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GeezerVison is showing developer.amazon.com — Ring Appstore MCP Server | Ring Developer Experience Documentation Alexa Amazon Appstore Ring AWS Documentation Support Contact Us My Cases Ring
Ring Appstore MCP ServerThe Ring Appstore Knowledge MCP Server connects your AI coding assistant (Kiro, Cursor, Cline, etc.) to Ring Appstore documentation. Instead of searching through docs on Developer Portal, your AI can instantly access Ring API documentation and answer your integration questions in real-time. What is Ring Appstore MCP?Ring Appstore MCP is a remote Model Context Protocol server backed by an Amazon Bedrock Knowledge Base. When your AI assistant receives a Ring Appstore-related question, it calls the MCP server's tools to search indexed documentation, retrieve full documents, and return accurate answers with source references — all without you leaving your IDE. The knowledge base includes Ring Appstore API references, integration guides, and technical specifications, so you can use it not just to look things up but to actively build apps against Ring Appstore APIs with your AI assistant generating working code from real documentation. Simple Example:
How It Works
Available ToolsThe Ring Appstore MCP server exposes two tools that your AI assistant calls automatically:
|
| Parameter | Type | Default | Description |
|---|---|---|---|
query |
string | (required) | The search query (e.g., "How do I authenticate with AVA APIs?") |
max_results |
string | "5" |
Maximum number of results to return |
min_confidence |
string | "0.2" |
Minimum confidence score threshold (0.0 to 1.0) |
Returns a structured result with:
status — "success", "clarification_needed", or "error"confidence — Overall confidence score across resultsresults[] — Each containing content, score, and metadata.source (file path or URL)followUpQuestions[] — Suggested follow-ups when results are unclearget_doc — Retrieve Full Document ContentFetches the complete content of a specific documentation file or web page. Typically used after search_docs to get the full text of a result.
| Parameter | Type | Description |
|---|---|---|
uri |
string | A file path from search_docs results (e.g., "api/authentication.md") or a Ring documentation URL |
Returns the full document content as a string.
https://knowledge.appstore-mcp.ring.amazon.dev/mcpChoose your IDE and follow the steps:
{
"ring-appstore-knowledge-mcp-server": {
"type": "streamable-http",
"url": "https://knowledge.appstore-mcp.ring.amazon.dev/mcp",
"disabled": false
}
}
{
"mcpServers": {
"ring-appstore-knowledge-mcp-server": {
"type": "streamable-http",
"url": "https://knowledge.appstore-mcp.ring.amazon.dev/mcp",
"disabled": false
}
}
}
~/Library/Application Support/Claude/claude_desktop_config.json%APPDATA%\Claude\claude_desktop_config.json{
"mcpServers": {
"ring-appstore-knowledge-mcp-server": {
"type": "streamable-http",
"url": "https://knowledge.appstore-mcp.ring.amazon.dev/mcp"
}
}
}
Once configured, try asking your AI assistant these questions:
"How do I authenticate with Ring AVA APIs?"
Ring Appstore MCP can help you build entire applications. Try this advanced example:
Use this prompt to build a complete React web app that streams Ring video and detects people in real-time:
Step 1: Initial Setup
Build a minimal React web app (use Vite React template with bun) for calling Ring
Appstore APIs. Set it up for local development with a separate config.js file where
you can manually paste credentials. Install the necessary dependencies but skip any
documentation files. Add a local proxy server to make the app work on localhost.
Use the Ring Appstore MCP server to get Ring Appstore API details.
Step 2: Configure Access Token
[Manual] Paste your access_token into the config file (token value should be
obtained separately).
Step 3: Add Live Streaming
When you click on a device, start a live streaming session and display the video
(canvas or video element). Add play/stop controls. Refer to Ring Appstore API
docs for implementation details.
Step 4: Add Person Detection
Build a real-time person detection app with live counting and historical graph
visualization. Stream video from Amazon Vision APIs into TensorFlow
"@tensorflow-models/coco-ssd" for person detection. Display current person count
and plot historical data using Recharts. No persistence needed. Include logging
throughout.
A working React app that:
https://knowledge.appstore-mcp.ring.amazon.dev/mcp"disabled": false in your configurationSupported devices