MCP Embedding Storage Server
An MCP server for storing and retrieving information using vector embeddings via the AI Embeddings API.
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:
- Processes content and breaks it into sections
- Generates embeddings for each section
- Stores both the content and embeddings in a database
- 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
# 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:
{
"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 storepath: Unique identifier path for the contenttype(optional): Content type (e.g., 'markdown')source(optional): Source of the contentparentPath(optional): Path of the parent content (if applicable)
search-content
Searches for content using vector similarity.
Parameters:
query: The search querymaxMatches(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 contentcontent: 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:
-
Generate Embeddings (
POST /api/generate-embeddings)- Generates embeddings for content and stores them in the database
- Required parameters:
contentandpath
-
Vector Search (
POST /api/vector-search)- Searches for content based on semantic similarity
- Required parameter:
prompt
Building from Source
# 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