n```\n\n### Enhanced Collection Creation\n\nThe `CreateCollection` tool now supports more vector configuration options:\n\n```javascript\n// Example usage\nconst result = await CreateCollection({\n  collectionName: \"my_vector_collection\",\n  vector: true,                       // Enable vector search\n  dimension: 1536,                    // Vector dimension (e.g., 1536 for OpenAI embeddings)\n  metric: \"cosine\"                    // Similarity metric: \"cosine\", \"euclidean\", or \"dot_product\"\n});\n```\n\n### Finding Distinct Values\n\nThe new `FindDistinctValues` tool allows you to find unique values for a field:\n\n```javascript\n// Example usage\nconst distinctValues = await FindDistinctValues({\n  collectionName: \"my_collection\",\n  field: \"category\",                  // Field to find distinct values for\n  filter: { active: true }            // Optional: Filter to apply\n});\n```\n\n### Optimized Bulk Operations\n\nBulk operations now use native batch processing for better performance:\n\n```javascript\n// Example: Bulk create records\nconst result = await BulkCreateRecords({\n  collectionName: \"my_collection\",\n  records: [\n    { title: \"Record 1\", content: \"Content 1\" },\n    { title: \"Record 2\", content: \"Content 2\" },\n    // ... more records\n  ]\n});\n\n// Example: Bulk update records\nconst updateResult = await BulkUpdateRecords({\n  collectionName: \"my_collection\",\n  records: [\n    { id: \"record1\", record: { title: \"Updated Title 1\" } },\n    { id: \"record2\", record: { title: \"Updated Title 2\" } },\n    // ... more records\n  ]\n});\n\n// Example: Bulk delete records\nconst deleteResult = await BulkDeleteRecords({\n  collectionName: \"my_collection\",\n  recordIds: [\"record1\", \"record2\", \"record3\"]\n});\n```\n\n### Improved Error Handling\n\nThe server now provides more detailed error messages with error codes to help diagnose issues more easily.\n\n## Changelog\nAll notable changes to this project will be documented in [this file](./CHANGELOG.md).\nThe format is based on [Keep a Changelog](https://keepachangelog.com), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).\n\n\n\n## Running evals\n\nThe 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](https://www.mcpevals.io/docs).\n\n```bash\nOPENAI_API_KEY=your-key  npx mcp-eval evals.ts tools.ts\n```\n## ❤️ Contributors\n\n[![astra-db-mcp contributors](https://contrib.rocks/image?repo=datastax/astra-db-mcp)](https://github.com/datastax/astra-db-mcp/graphs/contributors)\n\n## Badges\n[![Astra DB MCP Server on Glama.ai](https://glama.ai/mcp/servers/tigix0yf4b/badge)](https://glama.ai/mcp/servers/tigix0yf4b)\n\n[![MseeP.ai Security Assessment](https://mseep.net/pr/datastax-astra-db-mcp-badge.png)](https://mseep.ai/app/datastax-astra-db-mcp)\n\n[![Verified on MseeP](https://mseep.ai/badge.svg)](https://mseep.ai/app/932eb437-ab8e-4cf4-bbb5-1b3dbdb9f0aa)\n---","readmeFilename":"README.md"}