Connect Gradient AI LLM MCP for AI Agents
Build Complex Data Processing Pipelines with Document Analysis and Fine-Tuning
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What AI agents can do with Gradient AI LLM: 17 Tools for Advanced NLP & Model Development
These tools let your agent manage model lifecycles, process files, generate embeddings, and extract structured data from complex documents.
Analyze sentiment
Determines the emotional tone (positive, negative, neutral) present within a given document.
Answer question
Answers questions based on a source document (Note: This tool name was repeated in the listing data, but is described as answering questions).
Complete model
Predicts and generates the next sequence of text based on an initial prompt, supporting model guidance parameters.
Generate embeddings
Converts input text into a high-dimensional vector representation for use in advanced search or clustering tasks.
Upload file
Prepares files, such as documents or media, by uploading them to the workspace for subsequent operations.
Create model
Creates a new instance of a fine-tuned model using foundational models and provided data.
Create rag collection
Sets up a dedicated collection optimized for Retrieval Augmented Generation (RAG) operations.
Create transcription
Initiates an audio processing job to convert spoken word content into text format.
Delete model
Permanently removes a specific fine-tuned model instance from your workspace.
Extract entity
Pulls structured data, like names or dates, out of a document based on a defined schema.
Extract pdf
Reads and extracts both raw text and specific data points from uploaded PDF files.
Fine tune model
Trains a chosen model on custom samples you provide to boost its performance on niche tasks.
Get model
Retrieves specific details, like status or version information, about an existing fine-tuned model.
Get transcription
Retrieve the result of a transcription job
List embeddings
Shows which models are available for generating embeddings.
List models
Displays a list of all foundational and custom fine-tuned models currently in your workspace.
List rag collections
Fetches a list of every RAG collection you've set up within the workspace.
Personalize document
Modifies an existing document to tailor its content and tone for a specific audience.
Summarize document
Creates a concise summary of the main points found within a large source document.
Frequently Asked Questions
How do I use Gradient AI LLM MCP for complex document Q&A? +
You don't just paste the text in. You first upload your documents, then create a collection using the RAG tools. This indexes the content correctly, allowing your agent to read and answer questions accurately based only on your private materials.
Is Gradient AI LLM MCP useful for analyzing customer feedback? +
Yes. You can use the sentiment analysis tool to automatically sort through thousands of pieces of feedback, giving you a precise count and percentage breakdown of positive, negative, or neutral comments.
What if I need my AI agent to follow a specific company tone? +
You can fine-tune the model using your best existing content. This trains the LLM on your unique style and jargon, making its future generated output sound authentically like your brand.
Can I process audio files with Gradient AI LLM MCP? +
Yes. You start by running a transcription job that converts spoken word into text. This raw transcript can then be passed to other tools, like entity extraction or summarization, for deep analysis.
Does Gradient AI LLM MCP help with data cleanup? +
Absolutely. By using tools like extract_entity and extract_pdf, the system pulls structured data (like names, codes, dates) out of unstructured documents, making it ready for your database.
How can I start training a custom model with my own data? +
You can use the fine_tune_model tool. Simply provide the model ID and an array of training samples. The agent will handle the submission to Gradient's training infrastructure.
Can I use RAG (Retrieval Augmented Generation) with this server? +
Yes! The complete_model tool includes an optional rag parameter, allowing you to provide context or collection IDs to ground the model's responses in specific data.
How do I generate vector embeddings for my documents? +
Use the generate_embeddings tool by specifying a model slug (like 'bge-large') and a list of text inputs. It will return the high-dimensional vectors for your text.
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