aws-setup

name	aws-setup
description	Deploy OpenSearch search applications to Amazon OpenSearch Service or Amazon OpenSearch Serverless. Use this skill when the user wants to provision an OpenSearch domain or serverless collection on AWS, deploy search configurations to AWS, set up Bedrock connectors, configure IAM roles for OpenSearch, migrate a local search setup to AWS, or manage Amazon OpenSearch infrastructure. Activate even if the user says AOS, AOSS, OpenSearch Service, serverless collection, Bedrock connector, SigV4, or AWS deployment without mentioning search.
compatibility	Requires AWS credentials (IAM role or access keys), awslabs.aws-api-mcp-server, and opensearch-mcp-server. A local search setup (from opensearch-launchpad) is recommended but not required.
metadata	{"author":"opensearch-project","version":"2.0"}

OpenSearch 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)
• uv installed (for running helper scripts)
• A search configuration to deploy (typically built with the opensearch-launchpad skill)

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)

1. 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).
2. 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.
3. Validate credentials first — ALWAYS run aws sts get-caller-identity as the first step before any provisioning or deployment operation.
4. 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 not export it globally. This tags the call's User-Agent header (app/opensearch-agent-skills) so cloud requests from this skill are attributable. AWS calls made through awslabs.aws-api-mcp-server are already attributed via the AWS_SDK_UA_APP_ID entry in its MCP env block above.
• Track deployment state in .opensearch-deploy-state.json at 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.