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Amazon Kendra website screenshot

Amazon Kendra

Amazon Kendra is an intelligent enterprise search service powered by machine learning that enables organizations to index and search across multiple data sources, delivering highly accurate and relevant answers to natural language queries.

agent ready

Reference-quality API operations across every facet — a rich contract, published governance, transparent operations, and machine-readable commercial terms.

Kin Score

API Evangelist profiles Amazon Kendra the way a machine reads it — 58 machine-readable artifacts across 8 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 Kendra scores 78.3/100 (exemplar), 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 Kin Score How this is scored →
scored 2026-07-27 · rubric v0.5
Composite quality — 78.3/100 · exemplar
Contract Quality 19.8 / 25
Developer Ergonomics 13.9 / 20
Commercial Clarity 17.4 / 20
Operational Transparency 6.8 / 13
Governance 10.4 / 12
Discoverability 10.0 / 10
Agent readiness — 48/100 · agent ready
Machine-Readable Contract 18 / 18
Agentic Access Contract 15 / 15
MCP Server 0 / 12
Machine-Readable Auth 10 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 7 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 0 / 4
Consent & Bot Identity 0 / 3

How we profile Amazon Kendra

Each block below is one kind of artifact we hold for Amazon Kendra. 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 8

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 Kendra Data Sources API

Operations for managing data source connectors

Amazon Kendra Documents API

Operations for managing documents in the index

Amazon Kendra Experience API

Operations for managing search experiences

Amazon Kendra FAQs API

Operations for managing FAQ entries

Amazon Kendra Indexes API

Operations for creating and managing search indexes

Amazon Kendra Queries API

Operations for querying the search index

Amazon Kendra Query Suggestions API

Operations for query autocompletion

Amazon Kendra Thesaurus API

Operations for managing custom synonyms

Scroll within the panel for all 8 ·

Postman Collections 1

A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.

Ready-to-run Postman collections for exercising this provider's APIs.

Open Collections 1

Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.

Open, tool-agnostic API collections (OpenAPI-derived and Bruno).

Amazon Kendra API

OPEN COLLECTION

Arazzo Workflows 12

Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.

Multi-step API workflows described with the Arazzo specification.

Amazon Kendra Create FAQ and Query

Load an FAQ file from S3 into an index, wait until it is active, then query for FAQ-backed answers.

ARAZZO

Amazon Kendra Create Search Experience

Wait for an index to be active, create a hosted search experience on it, and confirm it via the experiences list.

ARAZZO

Amazon Kendra Create Thesaurus and Query

Load a custom synonym thesaurus from S3 into an index, wait until it is active, then run a synonym-aware query.

ARAZZO

Amazon Kendra Ingest Documents and Query

Directly upload documents into an index, wait until they finish indexing, then run a search query.

ARAZZO

Amazon Kendra Provision Index and Start First Sync

Create an index, wait until it is active, attach a data source, and kick off the first sync job.

ARAZZO

Amazon Kendra Query Suggestions then Search

Generate type-ahead query suggestions for a partial query, then run a full search using the top suggestion.

ARAZZO

Amazon Kendra Refresh Documents

Remove stale documents from an index, upload their refreshed versions, and wait until the new versions are indexed.

ARAZZO

Amazon Kendra Reschedule and Resync Data Source

Update a data source's sync schedule, trigger an immediate sync, and wait for that sync to succeed.

ARAZZO

Amazon Kendra Resolve Index by Name and Query

Look up an index by name, confirm it is active, and run a search query against it.

ARAZZO

Amazon Kendra Retrieve Passages for RAG

Retrieve semantically relevant passages for a question and run a parallel ranked query to enrich a RAG context.

ARAZZO

Amazon Kendra Sync Data Source and Query

Start a data source sync job on an existing connector, wait for it to succeed, then query the refreshed index.

ARAZZO

Amazon Kendra Teardown Data Source and Index

Delete a data source connector, confirm it is gone, then delete the index that owned it.

ARAZZO

Scroll within the panel for all 12 ·

Pricing Plans 1

Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.

Published pricing tiers and plan structures.

Rate Limits 1

Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.

Documented rate limits and quota policies.

Amazon Kendra Rate Limits

5 limits

RATE LIMITS

FinOps 1

Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.

Cost, billing, and metering signals for API financial operations.

Features 8

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.

Intelligent Search

ML-powered semantic search that understands natural language queries and context to return highly accurate answers from enterprise content.

GenAI RAG Support

Kendra Retriever API enables retrieval-augmented generation workflows with optimized passage chunking and ACL-based filtering for LLM integration.

Data Source Connectors

Native connectors for Amazon S3, SharePoint, Salesforce, ServiceNow, Google Drive, Confluence, and many more data repositories.

Relevance Tuning

Fine-tune search results based on document freshness, authoritative sources, and custom synonyms without ML expertise.

Experience Builder

No-code visual interface to build, customize, and launch search applications with drag-and-drop components.

Search Analytics Dashboard

Visibility into quality and usability metrics and user interaction patterns to identify content gaps.

Custom Document Enrichment

Preprocessing capabilities for metadata enrichment, document classification, entity extraction, and AWS AI service integration.

Incremental Learning

Learns from user interactions and feedback to promote preferred documents to the top of search results over time.

Scroll within the panel for all 8 ·

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 Kendra Context

4 classes · 16 properties

JSON-LD

Spectral 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 Kendra API Rules

5 rules · 4 warnings

SPECTRAL

Amazon Kendra API Rules

26 rules · 10 errors · 16 warnings

SPECTRAL

JSON Schema 4

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.

DataSource

6 properties

JSON SCHEMA

Faq

6 properties

JSON SCHEMA

Index

7 properties

JSON SCHEMA

QueryResult

4 properties

JSON SCHEMA

JSON Structure 4

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.

Amazon Kendra Data Source Structure

6 properties

JSON STRUCTURE

Amazon Kendra Faq Structure

6 properties

JSON STRUCTURE

Amazon Kendra Index Structure

7 properties

JSON STRUCTURE

Amazon Kendra Query Result Structure

4 properties

JSON STRUCTURE

Examples 4

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.

Security Posture 4

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.

Amazon Kendra Authentication

apiKey · 1 scheme

SECURITY

Amazon Kendra Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Amazon Kendra Vulnerability Disclosure

security.txt · contact published

SECURITY

Amazon Kendra Trust Center

PCI DSS, HIPAA, FedRAMP, GDPR, FIPS 140

SECURITY

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.

Amazon Kendra Agentic Access

26 operations · 17 acting

26 operations · 17 acting

AGENTIC

Use Cases 5

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.

Employee Productivity

Help employees find accurate answers and data-driven insights across internal knowledge bases and document repositories.

Customer Service

Power self-service chatbots and agent-assist solutions for contact centers with intelligent search.

SaaS Application Integration

Integrate intelligent search and conversational AI into customer-facing applications via the Kendra API.

Generative AI Applications

Use Kendra GenAI indices in Amazon Q Business and Amazon Bedrock knowledge bases to build RAG applications.

Enterprise Knowledge Management

Index and search across multiple heterogeneous data sources to create a unified knowledge search experience.

Resources

Every other property we hold for Amazon Kendra — 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.

Get Started 4

Portal, sign-up, and the first successful call

Documentation 1

Reference material describing how the API behaves

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

Operate 3

Status, limits, changes, and where to get help

Commercial 3

Pricing, plans, and the legal terms of use

Company 1

The organization behind the API

← All providers · Data indexed from github.com/api-evangelist/amazon-kendra · machine-readable index on apis.io