Supercharge your AI with MonkeyLearn. Automate Text Classification and Entity Extraction.
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MonkeyLearn analyzes raw text data via custom machine learning models. It classifies sentiment (positive, negative, mixed), pulls structured entities like names, dates, and organizations, and executes multi-step NLP workflows on user feedback or support tickets.
What your AI can do
Classify text
Classifies a given piece of text by determining its sentiment, topic, or intent against your custom models.
Extract text entities
Pulls structured data like names, organizations, and dates from unstructured text input.
Get api status
Checks the current status of your MonkeyLearn account to confirm API connectivity is active.
Determine the emotional tone or subject matter of a block of text using trained classifiers.
Extract specific data points (names, locations, dates) from raw text into structured JSON format.
Run multi-step NLP pipelines—for example, first classifying the topic, then extracting relevant entities—all in one call.
Retrieve details on available classifiers, extractors, and model versions to understand your data capabilities.
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Compatible AI Apps
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MonkeyLearn MCP Server: 12 Tools for NLP Analysis
These twelve tools let your agent perform everything from simple sentiment checks to running multi-step, custom machine learning pipelines on raw text data.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using MonkeyLearn on VinkiusClassify Text
Classifies a given piece of text by determining its sentiment, topic, or intent against your custom models.
Extract Text Entities
Pulls structured data like names, organizations, and dates from unstructured text...
Get Api Status
Checks the current status of your MonkeyLearn account to confirm API connectivity is...
Get Classifier Details
Retrieves detailed information about a specific text classification model you own or...
Get Extractor Details
Gets the full specifications and usage guide for a particular entity extraction tool.
List Classifier Tags
Lists all available tags or labels associated with your text classification models.
List Classifiers
Returns a list of all the sentiment, topic, and intent classifiers you have access to.
List Extractor Tags
Shows available tags for your entity extraction models.
List Extractors
Returns a list of all the entity extractors, like person or address pullers.
List Model Versions
Retrieves historical versions for specific models, allowing you to test older...
List Nlp Workflows
Lists the custom multi-step NLP pipelines (Workflows) you have built in MonkeyLearn...
Run Workflow
Executes a pre-defined, complex NLP workflow using multiple steps and tools on new text data.
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
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Replace [YOUR_TOKEN_HERE] with your token from
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Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
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- Built in DLP, auth, and compliance on every call
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Make Your AI Do More
Start with MonkeyLearn, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 12 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually tagging and reviewing customer feedback is a huge time sink.
Today, if your product manager wants to know what users are complaining about, they download thousands of support tickets. They then open a spreadsheet, manually reading through rows, trying to categorize each one: Is this 'Billing'? Is this 'UI Bug'? They copy-paste keywords into Google Sheets, and the data is always inconsistent.
With MonkeyLearn, you pass that entire dataset to your agent. The server uses `classify_text` to automatically assign sentiment (Negative) and topic (Billing). You don't read a single ticket; you get a clean count: 30% Billing issues, 55% Feature Request, 15% Praise. It’s instant.
MonkeyLearn MCP Server lets you run advanced NLP workflows.
Before this server, running a deep analysis meant coordinating multiple tools: First, call an extractor to get all names. Then, send those names through another tool to find associated companies. You'd have to write custom code just to manage the data flow between these two steps.
Now, you define that entire sequence inside one workflow using `run_workflow`. The agent handles the orchestration automatically. It’s not just running a function; it's running an end-to-end analytical process.
What your AI can actually do with this
Yo, listen up. This server connects your agent straight to MonkeyLearn's deep text analysis engine. You don't just get simple tags; you run custom machine learning models on raw user feedback or support tickets. It handles serious Natural Language Processing (NLP) tasks right through conversation.
Classify Text Sentiment & Topic: To figure out the vibe of a chunk of text, you use classify_text, which classifies it by determining its sentiment, topic, or intent using your custom models. You can check what classification types are available by calling list_classifiers. If you wanna know more about any specific classifier model you're running, get_classifier_details gives you the whole rundown.
Wanna see what tags are attached to those classifiers? Use list_classifier_tags.
Pull Structured Entities: Need names, dates, or company logos pulled out of a messy paragraph? You use extract_text_entities. This tool pulls structured data—like people's names, organizations, and addresses—and spits it back to you in JSON format. To see which types of entity pullers you got, run list_extractors. If you wanna check the specs on a specific extractor, get_extractor_details gives you the full usage guide.
You can also peep available tags for extractors with list_extractor_tags.
Execute Complex Workflows: Don't want to call five different tools? Use run_workflow. This tool executes pre-defined, multi-step NLP pipelines—like first classifying a topic and then pulling all the names mentioned—all in one shot. You can see what complex processes you built up with list_nlp_workflows.
System Management & Status: Before you start running anything, you gotta make sure your connection's good. Use get_api_status to confirm your MonkeyLearn account connectivity is live. To keep track of how models perform, list_model_versions lets you check historical versions for any specific model, so you can test older settings if the new ones are giving you trouble.
Need details on a specific text classification or entity extraction tool? You've already seen get_classifier_details and get_extractor_details. This stack gives you everything you need to analyze raw data.
019dd128-a06c-701b-abe4-8103d990d1e8 
Here's how it actually works
The bottom line is: you feed it raw text, and it gives you clean, actionable insights.
Subscribe to the server and input your MonkeyLearn API Key.
Your AI client sends text and a required tool call (e.g., classify_text) to the MCP Server.
The server runs the model, processes the data, and returns structured findings (tags, entities, or classifications) directly to your agent.
Who is this actually for?
Data Scientists who need to test NLP models without writing Python scripts. Product Managers tasked with summarizing thousands of user support tickets into actionable categories. Marketing Analysts needing to pull key product mentions from large survey datasets.
