AWS Amazon DynamoDB Accelerator (DAX)
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AI ReadinessAI-Aware (B-)61/100
Community OpenAPI document · agent-readyStorageDatabasehmac21 EndpointsREST
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For Agents
Provision and operate Amazon DynamoDB Accelerator (DAX) clusters, manage parameter and subnet groups, and scale replication factor for cached DynamoDB reads.
Use for: I want to provision a new DAX cluster in front of my DynamoDB table, Increase the replication factor on an existing DAX cluster, List all DAX clusters in this AWS account and region, Retrieve recent lifecycle events for a DAX cluster
Not supported: Does not handle reading or writing cached items, DynamoDB table operations, or VPC networking - use for DAX cluster lifecycle and configuration only.
Jentic maintains a curated, agent-optimized OpenAPI specification for Amazon DynamoDB Accelerator (DAX), keeping it validated and agent-ready. Amazon DynamoDB Accelerator (DAX) is a managed in-memory caching service that sits in front of Amazon DynamoDB to deliver sub-millisecond read latency for cached items. The DAX control plane API lets you provision and operate clusters, manage parameter groups and subnet groups, scale replication factor, describe events, and tag resources. It is intended for read-heavy workloads where DynamoDB latency or cost-per-read becomes a bottleneck and a write-through cache makes economic sense.
Install Jentic One Beta
Connect the Amazon DynamoDB Accelerator (DAX) to your agent
Jentic One is a self-hosted execution layer for AI agents. It lets your agent call the Amazon DynamoDB Accelerator (DAX), or any other public or private API you need. You set the rules, the agent never sees your credentials, and every call is logged.
Two steps, two machines. Install the instance in a safe environment, then register your agent from wherever it runs.
1
Step 1: Jentic One Host machine
# On the machine that will host your Jentic One instance:
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Famazonaws.com%2Faws-dax" | sh2
Step 2: Agent machine
# On the machine where your agent runs (keep this separate from the instance):
curl -fsSL "https://jentic.com/install.sh?src=apis&api=%2Fapis%2Famazonaws.com%2Faws-dax" | sh
jentic register # connects your agent to your Jentic One instanceJentic One is in public beta. The setup above keeps your agent separate from the instance, which is what you want before using real credentials: an agent running as the same OS user as Jentic One can read its stored keys directly. Just evaluating? A single local install is fine to start. See the secure deployment guide for the tiers.
Capabilities
What an agent can do with Amazon DynamoDB Accelerator (DAX) API.
Provision DAX clusters with CreateCluster and tear them down with DeleteCluster when read-cache workloads change
Adjust read capacity by calling IncreaseReplicationFactor or DecreaseReplicationFactor on a running cluster
Manage cluster configuration through CreateParameterGroup, UpdateParameterGroup, and DescribeParameters
Place clusters in target VPCs with CreateSubnetGroup and UpdateSubnetGroup
Investigate operational issues by calling DescribeEvents to retrieve cluster lifecycle and failure events
Apply IAM-style governance with TagResource, UntagResource, and ListTags on DAX clusters
Use Cases
Patterns agents use Amazon DynamoDB Accelerator (DAX) API for, with concrete tasks.
★ Add a Read Cache in Front of DynamoDB
Provision a DAX cluster to absorb hot-key read traffic from a DynamoDB table, cutting read latency from single-digit milliseconds to microseconds for cached items. The DAX control plane handles cluster creation, node placement across subnets, and parameter group binding so the application only needs to point its DAX client at the cluster endpoint. Useful when scaling read-heavy workloads where provisioned read capacity costs grow faster than the application can tolerate.
Call CreateCluster with a node type of dax.r4.large, replication factor 3, and a target subnet group, then poll DescribeClusters until the cluster status is available.
Scale a DAX Cluster for Traffic Spikes
Increase or decrease the number of read replicas in a DAX cluster as traffic patterns change. IncreaseReplicationFactor adds nodes for a marketing event or product launch, and DecreaseReplicationFactor removes them once the spike subsides, keeping the cluster sized to actual demand without re-creating it.
Call IncreaseReplicationFactor on cluster my-dax-cluster with NewReplicationFactor 5 and verify the cluster returns to status available.
Manage Cluster Parameter Groups
Create, update, and inspect DAX parameter groups to control TTL, query cache size, and engine-level tuning. Parameter groups let multiple clusters share a tested configuration and roll out changes via DescribeParameters and parameter group updates rather than per-cluster edits.
