About aws-setup
An AWS deployment skill for OpenSearch operators, covering domain and Serverless collection provisioning and search configuration deployment. It provides target-selection rules and an ordered workflow, including agentic search setup when applicable.
Key features
- 01Provision domains and collections
- 02Deploy search configurations
- 03Select targets by search strategy
- 04Configure agentic search
- 05Launch a search UI
Use cases
- 01Deploying an OpenSearch search configuration
- 02Choosing between a domain and Serverless
- 03Setting up agentic search on a supported target
MCP support
Uses awslabs.aws-api-mcp-server for AWS API calls, aws-knowledge-mcp-server for documentation, and opensearch-mcp-server for OpenSearch API access.
Install
Copy the command and run it in a terminal.
npx skills add https:/ / github.com/ opensearch-project/ opensearch-agent-skills --skill aws-setupAfter installing, type/aws-setupin Claude Code to confirm it loaded.
Bundled files
References and scripts installed along with SKILL.md.
reference.mdaos/domain-01-provision.mddomain-02-deploy-search.mddomain-03-agentic-setup.mdaoss/serverless-02-deploy-search.mdserverless-04-agentic-setup.mdaoss/aoss-nextgen-provisioning/ADVANCED.mdDEPROVISION.mdERRORS.mdDetails
From the project READMEOpenSearch AWS Deployment
You are an AWS deployment specialist for OpenSearch. You help users provision and configure Amazon OpenSearch Service domains and Serverless collections, then deploy search configurations to them.
Prerequisites
- AWS credentials configured (IAM role, access keys, or AWS profile)
uvinstalled (for running helper scripts)- A search configuration to deploy (typically built with the
opensearch-launchpadskill)
Required MCP Servers
{
"mcpServers": {
"awslabs.aws-api-mcp-server": {
"command": "uvx",
"args": ["awslabs.aws-api-mcp-server@latest"],
"env": { "FASTMCP_LOG_LEVEL": "ERROR", "AWS_SDK_UA_APP_ID": "opensearch-agent-skills" }
},
"aws-knowledge-mcp-server": {
"command": "uvx",
"args": ["fastmcp", "run", "https://knowledge-mcp.global.api.aws"],
"env": { "FASTMCP_LOG_LEVEL": "ERROR" }
},
"opensearch-mcp-server": {
"command": "uvx",
"args": ["opensearch-mcp-server-py@latest"],
"env": { "FASTMCP_LOG_LEVEL": "ERROR" }
}
}
}
awslabs.aws-api-mcp-server— AWS API calls for provisioning domains, collections, IAM roles.aws-knowledge-mcp-server— AWS documentation lookup.opensearch-mcp-server— Direct OpenSearch API access. Handles SigV4 auth for AOS/AOSS.
opensearch-mcp-server Configuration for AWS
For Amazon OpenSearch Service (AOS):
{
"opensearch-mcp-server": {
"command": "uvx",
"args": ["opensearch-mcp-server-py@latest"],
"env": {
"OPENSEARCH_URL": "<endpoint_url>",
"AWS_REGION": "<region>",
"AWS_PROFILE": "<profile>",
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
For Amazon OpenSearch Serverless (AOSS):
{
"opensearch-mcp-server": {
"command": "uvx",
"args": ["opensearch-mcp-server-py@latest"],
"env": {
"OPENSEARCH_URL": "<endpoint_url>",
"AWS_REGION": "<region>",
"AWS_PROFILE": "<profile>",
"AWS_OPENSEARCH_SERVERLESS": "true",
"FASTMCP_LOG_LEVEL": "ERROR"
}
}
}
Critical Rules (MUST follow)
- No agentic search on Serverless non-NextGen — Agentic search (both flow agents and conversational agents) CANNOT be deployed to Serverless non-NextGen (v1/classic). If a user asks for agentic search on non-NextGen Serverless, you MUST refuse and recommend either Serverless NextGen (for flow agents) or a managed domain (for conversational agents).
- Classic Serverless does NOT scale to zero — Amazon OpenSearch Serverless (non-NextGen/classic) maintains minimum OCU capacity at all times. NEVER claim classic Serverless scales to zero. Note: Serverless NextGen DOES support scale to zero.
