> For the complete documentation index, see [llms.txt](https://docs.envector.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.envector.io/1.4.x/integrations/mcp/mcp-guide.md). # MCP Guide ## Introduction The [Model Context Protocol (MCP)](https://modelcontextprotocol.io/) 🔌 is a protocol for communicating with LLMs, primarily used for exchanging information (context) or performing actions. 🚀 **enVector** supports using MCP with various AI applications. You can interact with your LLMs like Claude through natural language inputs. This guide supports how to start enVector MCP in details. ## Prerequisites * Python version >= 3.10 * A running enVector instance: [Docker Compose](https://github.com/CryptoLabInc/envector-docs/tree/v1.4.6/gitbook/en/integrations/installation/README.md) or [enVector Cloud](https://envector.io/) * Cloned GitHub Repository of enVector MCP server: > **Note**: The base MCP library requires Python 3.10+ while pyenvector supports 3.9+. ## Supporting tools 1. `get_index_list` : Get the list of indexes in enVector. 2. `get_index_info` : Get information about a specific index in enVector. 3. `create_index` : Create an index in enVector. 4. `insert` : Insert vectors and the corresponding metadata into enVector index. Supports to specify embedding model to get embedding vectors to insert. 5. `search` : Perform vector search and Retrieve Metadata from enVector. Supports to specify embedding model to get embedding vectors to search. ## Getting Started 1. Run enVector server via [Docker Compose](https://github.com/CryptoLabInc/envector-docs/tree/v1.4.6/gitbook/en/integrations/installation/README.md) or [enVector Cloud](https://envector.io/). 2. Clone the [envector-mcp-server](https://github.com/CryptoLabInc/envector-mcp-server) and setting up the Python environment. ```bash python -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` See more details in [envector-mcp-server manuals](https://github.com/CryptoLabInc/envector-mcp-server/blob/main/MANUAL.md). 3. (Optional) Run MCP server with the following example command: ```bash python srcs/server.py \ --envector-address "your-ev-address" \ --envector-cloud-access-token "your-ev-access-token" ``` This is optional when you run MCP server via AI application (e.g. Claude Desktop). See more details in [Run MCP Server](#run-mcp-server). 4. Connect your AI application (e.g. Claude) to the MCP server by configuring settings and Chat with enVector 🚀 ```json "envector-mcp-server": { "url": "http://MCPHOST:MCPPORT/mcp", "description": "enVector MCP Server" } ``` See more details in [Connect MCP Server](#connect-mcp-server). ## Connect MCP Server ### Connect to Remote MCP Server 1. Run MCP server on your Python environment with the following example command: ```bash python srcs/server.py \ --envector-address "your-ev-address" \ --envector-cloud-access-token "your-ev-access-token" ``` See more CLI options on [CLI Options](#cli-options) for running MCP server. 2. Configure your MCP settings `.some-mcp-config.json` in your application. ```json { "mcpServers": { "envector-mcp-server": { "url": "http://MCPHOST:MCPPORT/mcp", "description": "enVector MCP Server" } } } ``` ### Run MCP Server via AI application Some AI applications support to run MCP server on their applications directly, e.g. Claude Desktop. Configure your MCP settings `.some-mcp-config.json` in your application. ```json { "mcpServers": { "enVectorMCP": { "command": ".venv/bin/python3", "args": [ "srcs/server.py", "--envector-address", "your-ev-address", "--envector-cloud-access-token", "your-ev-access-token" ], "cwd": "/path/to/envector-mcp-server", "description": "enVector MCP Server" }, } } ``` ## MCP Server Options ### CLI Options Arguments to run Python scripts: * 💻 MCP execution * `--mode`: MCP execution mode, supporting `http` (default) and `stdio` transports. * `--host`: MCP HTTP bind host. The default is `127.0.0.1`. * `--port`: MCP HTTP bind port. The default is `8000`. * `--address`: MCP HTTP bind address. Overrides `--host` and `--port` if provided. * `--server-name`: MCP server name. The default is `envector_mcp_server`. * 🔌 enVector connection * `--envector-address`: enVector endpoint address (`{host}:{port}` or enVector Cloud endpoint ends with `.clusters.envector.io`). * `--envector-cloud-access-token`: access token of enVector Cloud. * 🔑 enVector options * `--envector-key-id`: enVector key id (identifier). * `--envector-key-path`: path to enVector key files. * `--envector-eval-mode`: enVector FHE evaluation mode. * `--encrypted-query`: whether to encrypt the query vectors. The index is encrypted by default. > ⚠️ **Note**: MCP server holds the key for homomorphic encryption as MCP server is a enVector Client. * ⚙️ Embedding options * `--embedding-mode`: Mode of the embedding model. Supports `femb` (FastEmb; by default), `hf` (huggingface), `sbert` (SBERT; sentence-transformers), and `openai` (OpenAI API). For `openai`, required to set environmental variable `OPENAI_API_KEY`. * `--embedding-model`: Embedding model name to use enVector. The `sentence-transformers/all-MiniLM-L6-v2` set as default, which dimension is 384. ### Use environment variables Copy `.env.example` to `.env` and configure `.env` as you want. ```bash # MCP execution MCP_SERVER_MODE="http" MCP_SERVER_ADDRESS="127.0.0.1:8000" MCP_SERVER_NAME="envector_mcp_server" # enVector connection ENVECTOR_ADDRESS="localhost:50050" ENVECTOR_CLOUD_ACCESS_TOKEN="" # enVector options ENVECTOR_KEY_ID="mcp_key" ENVECTOR_KEY_PATH="./keys" ENVECTOR_EVAL_MODE="mm" ENVECTOR_ENCRYPTED_QUERY="false" # Embedding mode EMBEDDING_MODE="femb" EMBEDDING_MODEL="sentence-transformers/all-MiniLM-L6-v2" ``` The detailed description of each options can be found in [CLI Options](#cli-options). ## Next Steps * [🔌 MCP Integration](/1.4.x/integrations/mcp/integration.md): How to integrate your AI applications * [Claude](/1.4.x/integrations/mcp/integration/mcp-claude.md) * [Cursor](/1.4.x/integrations/mcp/integration/mcp-cursor.md) * [Gemini](/1.4.x/integrations/mcp/integration/mcp-gemini.md) * [Codex](/1.4.x/integrations/mcp/integration/mcp-codex.md) * [Cline](/1.4.x/integrations/mcp/integration/mcp-cline.md) --- # Agent Instructions This documentation is published with GitBook. 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