Create a parameter group named dax-prod-defaults, update query-ttl-millis to 600000, and attach it to two existing DAX clusters.
Agent-Driven Cluster Lifecycle Management
An AI agent connected via Jentic can provision DAX clusters from a natural-language request, scale them in response to monitoring alerts, and decommission them on schedule. Jentic returns the matching DAX operation and its input schema so the agent can call CreateCluster, IncreaseReplicationFactor, or DeleteCluster with structured arguments and recover cleanly from validation errors.
Search Jentic for 'create a DAX cluster', load the CreateCluster schema, execute with the requested cluster name and node type, then poll DescribeClusters until status is available.
Key Endpoints
21 endpoints — jentic maintains a curated, agent-optimized openapi specification for amazon dynamodb accelerator (dax), keeping it validated and agent-ready.
METHOD
PATH
DESCRIPTION
POST
/#X-Amz-Target=AmazonDAXV3.CreateCluster
Create a new DAX cluster
POST
/#X-Amz-Target=AmazonDAXV3.DescribeClusters
Describe one or more DAX clusters
POST
/#X-Amz-Target=AmazonDAXV3.IncreaseReplicationFactor
Add read replicas to a cluster
POST
/#X-Amz-Target=AmazonDAXV3.DecreaseReplicationFactor
Remove read replicas from a cluster
POST
/#X-Amz-Target=AmazonDAXV3.DeleteCluster
Delete a DAX cluster
POST
/#X-Amz-Target=AmazonDAXV3.CreateSubnetGroup
Create a subnet group for cluster placement
POST
/#X-Amz-Target=AmazonDAXV3.DescribeEvents
List recent cluster events
POST
/#X-Amz-Target=AmazonDAXV3.CreateCluster
Create a new DAX cluster
POST
/#X-Amz-Target=AmazonDAXV3.DescribeClusters
Describe one or more DAX clusters
POST
/#X-Amz-Target=AmazonDAXV3.IncreaseReplicationFactor
Add read replicas to a cluster
POST
/#X-Amz-Target=AmazonDAXV3.DecreaseReplicationFactor
Remove read replicas from a cluster
POST
/#X-Amz-Target=AmazonDAXV3.DeleteCluster
Delete a DAX cluster
POST
/#X-Amz-Target=AmazonDAXV3.CreateSubnetGroup
Create a subnet group for cluster placement
POST
/#X-Amz-Target=AmazonDAXV3.DescribeEvents
List recent cluster events
Jentic AI Readiness Score
This API is usable in Jentic One now. Its AI-readiness score against Jentic's framework shows where it stands today and where improvements would make it even easier for agents to use.
A
Amazon DynamoDB Accelerator (DAX)
- AI-Aware (B-)61/100
99
Foundational Compliance63
Developer Experience & Jentic Compatibility37
AI-Readiness & Agent Experience94
Agent Usability50
Security100
AI DiscoverabilityPowered by JenticScoring Framework 1.0.0 | Scoring Engine 0.4.0
Show dimension breakdown
99
Foundational Compliance
Base layer of spec validity and structural soundness.
Grade: A+Signals: 4
97%
Lint Results
Aggregated quality score from linter diagnostics, weighted by severity.
100%
Resolution Completeness
Percentage of `$ref` references that resolve successfully.
100%
Specification Validity
Checks whether the API description parses successfully and conforms to its declared specification (e.g., OpenAPI).
100%
Structural Integrity
Structural correctness score based on schema issues using logarithmic dampening.
63
Developer Experience & Jentic Compatibility
Clarity, completeness, and ingestion readiness for developers and tooling.
Grade: B-Signals: 4
0%
Example Density
How richly the API is illustrated with examples.
100%
Example Validity
Percentage of examples that conform to their schemas.
50%
Response Coverage
Percentage of operations with complete response definitions (success, client error, server error).
100%
Tooling Readiness
Health of API ingestion, bundling, and resolution within Jentic pipelines.
37
AI-Readiness & Agent Experience
Semantic breadth, depth, and agent comprehension for AI systems.
Grade: FSignals: 4
49%
Description Coverage
Coverage of descriptions across API elements.
0%
Error Standardization
Coverage of RFC 9457 Problem Details for error responses.
100%
OperationId Quality
Coverage, uniqueness, and casing consistency of operationIds for AI inference.
0%
Summary Coverage
Coverage of summaries across operations/tags/info.
94
Agent Usability
Functional utility, complexity comfort, and AI orchestration readiness.
