Amazon Aurora DSQL
Amazon Aurora DSQL is a distributed SQL database service optimized for transactional workloads. It provides a serverless, fully managed PostgreSQL-compatible database with built-in high availability, scalability, and global distribution capabilities.
Limited machine-readable signal and partial portal coverage — documentation a human can read, but little a machine or agent can consume without scraping.
API Evangelist profiles Amazon Aurora DSQL the way a machine reads it — 92 machine-readable artifacts across 2 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon Aurora DSQL scores 37.4/100 (thin), with a separate agent-readiness read of 48/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Amazon Aurora DSQL
Each block below is one kind of artifact we hold for Amazon Aurora DSQL. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 2
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Amazon Aurora DSQL Clusters API
Operations for creating and managing Aurora DSQL clusters
Amazon Aurora DSQL Multi-Region Clusters API
Operations for managing multi-region cluster configurations
Features 10
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
Scroll within the panel for all 10 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Aurora Dsql Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon Aurora DSQL API Rules
SPECTRALAmazon Aurora DSQL API Rules
SPECTRALJSON Schema 19
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
ClusterStatus
JSON SCHEMAClusterSummary
JSON SCHEMACreateClusterInput
JSON SCHEMACreateClusterOutput
JSON SCHEMACreateMultiRegionClustersInput
JSON SCHEMACreateMultiRegionClustersOutput
JSON SCHEMADeleteClusterOutput
JSON SCHEMADeleteMultiRegionClustersInput
JSON SCHEMADeleteMultiRegionClustersOutput
JSON SCHEMAGetClusterEndpointOutput
JSON SCHEMAGetClusterOutput
JSON SCHEMALinkedClusterProperties
JSON SCHEMAListClustersOutput
JSON SCHEMAListTagsForResourceOutput
JSON SCHEMATagResourceInput
JSON SCHEMATagResourceOutput
JSON SCHEMAUntagResourceOutput
JSON SCHEMAUpdateClusterInput
JSON SCHEMAUpdateClusterOutput
JSON SCHEMAScroll within the panel for all 19 ·
JSON Structure 19
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Aurora Dsql Cluster Status Structure
JSON STRUCTUREAurora Dsql Cluster Summary Structure
JSON STRUCTUREAurora Dsql Create Cluster Input Structure
JSON STRUCTUREAurora Dsql Create Cluster Output Structure
JSON STRUCTUREAurora Dsql Create Multi Region Clusters Input Structure
JSON STRUCTUREAurora Dsql Delete Cluster Output Structure
JSON STRUCTUREAurora Dsql Delete Multi Region Clusters Input Structure
JSON STRUCTUREAurora Dsql Get Cluster Endpoint Output Structure
JSON STRUCTUREAurora Dsql Get Cluster Output Structure
JSON STRUCTUREAurora Dsql Linked Cluster Properties Structure
JSON STRUCTUREAurora Dsql List Clusters Output Structure
JSON STRUCTUREAurora Dsql List Tags For Resource Output Structure
JSON STRUCTUREAurora Dsql Tag Resource Input Structure
JSON STRUCTUREAurora Dsql Tag Resource Output Structure
JSON STRUCTUREAurora Dsql Untag Resource Output Structure
JSON STRUCTUREAurora Dsql Update Cluster Input Structure
JSON STRUCTUREAurora Dsql Update Cluster Output Structure
JSON STRUCTUREScroll within the panel for all 19 ·
Examples 19
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Scroll within the panel for all 19 ·
Security Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 6
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Integrations 10
Pre-built integrations with other platforms tell you where this provider already fits in a stack.
Pre-built integrations with other platforms and tools.
Scroll within the panel for all 10 ·
Resources
Every other property we hold for Amazon Aurora DSQL — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Agent Surfaces 2
MCP servers, agent skills, and machine-readable catalogs
Access & Security 4
Authentication, authorization, and security posture
← All providers · Data indexed from github.com/api-evangelist/amazon-aurora-dsql · machine-readable index on apis.io