MCP Embedding Storage Server

# MCP Embedding Storage Server An MCP server for storing and retrieving information using vector embeddings via the [AI Embeddings API](https://ai-embeddings.vercel.app/). ## Features - Store content with automatically generated embeddings - Search content using semantic similarity - Access content through both tools and resources - Use pre-defined prompts for common operations ## How It Works This MCP server connects to the AI Embeddings API, which: 1. Processes content and breaks it into sections 2. Generates embeddings for each section 3. Stores both the content and embeddings in a database 4. Enables semantic search using vector similarity When you search, the API finds the most relevant sections of stored content based on the semantic similarity of your query to the stored embeddings. ## Installation ```bash # Install with npm npm install -g mcp-embedding-storage # Or with pnpm pnpm add -g mcp-embedding-storage # Or with yarn yarn global add mcp-embedding-storage ``` ## Usage with Claude for Desktop Add the following configuration to your `claude_desktop_config.json` file: ```json { "mcpServers": { "embedding-storage": { "command": "mcp-embedding-storage" } } } ``` Then restart Claude for Desktop to connect to the server. ## Available Tools ### store-content Stores content with automatically generated embeddings. Parameters: - `content`: The content to store - `path`: Unique identifier path for the content - `type` (optional): Content type (e.g., 'markdown') - `source` (optional): Source of the content - `parentPath` (optional): Path of the parent content (if applicable) ### search-content Searches for content using vector similarity. Parameters: - `query`: The search query - `maxMatches` (optional): Maximum number of matches to return ## Available Resources ### search://{query} Resource template for searching content. Example usage: `search://machine learning basics` ## Available Prompts ### store-new-content A prompt to help store new content with embeddings. Parameters: - `path`: Unique identifier path for the content - `content`: The content to store ### search-knowledge A prompt to search for knowledge. Parameters: - `query`: The search query ## API Integration This MCP server integrates with the AI Embeddings API at https://ai-embeddings.vercel.app/ with the following endpoints: 1. **Generate Embeddings** (`POST /api/generate-embeddings`) - Generates embeddings for content and stores them in the database - Required parameters: `content` and `path` 2. **Vector Search** (`POST /api/vector-search`) - Searches for content based on semantic similarity - Required parameter: `prompt` ## Building from Source ```bash # Clone the repository git clone https://github.com/yourusername/mcp-embedding-storage.git cd mcp-embedding-storage # Install dependencies pnpm install # Build the project pnpm run build # Start the server pnpm start ``` ## License MIT