Grade: A+Signals: 1
94%
Complexity Comfort
Agent comfort level based on API operational and structural complexity.
50
Security
Trust, risk posture, and security compliance.
Grade: C-Signals: 1
50%
Authentication Strength
Average quality of security schemes based on authentication method strength (weakest link for OAuth2).
100
AI Discoverability
Findability, semantic richness, and reasoning readiness.
Grade: A+Signals: 1
100%
Descriptive Richness
Clarity and depth of descriptions across API elements.
Score it yourself
Every API in the directory is allowlisted, so you can re-score it with no key required.
npx @jentic/api-scorecard-cli score <openapi-url>Why Jentic?
What agents get from Jentic-routed access to this vendor.
Setup
Wiring Amazon DynamoDB Accelerator by hand means implementing AWS Signature v4 request signing, resolving the regional dax host, and setting the X-Amz-Target header to route each JSON action to the right operation. Through Jentic you install once, import Amazon DynamoDB Accelerator from the API Directory, store the AWS credentials once, and your agent calls it.
Permission scoping
This API routes every call through a single endpoint with the action named in the request, covering cluster operations like CreateCluster, DescribeClusters, and IncreaseReplicationFactor. Limit the agent to the operations it needs, such as DescribeClusters and DescribeEvents, so DeleteCluster is not included unless you add it.
Credential isolation
Your AWS credentials are stored once, encrypted, by your own Jentic One instance and injected at execution time. They never enter the agent's prompt, logs, or context.
Intent-based discovery
Agents search Jentic by intent such as 'create a DAX cluster' or 'add read replicas to my cache', and Jentic returns the matching Amazon DynamoDB Accelerator operation with its input schema so the agent calls the right endpoint without browsing the AWS service reference.
Related APIs
Alternatives and complements available in the Jentic catalogue.
FAQs
Specific to using Amazon DynamoDB Accelerator (DAX) API through Jentic.
Which OpenAPI specification does this Amazon DynamoDB Accelerator (DAX) page describe?
A curated, agent-optimized Jentic specification covering 21 DAX control plane operations. AWS describes DAX through its own official service model, the Smithy model in the `aws/api-models-aws` repository, and documents all 21 operations as reference pages in its API documentation at `https://docs.aws.amazon.com/amazondynamodb/latest/APIReference/`. That model is official, but it is not an OpenAPI document. The OpenAPI variant here descends from the community APIs.guru entry at `https://api.apis.guru/v2/specs/amazonaws.com/dax/2017-04-19/openapi.json`, generated from an AWS SDK model by the third-party aws2openapi converter, and Jentic keeps it validated and agent-ready. Get started with Jentic One, the self-hosted execution layer.
What authentication does the Amazon DAX API use?
The DAX control plane API uses AWS Signature Version 4 (HMAC) signing with an AWS access key ID and secret access key. Through Jentic, those credentials live encrypted in your Jentic One instance and are injected into signed requests at execution time, so the agent never sees the raw secret access key.
Can I scale a DAX cluster up and down with the API?
Yes. Use IncreaseReplicationFactor to add read replicas and DecreaseReplicationFactor to remove them. The cluster remains available during scaling, so no recreation is needed for routine capacity changes.
What are the rate limits for the Amazon DAX API?
The DAX control plane shares standard AWS service quotas, which are managed per account and region rather than published as fixed per-second limits in the spec. Treat sustained CreateCluster or scaling calls as throttle-prone and back off on ThrottlingException responses.
How do I provision a DAX cluster through Jentic?
Search Jentic for 'create a DAX cluster', load the CreateCluster operation schema, then execute it with parameters like ClusterName, NodeType, ReplicationFactor, and SubnetGroupName. Run pip install jentic to get the SDK and use the async search and execute pattern.
Does this API serve cached data, or only manage clusters?
This API only manages clusters and configuration. Reading and writing cached items goes through the DAX data-plane client libraries (which speak the DAX wire protocol) against the cluster endpoint returned by DescribeClusters.
Can I limit what my agent is allowed to do with the AWS DAX API?
Yes. Because Jentic One is self-hosted, your own rules decide which DAX operations and credentials the agent may use, even though every call routes through one endpoint with the action named in the request. You can allow the agent only the operations it needs, such as DescribeClusters and DescribeEvents for read-only monitoring, so a destructive action like DeleteCluster is never available unless you add it. Scaling actions like IncreaseReplicationFactor and DecreaseReplicationFactor can be granted or withheld the same way.
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