MCP For Claude Marketplace
MonkeyLearn

MonkeyLearn MCP Connector for Claude

A+

Analyze text data with custom machine learning models that classify sentiment, extract keywords, and tag topics automatically.

12 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Connect your MonkeyLearn account to any AI agent and run NLP text analysis through natural conversation.

What you can do

  • Text Classification — Classify text by sentiment, topic, intent, or custom labels
  • Entity Extraction — Pull structured data like names, keywords, and addresses from text
  • NLP Workflows — Run multi-step Studio workflows for complex pipelines
  • Model Management — List classifiers, extractors, model versions, and tags
  • Account Status — Verify API connectivity

How it works

  1. Subscribe to this server
  2. Enter your MonkeyLearn API Key
  3. Start analyzing text from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • Data Scientists — run NLP models without writing code
  • Product Teams — classify user feedback and support tickets
  • Marketers — extract insights from survey responses and reviews
text-classificationentity-extractionsentiment-analysisnlpmachine-learningdata-labeling

12 tools expose this connector's capabilities to your AI agent.

classify_text

Classify text data

extract_text_entities

Extract entities

get_api_status

Get account status

get_classifier_details

Get classifier info

get_extractor_details

Get extractor info

list_classifier_tags

List model tags

list_classifiers

List text classifiers

list_extractor_tags

List extractor tags

list_extractors

List text extractors

list_model_versions

List model versions

list_nlp_workflows

List account workflows

run_workflow

Run NLP workflow

See how to talk to your AI agent using MonkeyLearn.

Classify this customer review: 'The product is amazing but delivery was slow.'

Classification (Classifier: cl_pi3C7nuL): Tag: 'Mixed' (Confidence: 0.82). Sub-tags: 'Product Quality' → Positive (0.95), 'Delivery' → Negative (0.88). Full text analyzed successfully.

Extract entities from: 'John Smith from Apple Inc. visited our NYC office on March 15.'

Extraction (Extractor: ex_YCya9nrn): Person: 'John Smith' (Confidence: 0.97). Organization: 'Apple Inc.' (0.95). Location: 'NYC' (0.92). Date: 'March 15' (0.99). Total entities found: 4.

List all my classifiers and extractors.

Classifiers: 3. 1) 'Sentiment' (cl_pi3C7nuL, Custom, 500 samples). 2) 'Topic Detection' (cl_abc123, Pre-built). 3) 'Intent' (cl_xyz789, Custom, 1200 samples). Extractors: 2. 1) 'Entity Extractor' (ex_YCya9nrn, Pre-built). 2) 'Keyword Extractor' (ex_def456, Custom). Workflows: 1.

Yes. Point to any classifier model ID and pass text to get classification results with confidence scores.

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