MonkeyLearn Connector for AI agents.
3 live capabilities
Automate sentiment analysis and data extraction from messy text.
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Why people use MonkeyLearn
MonkeyLearn for Sentiment Analysis and Text Categorization
With this Connector, you just tell your agent to run a classification. It processes the text through trained models and returns a structured list of categories and scores. You go from manual data entry to having a clean dashboard in seconds.
What Vinkius changes
You get structured data from messy text without writing a single line of ML code.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Social Media Triage
A brand manager asks the agent to find all negative tweets and extract the specific complaints to identify product flaws.
- Real-world use case 02
Support Ticket Tagging
A support lead has the agent categorize 500 emails by Shipping Issue vs Billing to prioritize the support queue.
- Real-world use case 03
Review Summarization
A product owner asks the agent to summarize 100 Amazon reviews into a list of top keywords to identify common pain points.
Complete set · 3capabilities
The complete MonkeyLearn capability set.
These are the exact actions your AI can choose when you ask it to work with MonkeyLearn.
01—03
3 capabilities in this set.
Part of 3 available through MonkeyLearn.
- 01 Capability
Classify text
Assign labels like sentiment or topic to text using specific MonkeyLearn classifier models. It helps you categorize large amounts of feedback quickly.
- 02 Capability
Extract data
Pull out specific pieces of information, like names or dates, from unstructured text blocks. This turns messy conversations into structured data you can actually use.
- 03 Capability
Run pipeline
Run a multi-step MonkeyLearn pipeline on a block of text. This allows you to perform complex, chained analysis in one go.
Set up in minutes
One URL. Then ask MonkeyLearn to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use MonkeyLearn from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it MonkeyLearn, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable MonkeyLearn for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the MonkeyLearn URL.
- Step 03
Save and start
Save the connection and enable MonkeyLearn in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"monkeylearn-alternative-1": {
"url": "https://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using MonkeyLearn
Open Agent mode in chat and ask: "Using MonkeyLearn, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"monkeylearn-alternative-1": {
"url": "https://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using MonkeyLearn
Ask Copilot: "Using MonkeyLearn, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"monkeylearn-alternative-1": {
"url": "https://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using MonkeyLearn
Open Cascade and ask: "Using MonkeyLearn, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"monkeylearn-alternative-1": {
"url": "https://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using MonkeyLearn
Ask Cline: "Using MonkeyLearn, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add monkeylearn-alternative-1 --transport http "https://edge.vinkius.com/vk_preview_hyDDEZ4vXmuIKhDYNNvrmR8MIwEJKQ8m0bZeKHs6/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using MonkeyLearn
Ask Claude: "Using MonkeyLearn, show me...". 3 tools are ready
Where the request belongs
Work MonkeyLearn can move forward.
This is for the marketing manager drowning in social media comments, the support lead trying to prioritize tickets, or the data analyst who needs to turn qualitative feedback into quantitative charts.
Marketing Manager
Analyzing brand sentiment across hundreds of social posts to see what people actually think.
Customer Support Lead
Automatically tagging incoming emails so the right team gets the right tickets first.
Data Analyst
Converting thousands of open-ended survey responses into a clean spreadsheet of categories and keywords.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
Browse ConnectorsMonkeyLearn
Analyze text data with custom machine learning models that classify sentiment, extract keywords, and tag topics automatically.
MeaningCloud
Advanced text analytics for sentiment analysis, topic extraction, language detection, and automatic summarization.
DeepOpinion (No-code NLP & Text AI API)
Automate NLP and text analysis with DeepOpinion. list custom models, run single predictions, and process text batches directly from your AI agent.
Deep Talk
Equip your AI agent to analyze conversation datasets, extract topics, and monitor sentiment via the Deep Talk API.
NLP Cloud
High-performance NLP API for text summarization, entity extraction, classification, sentiment analysis, ASR, and translation.
TextRazor
Advanced Natural Language Processing (NLP) to extract entities, topics, and relations from text or URLs.
Bring your own AI
Change the model, client or framework. Keep MonkeyLearn connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about MonkeyLearn.
The practical details behind the request, access and result.
Can I use MonkeyLearn MCP to analyze my customer reviews?
Yes, you can use it to automatically categorize reviews by sentiment, topic, or intent. It helps you turn a mountain of text into organized data you can use for reports.
Does the MonkeyLearn MCP work for sentiment analysis?
It is specifically designed for sentiment analysis. It uses trained models to provide consistent scores that you can rely on for brand monitoring.
How does the MonkeyLearn MCP help with marketing data?
It helps you extract keywords and identify trends from social media posts or marketing copy, allowing you to see what topics are resonating with your audience.
Can I use MonkeyLearn MCP to extract specific info from emails?
Yes, you can use it to pull out specific details like order IDs, names, or dates from unstructured emails, saving you from manual data entry.
Is the MonkeyLearn MCP good for high-volume text processing?
It is built for scale. It allows your AI agent to process thousands of rows of feedback quickly and consistently without manual intervention.
Can I run multiple NLP steps at once with MonkeyLearn MCP?
Yes, you can chain multiple steps together. For example, you can have it classify an email by intent and then extract specific data points in one single request.
Can I classify multiple pieces of text in a single request?
Yes. The classify_text capability accepts an array of strings in the texts parameter, allowing you to process multiple entries simultaneously for better efficiency.
How do I extract specific entities like keywords or names?
Use the extract_data capability with a specific Extractor Model ID. It will parse your text and return the structured entities found based on that model's configuration.
Can I run a sequence of different NLP models at once?
Yes, by using the run_pipeline capability. Pipelines in MonkeyLearn allow you to chain classifiers and extractors together into a single workflow identified by a Pipeline ID.
One connection away
Give your agent a direct line to MonkeyLearn.
Connect MonkeyLearn once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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