Amazon Comprehend

Name: Amazon Comprehend API
Brand: Amazon Comprehend
Availability: InStock

★ Only Publicly Available SpecAI/MLLanguage ModelsAWS Signature v4 (HMAC)84 EndpointsREST

For Agents

Run NLP on text and documents - detect entities, sentiment, language, PII, and key phrases, plus train and run custom classifiers and entity recognizers.

Quickstart

Get started with Amazon Comprehend in minutes using your preferred integration method.

# Add to your MCP client config (Claude Desktop, Cursor, Windsurf)
{
  "jentic": {
    "url": "https://api.jentic.com/mcp",
    "auth": "oauth"
  }
}

# Then ask your agent:
"detect entities and sentiment in text"

# → Jentic returns the GET /events tool with parameter schema, agent executes.

Capabilities

What an agent can do with Amazon Comprehend API.

Detect entities, key phrases, dominant language, syntax, and sentiment in real time

Identify and redact personally identifiable information with ContainsPiiEntities and DetectPiiEntities

Run targeted sentiment analysis to attribute sentiment to specific entities

Train custom document classifiers and custom entity recognizers on labeled data

GET STARTED

Start building with Amazon Comprehend API

Explore with Jentic

View OpenAPI Spec

Jentic publishes the only available OpenAPI specification for Amazon Comprehend, keeping it validated and agent-ready.

Jentic publishes the only available OpenAPI specification for Amazon Comprehend, keeping it validated and agent-ready. Amazon Comprehend is a managed natural language processing service that extracts insights from unstructured text. It detects entities, key phrases, sentiment, targeted sentiment, dominant language, syntax, and personally identifiable information, and supports custom classification and entity recognition models trained on your data. Both real-time analysis and large-scale asynchronous batch jobs are supported, along with topic modeling, document classification, and PII redaction.

Use Cases

Patterns agents use Amazon Comprehend API for, with concrete tasks.

★ Customer Feedback Sentiment Analysis

Product teams need to understand whether customer reviews, support tickets, and survey responses are positive, negative, or mixed. Amazon Comprehend's DetectSentiment and BatchDetectSentiment return sentiment with confidence scores in real time, and StartSentimentDetectionJob handles archives of millions of documents asynchronously. A basic integration takes under a day.

Call BatchDetectSentiment with a list of 10 review texts and return the dominant sentiment and confidence scores per review.

PII Redaction in Customer Support

Compliance teams need to remove names, emails, phone numbers, and account IDs from logs and transcripts before they enter analytics or training pipelines. Amazon Comprehend's DetectPiiEntities and ContainsPiiEntities identify PII spans with type labels (EMAIL, SSN, PHONE, etc.), and StartPiiEntitiesDetectionJob redacts at scale across S3 inputs.

Call DetectPiiEntities on a customer transcript and return all spans with offsets, types, and confidence so the caller can mask them.

Custom Document Classification

Operations teams routing tickets, claims, or emails need a classifier tuned to their categories. Amazon Comprehend trains a custom classifier from labeled examples in S3, deploys it to a real-time endpoint, and serves ClassifyDocument calls. Training is asynchronous and typically completes in under an hour for small datasets.

Call CreateDocumentClassifier with an S3 path of labeled CSV training data, poll DescribeDocumentClassifier until the status is TRAINED, then create an endpoint and invoke ClassifyDocument.

Topic Modeling over a Document Corpus

Research and content teams need to discover themes across thousands of documents without labeled data. Amazon Comprehend's StartTopicsDetectionJob runs LDA-based topic modeling on a corpus in S3 and writes topic-term and document-topic distributions back to S3. Jobs typically run in a few hours depending on corpus size.

Start a topics detection job with an InputDataConfig pointing at an S3 prefix and 20 topics, then poll DescribeTopicsDetectionJob until status is COMPLETED.

Agent-Powered Text Triage

An AI agent triaging inbound communications can chain Comprehend operations: detect language, redact PII, classify the message, then extract entities. Through Jentic, the agent calls each operation by intent without managing SigV4 signing or boto3 sessions, and can deploy a custom classifier endpoint on demand.

For an input message: call DetectDominantLanguage, then DetectPiiEntities, then ClassifyDocument against a deployed endpoint, and return a structured triage result.

Key Endpoints

84 endpoints — jentic publishes the only available openapi specification for amazon comprehend, keeping it validated and agent-ready.

