An MCP-powered assistant that guides you from initial requirements to a running OpenSearch search setup. It collects a sample document, gathers preferences, plans a search architecture, and executes the plan — creating indices, ML models, ingest pipelines, and a local search UI — with optional deployment to Amazon OpenSearch Service or Serverless.
OpenSearch Launchpad Power — Add this power source URL in Kiro to get started.
- Open Kiro
- Go to Powers panel
- Click Add Power and paste:
https://github.com/opensearch-project/opensearch-launchpad/tree/main/kiro/opensearch-launchpad - Kiro reads
POWER.mdand connects the MCP server automatically — no local clone required.
- Python 3.11+ and
uvinstalled - Docker installed and running (Download Docker)
- For AWS deployment (optional): AWS credentials configured — see AWS Setup
OpenSearch Launchpad walks you through five phases to build a production-ready search solution:
| Phase | What happens |
|---|---|
| 1. Sample Document | Provide a sample document (built-in IMDB dataset, local file, URL, existing index, or paste JSON) |
| 2. Preferences | Set your budget, performance priority, query pattern, and deployment preferences |
| 3. Plan | An AI planner designs your search architecture (BM25, semantic, hybrid, or agentic) |
| 4. Execute | Automatically creates OpenSearch indices, ML models, ingest pipelines, and a search UI locally |
| 4.5 Evaluate | (Optional) Evaluate search quality and iterate on the architecture |
| 5. Deploy | (Optional) Deploy to Amazon OpenSearch Service or Amazon OpenSearch Serverless |
After installing the power, try:
"I want to build a semantic search app with 10M docs"
Kiro will guide you through each phase interactively.
Phase 5 deploys your local search solution to AWS. This is optional — Phases 1–4 work entirely locally.
Add these servers to the power's mcp.json configuration in Kiro:
{
"mcpServers": {
"awslabs.aws-api-mcp-server": {
"command": "uvx",
"args": ["awslabs.aws-api-mcp-server@latest"],
"env": { "FASTMCP_LOG_LEVEL": "ERROR" }
},
"aws-docs": {
"command": "uvx",
"args": ["awslabs.aws-documentation-mcp-server@latest"],
"env": { "FASTMCP_LOG_LEVEL": "ERROR" }
},
"opensearch-mcp-server": {
"command": "uvx",
"args": ["opensearch-mcp-server-py@latest"],
"env": { "FASTMCP_LOG_LEVEL": "ERROR" }
}
}
}aws configureOr set environment variables:
export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION="us-east-1"Your AWS user/role needs permissions for:
- OpenSearch Service — create/manage domains and serverless collections
- IAM — create and manage roles for OpenSearch
- Bedrock — invoke models (for semantic and agentic search)
Some MCP clients cannot find uvx or docker from the JSON config environment.
Fix: Locate the full paths and add them to env.PATH in your MCP config:
which uvx # e.g. /Users/you/.local/bin/uvx
which docker # e.g. /usr/local/bin/dockerThen in Kiro: Cmd+Shift+P → Kiro: Open user MCP config (JSON) and update:
See the Developer Guide for local development setup, MCP server internals, tool reference, and the release process.
This project is licensed under the Apache License, Version 2.0. See LICENSE.txt for details.
{ "mcpServers": { "opensearch-launchpad": { "command": "uvx", "args": ["opensearch-launchpad@latest"], "env": { "FASTMCP_LOG_LEVEL": "ERROR", "PATH": "/usr/local/bin:/usr/bin:/bin:/Users/you/.local/bin" } } } }