Amazon Documentdb

Amazon DocumentDB Toolkit

Overview

End-to-end DocumentDB toolkit covering seven workflows: connection (serverless-default cluster setup, TLS, VPC, driver config), schema design (embed-vs-reference, indexes, vector search for RAG), compatibility assessment (MongoDB -> DocumentDB), migration (DMS full-load + CDC + cutover), performance tuning (explain, COLLSCAN, anti-patterns), Well-Architected review (41 checks across 6 pillars), and major version upgrade (4.0->5.0, 5.0->8.0 in-place or near-zero-downtime).

The skill acts as an executor — it runs AWS CLI commands, DMS tasks, index tools, and explain() against the user's cluster rather than just advising. Each workflow produces concrete artifacts under artifacts/{app-name}/.

The AWS MCP server is recommended for executing AWS commands via its call_aws tool (sandboxed execution, audit logging), but it is not required — when the MCP server is not available, the same aws ... CLI commands run via shell.

Decision Guide

Pipeline order: connection -> schema-advisor for green-field; compatibility -> migration for MongoDB migrations; upgrade, well-architected, and performance are standalone.

Out-of-scope: DocumentDB Elastic Clusters (sharded horizontal scaling — not at feature parity with instance-based; lacks transactions, change streams, and many operators — steer customers to instance-based serverless or provisioned instead), Global Clusters DR orchestration beyond the upgrade path. Answer from general knowledge, note no bundled workflow covers them.

Cluster Creation Default: Serverless on 8.0

DocumentDB architecture primer (clarify this whenever the user is confused):

• Serverless = db.serverless as the instance class on a normal instance-based DocumentDB cluster. Auto-scales capacity, no instance-class decisions, costs up to 90% less when idle. This is the recommended default for most workloads.
• Instance-based = fixed instance class (db.r8g.large, db.r6g.xlarge, etc.). Use when the workload is sustained 24/7 high throughput and serverless scaling overhead is unacceptable.
• Elastic Clusters = a separate DocumentDB product for horizontal sharding. NOT the same as serverless. Elastic Clusters are not at feature parity with instance-based clusters — they lack support for transactions, change streams, and many aggregation operators. Steer customers away from Elastic Clusters unless they have a sharding requirement that exhausts even the largest instance-based options. Almost all workloads can be served by serverless or instance-based given DocumentDB's wide range of instance classes.

When creating any new DocumentDB cluster, you MUST use these exact commands — default is serverless on engine 8.0:

aws docdb create-db-cluster \
  --db-cluster-identifier <cluster_id> \
  --engine docdb \
  --engine-version 8.0.0 \
  --serverless-v2-scaling-configuration MinCapacity=1,MaxCapacity=16 \
  --master-username adminuser \
  --master-user-password '<password>' \
  --tags Key=created_by,Value=documentdb-skill Key=generation_model,Value=<your-model-id> \
  --region <region>

aws docdb create-db-instance \
  --db-instance-identifier <cluster_id>-instance \
  --db-instance-class db.serverless \
  --engine docdb \
  --db-cluster-identifier <cluster_id> \
  --tags Key=created_by,Value=documentdb-skill Key=generation_model,Value=<your-model-id> \
  --region <region>

Credential handling: the command above takes the password inline for a self-contained workflow. For production, prefer --manage-master-user-password instead of --master-user-password — DocumentDB then generates the password and stores it in Secrets Manager with rotation, keeping it out of shell history. The two flags are mutually exclusive; when you use managed passwords, retrieve the credential with aws secretsmanager get-secret-value --secret-id <arn> (the secret ARN is on the cluster's MasterUserSecret) when building the connection string.

This applies to every create-cluster request regardless of phrasing. Switch to a fixed instance class (db.r8g.large etc.) only if the user explicitly describes sustained 24/7 high throughput or says "provisioned" or "instance-based". Never recommend Elastic Clusters unless the user has exhausted instance-based scaling options.

When the user asks which instance types are available in a specific region, or asks about instance class options, requirements, or regional availability: fetch the live DocumentDB instance classes page via web_fetch or aws___search_documentation — do NOT answer from memory, as available instance classes vary by region and change with new launches. You can also verify programmatically: aws docdb describe-orderable-db-instance-options --engine docdb --region <region> lists every available instance class and engine version combination for that region.

