Amazon Aurora DSQL Skill

Aurora DSQL is a serverless, PostgreSQL-compatible distributed SQL database. This skill provides direct database interaction via MCP tools, schema management, migration support, and multi-tenant patterns.

Key capabilities:

• Direct query execution via MCP tools
• Schema management with DSQL constraints
• Migration support and safe schema evolution
• Multi-tenant isolation patterns
• IAM-based authentication

Reference Files

Load these files as needed for detailed guidance:

development-guide.md

When: ALWAYS load before implementing schema changes or database operations Contains: Best Practices, DDL rules, connection patterns, transaction limits, data type serialization patterns, application-layer referential integrity instructions, security best practices

MCP:

mcp-setup.md

When: Always load for guidance using or updating the DSQL MCP server Contains: Instructions for setting up the DSQL MCP server with 2 configuration options as sampled in mcp/.mcp.json

1. Documentation-Tools Only
2. Database Operations (requires a cluster endpoint)

mcp-tools.md

When: Load when you need detailed MCP tool syntax and examples. PREFER MCP tools for ad-hoc queries — execute directly rather than writing scripts. Contains: Tool parameters, detailed examples, usage patterns, input validation

language.md

When: MUST load when making language-specific implementation choices. ALWAYS prefer DSQL Connector when available. Contains: Driver selection, framework patterns, connection code for Python/JS/Go/Java/Rust

dsql-examples.md

When: Load when looking for specific implementation examples Contains: Code examples, repository patterns, multi-tenant implementations

troubleshooting.md

When: Load when debugging errors or unexpected behavior. SHOULD always consult for OCC errors, connection failures, or unexpected query results. Contains: Common pitfalls, error messages, solutions

onboarding.md

When: User explicitly requests to "Get started with DSQL" or similar phrase Contains: Interactive step-by-step guide for new users

access-control.md

When: MUST load when creating database roles, granting permissions, setting up schemas for applications, or handling sensitive data. ALWAYS use scoped roles for applications — create database roles with dsql:DbConnect. Contains: Scoped role setup, IAM-to-database role mapping, schema separation for sensitive data, role design patterns

DDL Migrations (modular):

ddl-migrations/overview.md

When: MUST load when performing DROP COLUMN, RENAME COLUMN, ALTER COLUMN TYPE, or DROP CONSTRAINT Contains: Table recreation pattern overview, transaction rules, common verify & swap pattern

ddl-migrations/column-operations.md

When: Load for DROP COLUMN, ALTER COLUMN TYPE, SET/DROP NOT NULL, SET/DROP DEFAULT migrations Contains: Step-by-step migration patterns for column-level changes

ddl-migrations/constraint-operations.md

When: Load for ADD/DROP CONSTRAINT, MODIFY PRIMARY KEY, column split/merge migrations Contains: Step-by-step migration patterns for constraint and structural changes

ddl-migrations/batched-migration.md

When: Load when migrating tables exceeding 3,000 rows Contains: OFFSET-based and cursor-based batching patterns, progress tracking, error handling

MySQL Migrations (modular):

mysql-migrations/type-mapping.md

When: MUST load when migrating MySQL schemas to DSQL Contains: MySQL data type mappings, feature alternatives, DDL operation mapping

mysql-migrations/ddl-operations.md

When: Load when translating MySQL DDL operations to DSQL equivalents Contains: ALTER COLUMN, DROP COLUMN, AUTO_INCREMENT, ENUM, SET, FOREIGN KEY migration patterns

mysql-migrations/full-example.md

When: Load when migrating a complete MySQL table to DSQL Contains: End-to-end MySQL CREATE TABLE migration example with decision summary

Query Plan Explainability (modular):

When: MUST load all four at Workflow 8 Phase 0 — query-plan/plan-interpretation.md, query-plan/catalog-queries.md, query-plan/guc-experiments.md, query-plan/report-format.md Contains: DSQL node types + Node Duration math + estimation-error bands, pg_class/pg_stats/pg_indexes SQL + correlated-predicate verification, GUC experiment procedures + 30-second skip protocol, required report structure + element checklist + support request template

SQL Compatibility Validation:

dsql-lint.md

When: MUST load before running dsql_lint, processing externally-sourced SQL (pg_dump, ORM migrations, user-pasted DDL), or resolving fixed_with_warning / unfixable diagnostics Contains: dsql_lint MCP tool reference, fix statuses, ORM integration, unfixable error resolution

MCP Tools Available

The aurora-dsql MCP server provides these tools:

Database Operations:

1. readonly_query - Execute SELECT queries (returns list of dicts)
2. transact - Execute DDL/DML statements in transaction (takes list of SQL statements)
3. get_schema - Get table structure for a specific table

SQL Validation:

1. dsql_lint - Validate SQL for DSQL compatibility and optionally auto-fix issues. Use before executing externally-sourced SQL.

