Moss - Real-time Semantic Search for Conversational AI

Expose Moss semantic search and index management as tools for any MCP-compatible client using @moss-tools/mcp-server. Your AI assistant can create indexes, add documents, and run sub-10ms semantic queries without leaving the conversation.

Why use Moss with MCP?

MCP (Model Context Protocol) lets AI clients call external tools in a standardized way. The Moss MCP server gives any compatible client direct access to your knowledge base - no custom code, no API wrappers, no context window stuffing.

Required tools

Moss account with project credentials
Node.js 18+
An MCP-compatible client (Claude Desktop, Cursor, VS Code, etc.)

Integration guide

Get your Moss credentials

Configure your MCP client

Add the Moss MCP server to your client’s configuration. Below are examples for common clients.

Claude Desktop
Cursor
Direct usage

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows):

claude_desktop_config.json

{
  "mcpServers": {
    "moss": {
      "command": "npx",
      "args": ["-y", "@moss-tools/mcp-server"],
      "env": {
        "MOSS_PROJECT_ID": "your-project-id",
        "MOSS_PROJECT_KEY": "your-project-key"
      }
    }
  }
}

Add to your Cursor MCP settings (.cursor/mcp.json):

.cursor/mcp.json

{
  "mcpServers": {
    "moss": {
      "command": "npx",
      "args": ["-y", "@moss-tools/mcp-server"],
      "env": {
        "MOSS_PROJECT_ID": "your-project-id",
        "MOSS_PROJECT_KEY": "your-project-key"
      }
    }
  }
}

Run the server directly from your terminal:

MOSS_PROJECT_ID=your-id MOSS_PROJECT_KEY=your-key npx @moss-tools/mcp-server

Start using Moss from your AI client

Once configured, your AI client can use Moss tools directly. Try prompts like:

“Create a Moss index called ‘product-docs’ with these FAQs…”
“Search my ‘product-docs’ index for return policy information”
“List all my Moss indexes”
“Load the ‘product-docs’ index for faster queries”

Available tools

The MCP server exposes the following tools to your AI client:

Search

Tool	Description
`query`	Semantic search over an index. Returns matching documents ranked by similarity. The index must be loaded with `load_index` first; queries run locally (~1-10ms).
`load_index`	Download an index into memory for fast local querying. Call this before querying an index repeatedly.

Index management

Tool	Description
`create_index`	Create a new index with documents. Supports `moss-minilm` and `moss-mediumlm` embedding models.
`list_indexes`	List all indexes in the project.
`get_index`	Get metadata and status for a specific index.
`delete_index`	Delete an index and all its documents.

Document operations

Tool	Description
`add_docs`	Add documents to an existing index. Supports upsert to update existing documents by ID.
`get_docs`	Retrieve documents from an index. Returns all documents if no IDs are specified.
`delete_docs`	Delete documents from an index by their IDs.

Jobs

Tool	Description
`get_job_status`	Check the status of an async job (e.g., index builds triggered by other SDKs).

Configuration

Environment variables

Variable	Required	Description
`MOSS_PROJECT_ID`	Yes	Your Moss project ID.
`MOSS_PROJECT_KEY`	Yes	Your Moss project key.

You can optionally set MOSS_CLOUD_API_BASE_URL to override the API base URL. Defaults to https://service.usemoss.dev.

Voice & Real-time

AI Frameworks

Developer Tools

MCP Server

Why use Moss with MCP?

Required tools

Integration guide

Available tools

Search

Index management

Document operations

Jobs

Configuration

Environment variables

​Why use Moss with MCP?

​Required tools

​Integration guide

​Available tools

​Search

​Index management

​Document operations

​Jobs

​Configuration

​Environment variables

Why use Moss with MCP?

Required tools

Integration guide

Available tools

Search

Index management

Document operations

Jobs

Configuration

Environment variables