Resource tagging (always apply on resource creation)

When creating any DocumentDB resource (create-db-cluster, create-db-instance), ALWAYS include these tags using the RDS list-of-records --tags syntax (DocumentDB uses the RDS API):

--tags Key=created_by,Value=documentdb-skill Key=generation_model,Value={your-model-id}

Example: --tags Key=created_by,Value=documentdb-skill Key=generation_model,Value=claude-sonnet-4-20250514

Include these tags even if the user does not mention tagging, so that they can identify the resources created via this skill. If the user provides additional tags, append these to their tags rather than replacing them. The same applies when tagging via add-tags-to-resource on existing resources.

Do NOT execute (refuse, explain why, offer assessment instead)

• Delete cluster or instance: delete-db-cluster, delete-db-instance — irreversible data loss
• Failover: failover-db-cluster — production impact, use only under planned change control
• Major version upgrade: modify-db-cluster --engine-version across major versions (4.0 -> 5.0, 5.0 -> 8.0) — requires prechecks and a rollback plan; use the MVU workflow in references/upgrade.md
• Reboot: reboot-db-instance — production impact

When refusing, explain why and offer the matching assessment workflow:

"I can't perform [action] because [reason]. I can run an assessment to help you decide. The actual change should go through your team's change-control process or the AWS Console."

Common Tasks

1. Verify Dependencies

Check that required tools are available in context before running any workflow.

Constraints:

• You MUST verify call_aws (or AWS CLI v2), shell, and web_fetch are available in context
• You MUST check python3 >= 3.6 for wa_review.py, the amazon-documentdb-tools compat tool, and the index tool
• You MUST check git, curl, mongosh, and ssh only when a specific workflow requires them
• You MUST inform the user of any missing tools and respect a decision to abort
• You MUST NOT invoke the tools during verification because that would trigger live AWS calls or cluster connections before the user confirms they are ready
• You SHOULD confirm credentials are valid with aws sts get-caller-identity before live-analysis steps

2. Classify the Request and Route

Use the Decision Guide to pick one workflow.

Constraints:

• You MUST name the workflow you are routing to before loading the reference
• You MUST pass along cluster id, region, app name, source URI, and engine versions the user already supplied — they SHOULD NOT re-type these
• You MAY ask one clarifying question if a request straddles two workflows
• You MUST NOT fabricate workflow names for out-of-scope topics because doing so misleads the user about coverage

3. Execute the Workflow

Load the matching references/<workflow>.md and follow its ## Workflow section.

Constraints:

• You MUST execute AWS CLI commands, DMS calls, mongosh queries, and bundled scripts yourself — the skill is an executor unless a step requires credentials the agent doesn't have
• You MUST explain what step is running, why, and which tool is being called before running it
• Extract required parameters from the conversation first — if cluster_id, region, or other required values are already present, use them and proceed. Only ask for missing parameters, and ask for all missing ones together in a single prompt.
• You MUST support multiple input methods for parameters: direct input, file path, or URL
• You MUST validate parameter formats: cluster id (lowercase, hyphens), region (us-east-1), ARN (arn:aws:...), ISO-8601, CIDR
• You MUST NOT create or access credentials directly because the skill has no safe way to store or rotate them — use IAM roles, instance profiles, Secrets Manager ARNs, or delegate credential setup (e.g. aws sso login / aws configure) to the user
• You MUST NOT use call_aws with positional filesystem arguments because the MCP sandbox rejects them — pass JSON payloads inline or invoke scripts under scripts/ via shell
• You MUST NOT grant wildcard IAM (Action: "*" or Resource: "*") or open security groups to 0.0.0.0/0 in examples because those defaults cause customer production incidents
• You SHOULD save artifacts to artifacts/{app-name}/: compatibility-report.md, migration-plan.md, upgrade-plan.md, wa_review_results.json
• If multiple workflows ran, you MUST close with a 2–4 line synthesis linking the artifacts

Required parameters (ask upfront, together): cluster_id — the cluster name the user refers to (e.g. "my cluster xyz" or "cluster xyz"), maps to --db-cluster-identifier in AWS CLI (lowercase-hyphens); region (e.g. us-east-1); app_name. Per workflow: source_uri (compat/migration), target_version (5.0 or 8.0 for upgrade/compat), engine_class (db.serverless default, or db.r8g.large etc. for provisioned instance-based).