# MonkeyLearn Alternative MCP > MonkeyLearn Alternative handles advanced text analysis directly through your AI agent. It classifies sentiment, pulls specific data points from messy documents, and chains these steps into automated pipelines. Feed it raw text—be it customer feedback or social media comments—and get structured insights instantly, without writing any ML code. ## Overview - **Category:** marketing-automation - **Price:** Free - **Tags:** nlp, text-analysis, sentiment-analysis, keyword-extraction, machine-learning ## Description When you feed your AI client raw text—support tickets, social media sludge, product reviews—you don't want a pile of words; you want data. This MCP Server handles that advanced Natural Language Processing right inside your agent. You just point it at the mess, and it spits out structured insights without needing any ML code or complicated setup. *** **`classify_text`**: **Categorize Text Sentiment or Topic** You use `classify_text` to categorize whatever text you throw at it. It takes an input string and assigns predefined labels—you know, like 'Positive,' 'Negative,' or maybe a specific topic like 'Billing Issue' or 'Shipping Inquiry.' This tool relies on models trained either by you or pre-configured in the system; it doesn't guess. You tell it what to look for, and it nails down whether that text is genuinely positive, deeply negative, or if it’s about something entirely different. It tells your agent *what* the text means at a high level. **`extract_data`**: **Pull Structured Data from Documents** Need to grab specific details out of a massive chunk of unstructured text? That's what `extract_data` does. You feed it random passages, and this tool acts like a digital vacuum cleaner, pulling out precise data points. It pulls keywords, full names, unique IDs—think order numbers or product model numbers—and any other specific entity you define. It doesn't just tell you something is there; it extracts the actual value so your agent can use it immediately for records or follow-up actions. **`run_pipeline`**: **Execute Multi-Step NLP Workflows** When you have a complex problem, you don’t want to manually chain tools together. Use `run_pipeline`. This tool is the workflow engine: it links classification steps and extraction calls into one single command. Instead of running A, waiting for the output, then feeding that output into B, this server processes the whole thing sequentially in one go. You can process complicated data by having multiple NLP steps run one after the other—like classifying the sentiment *and* extracting the order ID—all without telling your agent to wait between steps. It handles the entire data journey from start to finish. *** This server turns chaotic, unstructured text into labeled records that your AI client can act on. You just subscribe it on Vinkius and connect it to your preferred AI client; your agent handles calling all these necessary tools for you. ## Tools ### classify_text Categorizes input text into predefined labels, such as sentiment or topic, using a specified MonkeyLearn classification model. ### extract_data Pulls specific data points, like names or IDs, from unstructured text based on defined extraction models. ### run_pipeline Executes a predefined sequence of NLP steps (e.g., classify then extract) on a given text input. ## Prompt Examples **Prompt:** ``` Classify the sentiment of these reviews using model cl_pi3C7JiL: ['I love this!', 'It was okay', 'Terrible experience'] ``` **Response:** ``` 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). ``` **Prompt:** ``` Extract keywords from this text using model ex_y7BPYzNG: 'The new smartphone features a high-resolution OLED screen and 5G connectivity.' ``` **Response:** ``` I've extracted the following keywords: 'smartphone', 'high-resolution OLED screen', and '5G connectivity'. ``` **Prompt:** ``` Run the support pipeline p_12345 on this customer email to categorize and extract the order ID. ``` **Response:** ``` The pipeline has processed the email. Category: 'Shipping Issue'. Extracted Order ID: 'ORD-99283'. ``` ## Capabilities ### Categorize Text Sentiment or Topic The `classify_text` tool assigns predefined labels—like 'Positive,' 'Negative,' or 'Billing Issue'—to raw text based on a specified model. ### Pull Structured Data from Documents Using `extract_data`, the server pulls specific keywords, names, IDs, and entities out of any chunk of unstructured text. ### Execute Multi-Step NLP Workflows The `run_pipeline` tool chains classification and extraction calls into a single command. It processes complex data by running several steps sequentially in one go. ## Use Cases ### Analyzing Customer Support Feedback A support team receives 200 daily emails. Instead of having an analyst read every one, they prompt their agent to run a pipeline: 'Run the support pipeline on these emails.' The server uses `classify_text` to