Documentation & Knowledge:

1. dsql_search_documentation - Search Aurora DSQL documentation
2. dsql_read_documentation - Read specific documentation pages
3. dsql_recommend - Get DSQL best practice recommendations

Note: There is no list_tables tool. Use readonly_query with information_schema.

See mcp-setup.md for detailed setup instructions. See mcp-tools.md for detailed usage and examples.

AWS Knowledge MCP (awsknowledge)

Consult for verifying DSQL service limits before advising users. The numeric limits below are defaults that may change — when a user's decision depends on an exact limit, verify it first:

When to verify: Before recommending batch sizes, connection pool settings, or schema designs where hitting a limit would cause failures; any time the exact number can affect user decision.

Fallback: If awsknowledge is unavailable, use the defaults above and flag that limits should be verified against DSQL documentation.

CLI Scripts Available

Bash scripts in scripts/ for cluster management (create, delete, list, cluster info), psql connection, and bulk data loading from local/s3 csv/tsv/parquet files. See scripts/README.md for usage and hook configuration.

Quick Start

1. Explore: Use readonly_query with information_schema to list tables. Use get_schema for table structure.
2. Query: Use readonly_query for SELECT queries. MUST include tenant_id in WHERE for multi-tenant apps. MUST build SQL with safe_query.build().
3. Schema changes: Use transact with one DDL per transaction. MUST batch DML under 3,000 rows. MUST use CREATE INDEX ASYNC in a separate call. Use dsql_lint to validate first.

Common Workflows

Workflow 1: Create Multi-Tenant Schema

1. Create main table with tenant_id column using transact
2. Create async index on tenant_id in separate transact call
3. Create composite indexes for common query patterns (separate transact calls)
4. Verify schema with get_schema

• MUST include tenant_id in all tables
• MUST use CREATE INDEX ASYNC exclusively
• MUST issue each DDL in its own transact call: transact(["CREATE TABLE ..."])
• MUST serialize arrays as TEXT or JSON; cast back at query time (string_to_array(text, ',') or jsonb_array_elements_text(json::jsonb))

Workflow 2: Safe Data Migration

Every DDL statement generated in this workflow MUST be validated with dsql_lint(fix=true) before its transact call — applies to step 2 (ADD COLUMN) and step 5 (async index). DML (UPDATE in step 3) does not require linting.

1. Validate ALTER TABLE DDL with dsql_lint(sql=..., fix=true) — handle diagnostics per dsql-lint.md
2. Add column using transact: transact(["ALTER TABLE ... ADD COLUMN ..."])
3. Populate existing rows with UPDATE in separate transact calls (batched under 3,000 rows)
4. Verify migration with readonly_query using COUNT
5. If an index is needed: validate CREATE INDEX ASYNC DDL with dsql_lint(sql=..., fix=true), then create via transact

• MUST validate every externally-sourced or generated DDL statement with dsql_lint before executing
• MUST add column first, populate later
• MUST issue ADD COLUMN with only name and type; apply DEFAULT via separate UPDATE
• MUST batch updates under 3,000 rows in separate transact calls
• MUST issue each ALTER TABLE in its own transaction

Recovery — batch fails midway: Rows already updated keep their new value (each batch committed independently). Resume by filtering on the unset state (WHERE new_column IS NULL) and continue. Re-running is safe because the filter naturally excludes completed rows.

Workflow 3: Application-Layer Referential Integrity

INSERT: MUST validate parent exists with readonly_query → throw error if not found → insert child with transact.

DELETE: MUST check dependents with readonly_query COUNT → return error if dependents exist → delete with transact if safe.

Workflow 4: Query with Tenant Isolation

1. MUST authorize the caller against the tenant — format validation does not establish authorization
2. MUST build SQL with safe_query.build() — use allow()/regex() for values (emits 'v'), ident() for table/column names (emits "v"). See input-validation.md
3. MUST include tenant_id in the WHERE clause; reject cross-tenant access at the application layer

Workflow 5: Set Up Scoped Database Roles

MUST load access-control.md for role setup, IAM mapping, and schema permissions.

Workflow 6: Table Recreation DDL Migration

DSQL does NOT support direct ALTER COLUMN TYPE, DROP COLUMN, DROP CONSTRAINT, or MODIFY PRIMARY KEY. These require the Table Recreation Pattern. This is a destructive workflow that requires user confirmation at each step. Every generated DDL in the pattern (CREATE new, INSERT ... SELECT, DROP old, RENAME) MUST be validated with dsql_lint(sql=..., fix=true) before execution.

MUST load ddl-migrations/overview.md before attempting any of these operations.

Workflow 7: Validate and Migrate to DSQL

MUST load dsql-lint.md before running dsql_lint — it defines diagnostic handling, the three fix_result.status values (fixed, fixed_with_warning, unfixable), and user-confirmation gates.

Run dsql_lint(sql=source_sql, fix=true) to validate and auto-convert PostgreSQL-compatible SQL. dsql_lint uses a PostgreSQL parser, so MySQL dialect syntax that PostgreSQL cannot parse (e.g., PARTITION BY HASH, AUTO_INCREMENT in some positions) surfaces as a parse_error rule rather than individual diagnostics.

