RAG Context MCP server

The RAG Context MCP Server provides persistent memory and context management for AI assistants using local vector storage and database. It enables efficient storage and retrieval of contextual information through semantic search and indexed retrieval, all while keeping your data private and local.

Installation

Using npm

npm install -g @rag-context/mcp-server

Using npx (no installation required)

npx @rag-context/mcp-server

Configuration

For Claude Desktop

Add the following to your Claude Desktop configuration file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "rag-context": {
      "command": "npx",
      "args": ["@rag-context/mcp-server"],
      "env": {
        "RAG_CONTEXT_DATA_DIR": "/path/to/your/data/directory"
      }
    }
  }
}

For Cursor

In Cursor settings, add the MCP server:

env RAG_CONTEXT_DATA_DIR=/path/to/your/data/directory npx @rag-context/mcp-server

Environment Variables

• RAG_CONTEXT_DATA_DIR: Directory where the database and vector index will be stored (default: ~/.rag-context-mcp)

Usage

The server provides two main tools:

setContext

Store information in memory with automatic vectorization:

{
  "tool": "setContext",
  "arguments": {
    "key": "user_preferences",
    "content": "The user prefers dark mode and uses VS Code as their primary editor",
    "metadata": {
      "category": "preferences",
      "timestamp": "2024-01-15"
    }
  }
}

getContext

Retrieve relevant context using semantic search:

{
  "tool": "getContext",
  "arguments": {
    "query": "What are the user's editor preferences?",
    "limit": 5,
    "threshold": 0.7
  }
}

System Prompt for AI Assistants

To effectively use this MCP server, add the following to your AI assistant's system prompt:

Memory and Context Management

You have access to a persistent memory system through the RAG Context MCP server. This allows you to store and retrieve information across conversations.

When to Store Context

Store information when:

• Users share preferences, settings, or personal information
• Important project details or configurations are discussed
• Key decisions or agreements are made
• Useful code snippets or solutions are created
• Learning about user's workflow, tools, or environment

How to Store Context

Use the setContext tool with:

• A descriptive, unique key (e.g., "project_setup_nextjs", "user_pref_editor")
• Clear, concise content that captures the essential information
• Relevant metadata (category, project, date, etc.)

Example:

{
  "key": "project_api_structure",
  "content": "The project uses a REST API with /api/v1 prefix. Authentication is handled via JWT tokens in the Authorization header. Main endpoints: /users, /posts, /comments",
  "metadata": {
    "project": "blog-platform",
    "type": "architecture",
    "date": "2024-01-15"
  }
}

When to Retrieve Context

Retrieve context when:

• Starting a new conversation about a previously discussed topic
• Users reference past discussions or decisions
• You need to recall specific technical details or preferences
• Building upon previous work or solutions

How to Retrieve Context

Use the getContext tool with:

• A natural language query describing what you're looking for
• Appropriate limit (usually 3-5 results)
• Threshold of 0.7 for balanced precision/recall

Example:

{
  "query": "API authentication setup for the blog project",
  "limit": 3,
  "threshold": 0.7
}

Troubleshooting

Common Issues

1. "VectorStore not initialized" error

   • Ensure the data directory exists and has write permissions
   • Check that the RAG_CONTEXT_DATA_DIR path is valid

2. Slow first startup

   • The embedding model is downloaded on first use (~30MB)
   • Subsequent starts will be much faster

3. High memory usage

   • The embedding model requires ~200MB RAM
   • Consider limiting the number of stored contexts

How to install this MCP server

For Claude Code

To add this MCP server to Claude Code, run this command in your terminal:

claude mcp add-json "rag-context" '{"command":"npx","args":["@rag-context/mcp-server"],"env":{"RAG_CONTEXT_DATA_DIR":"/path/to/your/data/directory"}}'

See the official Claude Code MCP documentation for more details.

For Cursor

There are two ways to add an MCP server to Cursor. The most common way is to add the server globally in the ~/.cursor/mcp.json file so that it is available in all of your projects.

If you only need the server in a single project, you can add it to the project instead by creating or adding it to the .cursor/mcp.json file.

Adding an MCP server to Cursor globally

To add a global MCP server go to Cursor Settings > Tools & Integrations and click "New MCP Server".

When you click that button the ~/.cursor/mcp.json file will be opened and you can add your server like this:

{
    "mcpServers": {
        "rag-context": {
            "command": "npx",
            "args": [
                "@rag-context/mcp-server"
            ],
            "env": {
                "RAG_CONTEXT_DATA_DIR": "/path/to/your/data/directory"
            }
        }
    }
}

Adding an MCP server to a project

To add an MCP server to a project you can create a new .cursor/mcp.json file or add it to the existing one. This will look exactly the same as the global MCP server example above.

How to use the MCP server

Once the server is installed, you might need to head back to Settings > MCP and click the refresh button.

The Cursor agent will then be able to see the available tools the added MCP server has available and will call them when it needs to.

You can also explicitly ask the agent to use the tool by mentioning the tool name and describing what the function does.

For Claude Desktop

To add this MCP server to Claude Desktop:

1. Find your configuration file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json
• Linux: ~/.config/Claude/claude_desktop_config.json

2. Add this to your configuration file:

{
    "mcpServers": {
        "rag-context": {
            "command": "npx",
            "args": [
                "@rag-context/mcp-server"
            ],
            "env": {
                "RAG_CONTEXT_DATA_DIR": "/path/to/your/data/directory"
            }
        }
    }
}

3. Restart Claude Desktop for the changes to take effect