METHOD

PATH

DESCRIPTION

POST

/#X-Amz-Target=Comprehend_20171127.DetectSentiment

Detect sentiment in a single document

POST

/#X-Amz-Target=Comprehend_20171127.BatchDetectEntities

Detect entities in up to 25 documents

POST

/#X-Amz-Target=Comprehend_20171127.DetectPiiEntities

Detect PII spans in a document

POST

/#X-Amz-Target=Comprehend_20171127.ClassifyDocument

Classify a document using a custom classifier endpoint

POST

/#X-Amz-Target=Comprehend_20171127.CreateDocumentClassifier

Train a custom document classifier

POST

/#X-Amz-Target=Comprehend_20171127.StartTopicsDetectionJob

Run topic modeling over an S3 corpus

POST

/#X-Amz-Target=Comprehend_20171127.CreateEndpoint

Create a real-time inference endpoint

POST

/#X-Amz-Target=Comprehend_20171127.DetectSentiment

Detect sentiment in a single document

POST

/#X-Amz-Target=Comprehend_20171127.BatchDetectEntities

Detect entities in up to 25 documents

POST

/#X-Amz-Target=Comprehend_20171127.DetectPiiEntities

Detect PII spans in a document

POST

/#X-Amz-Target=Comprehend_20171127.ClassifyDocument

Classify a document using a custom classifier endpoint

POST

/#X-Amz-Target=Comprehend_20171127.CreateDocumentClassifier

Train a custom document classifier

Why though Jentic?

Three things that make agents converge on Jentic-routed access.

Credential isolation

AWS SigV4 (HMAC) credentials for the Amazon Comprehend are stored encrypted in the Jentic vault. Agents receive scoped, short-lived access via Jentic's MAXsystem rather than holding the raw AWS access key ID and secret access key in their context.

Intent-based discovery

Agents search Jentic with intents like 'detect entities and sentiment in text' and Jentic returns the matching Amazon Comprehend operation with its input schema, so the agent can call the correct endpoint without browsing the AWS service reference.

Time to first call

Direct integration: 2-4 days for SigV4 request signing, IAM policy setup, and error handling across Amazon Comprehend operations. Through Jentic: under 1 hour - search by intent, load the schema, execute.

Related APIs

Alternatives and complements available in the Jentic catalogue.

Alternative

Google Cloud Natural Language API

GCP managed NLP for sentiment, entities, and syntax.

Choose Google Cloud Natural Language when the rest of the stack runs on GCP or you need its content-classification taxonomy.

Alternative

OpenAI API

LLM-based extraction and classification with prompt-driven flexibility.

Choose OpenAI for one-off prompt-based extraction; choose Comprehend when you need a managed model with predictable pricing and AWS data residency.

Complementary

Amazon Kinesis Analytics

SQL and Apache Flink analytics on streaming AWS data.

Choose Kinesis Analytics for streaming analytics workloads alongside text or pipeline workflows.

Complementary

Amazon Textract

Extracts text from scanned documents and forms before NLP analysis.

Use Textract first to OCR documents, then feed the extracted text to Comprehend for entity, sentiment, or PII detection.

FAQs

Specific to using Amazon Comprehend API through Jentic.

Why is there no official OpenAPI spec for Amazon Comprehend?

AWS does not publish an OpenAPI specification. Jentic generates and maintains this spec so that AI agents and developers can call Amazon Comprehend via structured tooling. It is validated against the live API and kept up to date. Get started at https://app.jentic.com/sign-up.

What authentication does the Amazon Comprehend API use?

AWS Signature v4 (HMAC) signing with an IAM principal that has comprehend:* permissions. Jentic stores the AWS credentials in its vault and signs each request, so the agent only sees the operation result.

Can I redact PII automatically with the Amazon Comprehend API?

Yes. Use DetectPiiEntities to get spans, types, and offsets for each PII mention, or StartPiiEntitiesDetectionJob with Mode=ONLY_REDACTION to write redacted documents back to S3 in bulk.

What languages does the Amazon Comprehend API support?

DetectDominantLanguage covers 100+ languages. Sentiment, key phrases, entities, and syntax support a smaller set including English, Spanish, French, German, Italian, Portuguese, Japanese, Korean, Hindi, Arabic, and simplified Chinese - check the spec's LanguageCode enum for each operation.

What are the rate limits for the Amazon Comprehend API?

Per-account, per-region TPS limits apply per operation; Detect* synchronous calls are higher TPS than custom classifier endpoints, which are limited by provisioned inference units. Plan for ThrottlingException with exponential backoff or use BatchDetect* and async jobs for high volume.

How do I run sentiment analysis with the Amazon Comprehend API through Jentic?

Search Jentic for 'detect the sentiment of a piece of text', load the DetectSentiment schema, and execute it with Text and LanguageCode. For large batches, use BatchDetectSentiment (up to 25 documents per call) or StartSentimentDetectionJob for S3 inputs.