tag them ('Billing Issue') and then `extract_data` to pull out the relevant Order ID, giving immediate counts for triage. ### Monitoring Brand Health from Reviews A marketing team wants a quick pulse on a new product launch. They feed in 500 social media comments and ask their agent to use `classify_text`. The result instantly breaks down the mood: 'Positive' (45%), 'Negative' (30%), 'Neutral' (25%)—highlighting areas of concern. ### Onboarding Data into a System A developer needs to ingest unstructured vendor contracts. They use `run_pipeline` on the contract text. The pipeline first classifies the document type, then uses `extract_data` to guarantee they pull out legal dates and signatory names in a clean JSON format for database entry. ### Filtering Research Papers A researcher dumps 10 technical papers into their agent. They instruct the agent to use `classify_text` to filter them down by 'Topic: Quantum Computing' and then run `extract_data` on the remaining texts to gather all cited model numbers for a bibliography. ## Benefits - Automate classification: Instead of manually reading hundreds of reviews to find the mood, run `classify_text` on a batch. It returns precise sentiment scores and topic labels for every single piece of feedback. - Pinpoint key details: Stop searching through massive text blocks for one ID number. Use `extract_data` to reliably pull specific entities—like order numbers or product names—and format them instantly. - Build complex workflows easily: Don't write a multi-step script. The `run_pipeline` tool chains classification and extraction so you process data end-to-end with one command. - Scale your insights: This server handles volume. You feed it hundreds of support tickets, and the system processes them all at once, giving you aggregated reports on topics and sentiments. - Stay focused on data, not plumbing: You get robust NLP capabilities without managing ML infrastructure or dealing with complex API orchestration. ## How It Works The bottom line is: you provide the text and the goal; the server handles the complex ML calls and gives you clean JSON output. 1. Subscribe to the MonkeyLearn Alternative server and provide your API key. 2. Tell your AI client (e.g., 'Run the support pipeline on these emails...') 3. The agent invokes the appropriate tool (`classify_text`, `extract_data`, or `run_pipeline`) with the text data, processes it using the external models, and returns structured results. ## Frequently Asked Questions **How does MonkeyLearn Alternative MCP Server classify text?** It uses the `classify_text` tool to categorize input. You specify which type of classification you need (like Sentiment or Topic), and it runs that task against its available models, returning a label and confidence score. **Can I use MonkeyLearn Alternative MCP Server for data extraction?** Yes. Use the `extract_data` tool to pull specific pieces of information—names, IDs, dates—from any messy text block. It guarantees you get structured output. **What is the difference between `classify_text` and `run_pipeline`?** `classify_text` does one job: labeling. `run_pipeline` runs multiple jobs in sequence, for example, it might first classify the text, *then* use the classification result to inform what data needs extracting. **Does MonkeyLearn Alternative MCP Server handle large volumes of text?** Absolutely. The tools are designed for batch processing. You feed them a list or chunk of data (like 500 reviews), and they process the entire set rather than just single inputs. **What credentials are required to use the `classify_text` tool?** You must supply a valid MonkeyLearn API key. This key authenticates your connection and grants access to the specific pre-trained or custom classifiers you select. **Are there rate limits when I run complex operations with `run_pipeline`?** Yes, standard API usage limits apply. The server adheres to the established MonkeyLearn rate structure. Check the official documentation for current quotas and best practices for managing high-volume calls. **What is the optimal format when I use `extract_data`?** Passing plain, unstructured text strings works best. While you can pass varied data, remember that the quality of extracted entities depends entirely on the clarity and structure of the source material. **Does the server only work with specific AI clients? (Compatibility)** No, because this is an MCP server, it connects to any compatible agent. It functions across all major platforms—including Claude, Cursor, and VS Code—that adhere to the Model Context Protocol. **Can I classify multiple pieces of text in a single request?** Yes. The `classify_text` tool 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` tool 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` tool. Pipelines in MonkeyLearn allow you to chain classifiers and extractors together into a single workflow identified by a Pipeline ID.