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Documentation Index

Fetch the complete documentation index at: https://docs.moss.dev/llms.txt

Use this file to discover all available pages before exploring further.

Every page in these docs has a Copy page button paste any page directly into Claude, Cursor, ChatGPT, or your preferred AI coding tool to give it full context. We publish an llms.txt file a compact, machine-readable index of all Moss documentation that AI tools can fetch at the start of a session.

llms.txt

Full index of Moss documentation in a format AI tools can parse

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MCP Server

The Moss MCP server lets any MCP compatible client Claude Desktop, Cursor, VS Code call Moss tools directly. No SDK code needed: your AI assistant can create indexes, add documents, and run semantic search queries from within the conversation. 
{
  "mcpServers": {
    "moss": {
      "command": "npx",
      "args": ["-y", "@moss-tools/mcp-server"],
      "env": {
        "MOSS_PROJECT_ID": "your-project-id",
        "MOSS_PROJECT_KEY": "your-project-key"
      }
    }
  }
}
Add this to your client’s config file — ~/Library/Application Support/Claude/claude_desktop_config.json for Claude Desktop, or .cursor/mcp.json for Cursor. Get your credentials from the Moss Portal.

MCP Server setup

Full setup guide with client-specific instructions and available tools

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Starter Prompt for Vibecoding

Paste this into your AI coding tool before starting a Moss project. 
You are helping me build an application that uses Moss for real-time semantic search.

## About Moss
Moss is a semantic search runtime for conversational AI with sub-10ms local queries, instant index
updates, same SDK for browser, on-device, and cloud. No separate search infrastructure needed.

## Documentation
- Quickstart:    https://docs.moss.dev/docs/start/quickstart
- Core concepts: https://docs.moss.dev/docs/start/core-concepts
- SDK reference: https://docs.moss.dev/docs/reference/sdk
- API reference: https://docs.moss.dev/api-reference/v1
- MCP server:    https://docs.moss.dev/docs/integrations/mcp-server
- Full doc index (LLM-friendly): https://docs.moss.dev/llms.txt

## Setup
- JS package: @moss-dev/moss  |  Python package: moss
- Credentials: MOSS_PROJECT_ID and MOSS_PROJECT_KEY from https://portal.usemoss.dev

## Key concepts
- createIndex / loadIndex / query is the core flow — see the quickstart for full examples
- Hybrid search: pass alpha (0.0 = keyword, 1.0 = semantic, default 0.8) to query()
- Mutations (createIndex, addDocs, deleteDocs) are async jobs; SDK polls until completion
- Embedding models: moss-minilm (fast, default), moss-mediumlm (higher accuracy), custom (bring your own vectors)
- Metadata filtering on local indexes: $eq, $ne, $in, $gt, $lt, $and, $or operators

## Common errors
- Unauthorized: missing or wrong MOSS_PROJECT_ID / MOSS_PROJECT_KEY
- Index not found: call createIndex() before loading or querying
- Index not loaded: call loadIndex() before query() — JS falls back to cloud if skipped
- Missing embeddings runtime: use moss-minilm or moss-mediumlm unless supplying custom vectors

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Go deeper

Quickstart

Working index and query in under 5 minutes

Core Concepts

Indexes, documents, embeddings, and hybrid search

MCP Server

Use Moss tools directly from Claude Desktop or Cursor

Voice Agent (LiveKit)

Inject real-time context into a voice agent pipeline