MCP For Claude Marketplace
MonkeyLearn

MonkeyLearn MCP Connector for Claude

A+

Automate text analysis via MonkeyLearn — classify sentiment, extract keywords, and run custom NLP pipelines directly from any AI agent.

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

Connect MonkeyLearn to your AI agent to perform advanced Natural Language Processing (NLP) tasks through simple conversation.

What you can do

  • Text Classification — Categorize text into labels like Sentiment, Topic, or Intent using pre-trained or custom classifiers via the classify_text tool.
  • Data Extraction — Automatically pull specific entities, keywords, or names from unstructured text using the extract_data tool.
  • Workflow Pipelines — Chain multiple classification and extraction steps into a single automated process using the run_pipeline tool.

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 Analysts — quickly process large volumes of feedback or support tickets without writing scripts.
  • Marketing Teams — monitor brand sentiment and extract trending topics from social media or reviews.
  • Developers — integrate NLP capabilities into their workflow without managing complex machine learning infrastructure.
nlptext-analysissentiment-analysiskeyword-extractionmachine-learning

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

classify_text

g., Sentiment Analysis, Topic Classification) using a specific MonkeyLearn classifier model. Classify text into predefined labels

extract_data

Extract specific data from text

run_pipeline

Run a MonkeyLearn pipeline on text

See how to talk to your AI agent using MonkeyLearn.

Classify the sentiment of these reviews using model cl_pi3C7JiL: ['I love this!', 'It was okay', 'Terrible experience']

I've analyzed the reviews. The first is classified as 'Positive' (98% confidence), the second as 'Neutral' (75% confidence), and the third as 'Negative' (99% confidence).

Extract keywords from this text using model ex_y7BPYzNG: 'The new smartphone features a high-resolution OLED screen and 5G connectivity.'

I've extracted the following keywords: 'smartphone', 'high-resolution OLED screen', and '5G connectivity'.

Run the support pipeline p_12345 on this customer email to categorize and extract the order ID.

The pipeline has processed the email. Category: 'Shipping Issue'. Extracted Order ID: 'ORD-99283'.

Yes. The `classify_text` tool accepts an array of strings in the `texts` parameter, allowing you to process multiple entries simultaneously for better efficiency.

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