Runs classification tools on recent customer feedback to identify the top three pain points by sentiment and topic.
Uses list_classifiers and get_classifier_details to audit available NLP models before building a new pipeline with run_workflow.
Passes incoming support tickets through extract_text_entities to automatically pull key details like account numbers, names, and service dates for triage.
What Changes When You Connect
Analyze Feedback Instantly: Instead of manually reading hundreds of support tickets, use classify_text to get an immediate breakdown of sentiment (Positive/Negative) and the core topic. You'll know which issue needs attention before a human even looks at it.
Structure Messy Data: Stop treating key details like names or account numbers as messy text blobs. Use extract_text_entities to pull them into clean, structured JSON objects, ready for database insertion.
Build Complex Logic in One Call: Don't chain 5 different API calls. With run_workflow, you define a multi-step process—like 'Check Topic -> Extract Key Names -> Classify Urgency'—and get the final result in one go.
Audit Your Tools Easily: Before writing anything, use list_classifiers and get_extractor_details. This gives your agent the necessary context to know exactly which models exist for the job, preventing failed calls.
Keep Track of Changes: Use list_model_versions when a classifier updates. You can test older versions side-by-side with new ones, ensuring a model change doesn't break your core business logic.
See it in action
Triage Incoming Support Tickets
A support team gets 50 tickets in an hour. Instead of having agents manually read each one, the agent sends all text to classify_text. It immediately filters the list, telling you which are 'Billing' (Topic) and 'Angry' (Sentiment), allowing priority dispatching.
Processing Survey Data at Scale
Marketing receives 10,000 survey responses. Instead of a spreadsheet analysis, the agent loops through them, using extract_text_entities to pull out every mentioned competitor's name and product SKU, giving immediate competitive intelligence.
Automating Research Summaries
A researcher needs to analyze multiple articles. The agent uses a custom workflow (run_workflow) that first summarizes the text, then runs classify_text on the summary for 'Bias', and finally extracts key figures using extract_text_entities.
Auditing API Functionality
A developer needs to ensure their agent can run a complex pipeline. They first use list_nlp_workflows to see what's available, then call get_api_status to confirm the server connection is active before testing.
The honest tradeoffs
Asking the LLM to do complex data structuring
Just pasting a huge block of text into the agent and asking, 'Tell me the sentiment, topic, and all the names here.' The LLM might hallucinate or miss edge cases.
Don't trust general prompting. Use specific tools. Pass the data through classify_text for reliable sentiment, and then pass the same text to extract_text_entities to get guaranteed structured data.
Building logic with multiple generic calls
Calling list_classifiers, getting a name, then calling get_classifier_details for that name, and repeating this many times. It's slow and messy.
If the process is multi-step (e.g., 'List -> Check Details -> Run'), wrap it into a single workflow using run_workflow. This keeps the logic clean and atomic.
Ignoring model versioning
Relying on a classifier that was fine last month, but now fails because MonkeyLearn updated its underlying model.
Always check list_model_versions before deploying critical pipelines. This lets you rollback to a stable version if performance dips.
When It Fits, When It Doesn't
Use this server when your goal is structured, actionable data derived from raw text. If the task requires classification (sentiment/topic) or extraction of specific entities (names, dates), this is the right place. The key differentiator is that you are building a repeatable, machine-driven process—not just asking a question.
Don't use it if you simply need to generate creative text, summarize ideas vaguely, or perform general chat conversation. For those tasks, stick with standard LLM prompting alone. If your task involves a fixed sequence of data transformations (e.g., 'Find all people -> Check their company -> Classify the industry'), then you absolutely need run_workflow to manage that complexity reliably.
Questions you might have
How do I check if the MonkeyLearn MCP Server is connected? +
You call get_api_status. This tool quickly verifies your API key and connection status, confirming that the server can actually talk to your account before you run any heavy analysis.
Should I use classify_text or run_workflow for topic detection? +
If topic detection is one of several steps in a larger process (e.g., Topic -> Entity Extraction -> Sentiment), use run_workflow. If it's the only thing you need, classify_text is simpler and faster.
What if I want to pull names from an address block? +
Use extract_text_entities. This tool is designed specifically to isolate structured data like Persons, Organizations, or Locations even when they appear mixed into a paragraph of text.
How do I see what classifiers are available? +
You call list_classifiers. This gives you an index of all the topic and sentiment models you can use without having to guess which one is correct or active.
What does the `get_api_status` tool confirm about my account access? +
It verifies that your API key is active and correctly connected. This check confirms general service connectivity before you run intensive jobs, ensuring a quick diagnosis if there are authentication issues.
If I need to revert or debug, how does `list_model_versions` help me find an older classifier or extractor? +
This tool retrieves all historical and current versions of your models. It's essential for debugging when a new deployment breaks analysis because you can pinpoint and select the last known working version ID.
What is the operational difference between calling `classify_text` versus using `run_workflow`? +
classify_text executes a single, defined model check (like sentiment analysis). In contrast, run_workflow triggers complex pipelines built in Studio, allowing data to pass through multiple sequential actions automatically.
How do I see all predefined tags using the `list_classifier_tags` tool? +
The tool retrieves every available tag associated with a specific classifier model. This lets you confirm exactly what labels your trained model recognizes, which is useful before initiating any text analysis jobs.
Can I classify text by sentiment or topic? +
Yes. Point to any classifier model ID and pass text to get classification results with confidence scores.
How does MonkeyLearn authentication work? +
MonkeyLearn uses Authorization: Token {API_KEY} header against api.monkeylearn.com/v3.
Can I extract named entities from text? +
Yes. Use an extractor model to pull keywords, people names, organizations, locations, and more from raw text.
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