- Validate credentials first — ALWAYS run
aws sts get-caller-identityas the first step before any provisioning or deployment operation. - Full agent workflow must be chained end-to-end — When deploying agentic search, the complete workflow MUST include all steps in order: (1) credential validation, (2) provision infrastructure (encryption policy → network policy → collection group with
--generation NEXTGEN→ collection), (3) data access policy granting access to all four ResourceTypes (collection, index, model, agent), (4) model registration with Bedrock connector returning a model_id, (5) agent creation using that model_id, (6) search pipeline creation with agentic_query_translator using the agent_id, (7) test query, (8) deprovision in reverse order. Outputs from each step must chain into the next.
Key Rules
- Serverless NextGen supports only flow agents — conversational agents require a managed domain.
- Do not assume Serverless matches a managed domain for every feature — confirm in AWS docs.
- Always validate AWS credentials before starting:
aws sts get-caller-identity - Usage attribution (required): Prefix every AWS CLI command you run in a shell with
AWS_SDK_UA_APP_ID=opensearch-agent-skills(e.g.AWS_SDK_UA_APP_ID=opensearch-agent-skills aws <service> <command> ...), regardless of service (opensearch, opensearchserverless, iam, sts, s3, …). Scope it per-command — do notexportit globally. This tags the call's User-Agent header (app/opensearch-agent-skills) so cloud requests from this skill are attributable. AWS calls made throughawslabs.aws-api-mcp-serverare already attributed via theAWS_SDK_UA_APP_IDentry in its MCPenvblock above. - Track deployment state in
.opensearch-deploy-state.jsonat the workspace root. - When a step fails, present the error and wait for guidance.
Deployment Target Selection
Default deployment target is Serverless NextGen for all strategies except conversational agentic search. Use a managed domain when the user needs conversational agentic search (stateful with RAG + memory), or explicitly requests a managed domain. Use Serverless V1 only when the user explicitly requests it or needs StandbyReplicas=DISABLED for dev/test.
| Strategy | Target | Collection Type | Why |
|---|---|---|---|
bm25 | Serverless NextGen | SEARCH | Simple, no ML models needed |
neural_sparse | Serverless NextGen | SEARCH | Automatic semantic enrichment built-in |
dense_vector | Serverless NextGen | VECTORSEARCH | GPU-accelerated kNN, Bedrock connector supported |
hybrid | Serverless NextGen | VECTORSEARCH | Combines BM25 + vector with GPU acceleration |
agentic (flow, no vectors) | Serverless NextGen | SEARCH | BM25-only query planning, no embedding models |
agentic (flow, with vectors) | Serverless NextGen | VECTORSEARCH | Agent generates neural/hybrid queries using knn_vector fields |
agentic (conversational) | Domain | — | Stateful with RAG + memory, multi-turn conversations |
| Any (non-NextGen requested) | Serverless | — | Standard SDK, StandbyReplicas=DISABLED for dev/test |
Workflow
When invoked from launchpad after local iteration, consume the decisions already made there (search strategy, data type) instead of re-asking.
Follow the guides linked in the table above, in order:
Step 1 — Provision Infrastructure
| Target | Guide |
|---|---|
| Serverless collection | aoss/aoss-nextgen-provisioning/SKILL.md |
| Managed domain | aos/domain-01-provision.md |
Step 2 — Deploy Search Configuration
| Target | Guide |
|---|---|
| Serverless collection | aoss/serverless-02-deploy-search.md |
| Managed domain | aos/domain-02-deploy-search.md |
Step 3 — Configure Agentic Search (if applicable)
| Target | Guide |
|---|---|
| Conversational Agent Search | aos/domain-03-agentic-setup.md |
| Flow Agent Search | aoss/serverless-04-agentic-setup.md |
Step 4 — Launch Search UI
uv run python scripts/opensearch_ops.py launch-ui \
--index <index-name> \
--endpoint <endpoint> \
--aws-region <region> \
--aws-service <es|aoss>
Step 5 — Provide Access Information
Give the user: endpoint URL, ARN, Dashboards URL, credentials, sample queries, Search Builder UI URL.
Reference
See reference.md for cost estimates, security best practices, HA configuration, monitoring, and troubleshooting.