![PyPI](https://img.shields.io/pypi/v/mcp-server-milvus) [![PyPI - Downloads](https://static.pepy.tech/badge/mcp-server-milvus)](https://pepy.tech/project/mcp-server-milvus) ![PyPI - Monthly Downloads](https://static.pepy.tech/badge/mcp-server-milvus/month) # MCP Server for Milvus > The Model Context Protocol (MCP) is an open protocol that enables seamless integration between LLM applications and external data sources and tools. Whether you're building an AI-powered IDE, enhancing a chat interface, or creating custom AI workflows, MCP provides a standardized way to connect LLMs with the context they need. This repository contains a MCP server that provides access to [Milvus](https://milvus.io/) vector database functionality. **Note:** This repository is a fork of https://github.com/zilliztech/mcp-server-milvus.git with various improvements and added support for `uvx` execution, making it easier to run without installation. ![MCP with Milvus](Claude_mcp+1080.gif) ## Prerequisites Before using this MCP server, ensure you have: - Python 3.10 or higher - A running [Milvus](https://milvus.io/) instance (local or remote) - [uv](https://github.com/astral-sh/uv) installed (recommended for running the server) - [npx](https://nodejs.org/en/download/) installed (for testing with MCP Inspector) ## Usage The recommended way to use this MCP server is to run it directly with `uvx` without installation. This is how both Claude Desktop and Cursor are configured to use it in the examples below. ### Running the Server You can run the MCP server for Milvus using the following command, replacing `http://localhost:19530` with your Milvus server URI: ```bash uvx mcp-server-milvus --milvus-uri http://localhost:19530 ``` Alternatively, you can set environment variables using a `.env` file in your project directory and then run the server using the following `uvx` command: ```bash # Create a .env file with your Milvus configuration cat > .env < `Features` > `MCP` 2. Click on the `+ Add New MCP Server` button 3. Fill out the form: - **Type**: Select `stdio` (since you're running a command) - **Name**: `milvus` - **Command**: `uvx mcp-server-milvus@latest --milvus-uri http://127.0.0.1:19530` > ⚠️ Note: Use `127.0.0.1` instead of `localhost` to avoid potential DNS resolution issues. ### Option 2: Using Project-specific Configuration (Recommended) Create a `.cursor/mcp.json` file in your project root: 1. Create the `.cursor` directory in your project root: ```bash mkdir -p /path/to/your/project/.cursor ``` 2. Create a `mcp.json` file with the following content: ```json { "mcpServers": { "milvus": { "command": "uvx", "args": [ "mcp-server-milvus@latest", "--milvus-uri", "http://127.0.0.1:19530" ] } } } ``` #### For SSE Mode: 1. Start the service by running the following command: ```bash uv run mcp-server-milvus --sse --milvus-uri http://your_sse_host ``` > **Note**: Replace `http://your_sse_host` with your actual SSE host address. 2. Once the service is up and running, overwrite the `mcp.json` file with the following content: ```json { "mcpServers": { "milvus-sse": { "url": "http://your_sse_host:port/sse", "disabled": false, "autoApprove": [] } } } ``` ### Completing the Integration After completing the above steps, restart Cursor or reload the window to ensure the configuration takes effect. ## Verifying the Integration To verify that Cursor has successfully integrated with your Milvus MCP server: 1. Open `Cursor Settings` > `MCP` 2. Check if "milvus" or "milvus-sse" appear in the list(depending on the mode you have chosen) 3. Confirm that the relevant tools are listed (e.g., milvus_list_collections, milvus_vector_search, etc.) 4. If the server is enabled but shows an error, check the Troubleshooting section below ## Available Tools The server provides the following tools: ### Search and Query Operations - `milvus_text_search`: Search for documents using full text search - Parameters: - `collection_name`: Name of collection to search - `query_text`: Text to search for - `limit`: The maximum number of results to return (default: 5) - `output_fields`: Fields to include in results - `drop_ratio`: Proportion of low-frequency terms to ignore (0.0-1.0) - `milvus_vector_search`: Perform vector similarity search on a collection - Parameters: - `collection_name`: Name of collection to search - `vector`: Query vector - `vector_field`: Field name for vector search (default: "vector") - `limit`: The maximum number of results to return (default: 5) - `output_fields`: Fields to include in results - `filter_expr`: Filter expression - `metric_type`: Distance metric (COSINE, L2, IP) (default: "COSINE") - `milvus_hybrid_search`: Perform hybrid search on a collection - Parameters: - `collection_name`: Name of collection to search - `query_text`: Text query for search - `text_field`: Field name for text search - `vector`: Vector of the text query - `vector_field`: Field name for vector search - `limit`: The maximum number of results to return - `output_fields`: Fields to include in results - `filter_expr`: Filter expression - `milvus_query`: Query collection using filter expressions - Parameters: - `collection_name`: Name of collection to query - `filter_expr`: Filter expression (e.g. 