• For MySQL-origin SQL, MUST cross-check the source against mysql-migrations/type-mapping.md even when lint returns clean — ENGINE= clauses and SET(...) column types can pass silently through the PostgreSQL parser.
• On parse_error, fall back to mysql-migrations/type-mapping.md for manual conversion, then re-run dsql_lint on the converted output before executing.

Workflow 8: Query Plan Explainability

Explains why the DSQL optimizer chose a particular plan. Triggered by slow queries, high DPU, unexpected Full Scans, or plans the user doesn't understand. REQUIRES a structured Markdown diagnostic report is the deliverable beyond conversation — run the workflow end-to-end before answering. Use the aurora-dsql MCP when connected; fall back to raw psql with a generated IAM token (see the fallback block below) otherwise.

Phase 0 — Load reference material. Read all four before starting — each has content later phases need verbatim (node-type math, exact catalog SQL, the >30s skip protocol, required report elements):

1. query-plan/plan-interpretation.md — node types, duration math, anomalous values
2. query-plan/catalog-queries.md — pg_class / pg_stats / pg_indexes SQL
3. query-plan/guc-experiments.md — GUC procedures and >30s skip protocol
4. query-plan/report-format.md — required report structure

Phase 1 — Capture the plan. ALWAYS run readonly_query("EXPLAIN ANALYZE VERBOSE …") on the user's query verbatim (SELECT form) — ALWAYS capture a fresh plan from the cluster, even when the user describes the plan or reports an anomaly. MAY leverage get_schema or information_schema for schema sanity checks. When EXPLAIN errors (relation does not exist, column does not exist), MUST report the error verbatim — MUST NOT invent DSQL-specific semantics (e.g., case sensitivity, identifier quoting) as the root cause. Extract Query ID, Planning Time, Execution Time, DPU Estimate. SELECT runs as-is. UPDATE/DELETE rewrite to the equivalent SELECT (same join chain + WHERE) — the optimizer picks the same plan shape. INSERT, pl/pgsql, DO blocks, and functions MUST be rejected. MUST NOT use transact --allow-writes for plan capture; it bypasses MCP safety.

Phase 2 — Gather evidence. Using SQL from catalog-queries.md, query pg_class, pg_stats, pg_indexes, COUNT(*), COUNT(DISTINCT). Classify estimation errors per plan-interpretation.md (2x–5x minor, 5x–50x significant, 50x+ severe). Detect correlated predicates and data skew.

Phase 3 — Experiment (conditional). ≤30s: run GUC experiments per guc-experiments.md (default + merge-join-only) plus optional redundant-predicate test. >30s: skip experiments, include the manual GUC testing SQL verbatim in the report, and do not re-run for redundant-predicate testing. Anomalous values (impossible row counts): confirm query results are correct despite the anomalous EXPLAIN, flag as a potential DSQL bug, and produce the Support Request Template from report-format.md.

Phase 4 — Produce the report, invite reassessment. Produce the full diagnostic report per the "Required Elements Checklist" in query-plan/report-format.md — structure is non-negotiable. End with the "Next Steps" block from that reference so the user can ask for a reassessment after applying a recommendation. When the user says "reassess" (or equivalent), re-run Phase 1–2 and append an "Addendum: After-Change Performance" to the original report (before/after table, match against expected impact) rather than producing a new report.

psql fallback (MCP unavailable). Pipe statements into psql via heredoc and check $?; report failures without proceeding on partial evidence:

TOKEN=$(aws dsql generate-db-connect-admin-auth-token --hostname "$HOST" --region "$REGION")
PGPASSWORD="$TOKEN" psql "host=$HOST port=5432 user=admin dbname=postgres sslmode=require" <<<"EXPLAIN ANALYZE VERBOSE <sql>;"

Safety. Plan capture uses readonly_query exclusively — it rejects INSERT/UPDATE/DELETE/DDL at the MCP layer. Rewrite DML to SELECT (Phase 1) rather than asking transact --allow-writes to run it; write-mode transact bypasses all MCP safety checks. MUST NOT run arbitrary DDL/DML or pl/pgsql.

Error Scenarios

• awsknowledge returns no results: Use the default limits in the table above and note that limits should be verified against DSQL documentation.
• dsql_lint unavailable or timing out: See the Error Handling section of dsql-lint.md. Do not silently skip validation — inform the user and require explicit confirmation before proceeding with manual rules from development-guide.md.
• OCC serialization error: Retry the transaction. If persistent, check for hot-key contention — see troubleshooting.md.
• Transaction exceeds limits: Split into batches under 3,000 rows — see batched-migration.md.
• Token expiration mid-operation: Generate a fresh IAM token — see authentication-guide.md. See troubleshooting.md for other issues.

Additional Resources

• Aurora DSQL Documentation
• Code Samples Repository
• PostgreSQL Compatibility
• CloudFormation Resource