'age > 20') - `output_fields`: Fields to include in results - `limit`: The maximum number of results to return (default: 10) ### Collection Management - `milvus_list_collections`: List all collections in the database - `milvus_create_collection`: Create a new collection with specified schema - Parameters: - `collection_name`: Name for the new collection - `collection_schema`: Collection schema definition - `index_params`: Optional index parameters - `milvus_load_collection`: Load a collection into memory for search and query - Parameters: - `collection_name`: Name of collection to load - `replica_number`: Number of replicas (default: 1) - `milvus_release_collection`: Release a collection from memory - Parameters: - `collection_name`: Name of collection to release - `milvus_get_collection_info`: Lists detailed information like schema, properties, collection ID, and other metadata of a specific collection. - Parameters: - `collection_name`: Name of the collection to get detailed information about ### Data Operations - `milvus_insert_data`: Insert data into a collection - Parameters: - `collection_name`: Name of collection - `data`: Dictionary mapping field names to lists of values - `milvus_delete_entities`: Delete entities from a collection based on filter expression - Parameters: - `collection_name`: Name of collection - `filter_expr`: Filter expression to select entities to delete ## Environment Variables - `MILVUS_URI`: Milvus server URI (can be set instead of --milvus-uri) - `MILVUS_TOKEN`: Optional authentication token - `MILVUS_DB`: Database name (defaults to "default") ## Development ### Testing During Development To test the server locally using the [MCP Inspector](https://github.com/modelcontextprotocol/inspector): ```bash npx @modelcontextprotocol/inspector uvx mcp-server-milvus@latest ``` ## Examples ### Using Claude Desktop #### Example 1: Listing Collections ``` What are the collections I have in my Milvus DB? ``` Claude will then use MCP to check this information on your Milvus DB. ``` I'll check what collections are available in your Milvus database. Here are the collections in your Milvus database: 1. rag_demo 2. test 3. chat_messages 4. text_collection 5. image_collection 6. customized_setup 7. streaming_rag_demo ``` #### Example 2: Searching for Documents ``` Find documents in my text_collection that mention "machine learning" ``` Claude will use the full-text search capabilities of Milvus to find relevant documents: ``` I'll search for documents about machine learning in your text_collection. > View result from milvus-text-search from milvus (local) Here are the documents I found that mention machine learning: [Results will appear here based on your actual data] ``` ### Using Cursor #### Example: Creating a Collection In Cursor, you can ask: ``` Create a new collection called 'articles' in Milvus with fields for title (string), content (string), and a vector field (128 dimensions) ``` Cursor will use the MCP server to execute this operation: ``` I'll create a new collection called 'articles' with the specified fields. Collection 'articles' has been created successfully with the following schema: - title: string - content: string - vector: float vector[128] ``` ## Troubleshooting ### Common Issues #### Connection Errors If you see errors like "Failed to connect to Milvus server": 1. Verify your Milvus instance is running: `docker ps` (if using Docker) 2. Check the URI is correct in your configuration 3. Ensure there are no firewall rules blocking the connection 4. Try using `127.0.0.1` instead of `localhost` in the URI #### Authentication Issues If you see authentication errors: 1. Verify your `MILVUS_TOKEN` is correct 2. Check if your Milvus instance requires authentication 3. Ensure you have the correct permissions for the operations you're trying to perform #### Tool Not Found If the MCP tools don't appear in Claude Desktop or Cursor: 1. Restart the application 2. Check the server logs for any errors 3. Verify the MCP server is running correctly 4. Press the refresh button in the MCP settings (for Cursor) ### Getting Help If you continue to experience issues: 1. Check the [GitHub Issues](https://github.com/zilliztech/mcp-server-milvus/issues) for similar problems 2. Join the [Zilliz Community Discord](https://discord.gg/zilliz) for support 3. File a new issue with detailed information about your problem