Gradient AI (LLM API & Finetuning) Connector for AI agents.

19 live capabilities

Manage fine-tuned models and RAG collections for custom LLM infrastructure.

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AI Agent

Why people use Gradient AI (LLM API & Finetuning)

Gradient AI for LLM Fine-Tuning and Model Management

This Connector changes that by giving your agent direct control over the Gradient AI infrastructure. You can start a fine-tuning job, check on transcription results, or manage your RAG collections with simple commands. It moves the complexity of model management into the background so you can focus on the actual product.

What Vinkius changes

You get a direct pipeline to Gradient AI's infrastructure without leaving your AI client.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

Real-world use case 01

Building a customer support bot

A dev needs to answer questions from a 200-page manual.
Real-world use case 02

Sentiment analysis at scale

A marketing lead wants to know how people feel about a product launch.
Real-world use case 03

Training a niche model

An engineer needs a model that speaks in a very specific brand voice.

Complete set · 19capabilities

The complete Gradient AI (LLM API & Finetuning) capability set.

These are the exact actions your AI can choose when you ask it to work with Gradient AI (LLM API & Finetuning).

Capability set01 / 05

01—04

4 capabilities in this set.

Part of 19 available through Gradient AI (LLM API & Finetuning).

01 Capability
Analyze sentiment
Tells you the emotional tone of a document. It's useful for categorizing customer feedback at scale.
02 Capability
Answer question
Pulls specific answers from a source document. Use this for building Q&A bots on your internal docs.
03 Capability
Complete model
Generates text based on a prompt. It handles the core task of getting a response from your chosen model.
04 Capability
Fine tune model
Trains a model on a specific set of samples. Use this to teach the model niche behaviors or styles.

Capability set02 / 05

05—08

4 capabilities in this set.

Part of 19 available through Gradient AI (LLM API & Finetuning).

05 Capability
Generate embeddings
Turns text into high-dimensional vectors. This is the standard way to prepare data for similarity search.
06 Capability
Get model
Shows the details of a specific model. Use this to check parameters or status during an experiment.
07 Capability
Get transcription
Checks the results of a transcription job. Use this to see if your audio has been successfully processed.
08 Capability
List embeddings
Shows which models you can use for embeddings. This helps you pick the right vector size for your project.

Capability set03 / 05

09—12

4 capabilities in this set.

Part of 19 available through Gradient AI (LLM API & Finetuning).

09 Capability
List models
Shows all foundational and fine-tuned models. It's the quickest way to see what's currently available to you.
10 Capability
List rag collections
Shows all RAG collections in your workspace. Use this to manage multiple knowledge bases at once.
11 Capability
Personalize document
Rewrites a document for a specific audience. It helps tailor content for different user segments.
12 Capability
Summarize document
Creates a short summary of a long document. It saves time when you need to digest large amounts of text.

Capability set04 / 05

13—16

4 capabilities in this set.

Part of 19 available through Gradient AI (LLM API & Finetuning).

13 Capability
Upload file
Sends a file to the cloud for processing. It's the first step for any operation involving PDFs or large docs.
14 Capability
Create model
Sets up a new fine-tuned model instance. Use this to start a new training run with specific parameters.
15 Capability
Create rag collection
Builds a collection for RAG operations. It organizes your data so your agent can find relevant info quickly.
16 Capability
Create transcription
Starts a job to turn audio into text. It handles the heavy lifting of speech-to-text processing.

Capability set05 / 05

17—19

3 capabilities in this set.

Part of 19 available through Gradient AI (LLM API & Finetuning).

17 Capability
Delete model
Removes a fine-tuned model from your workspace. Use this to keep your environment clean of old experiments.
18 Capability
Extract entity
Pulls structured data out of a document based on a schema. It turns messy text into clean, usable fields.
19 Capability
Extract pdf
Pulls text and data out of PDF files. It's the primary way to ingest static documents into your system.

Set up in minutes

One URL. Then ask Gradient AI (LLM API & Finetuning) to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Gradient AI (LLM API & Finetuning) from the conversation.

Choose your client

Live preview

Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning), and paste the URL above.
Step 03

Turn it on in chat

Select +, open Connectors, and enable Gradient AI (LLM API & Finetuning) for the conversation.

ChatGPT · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP

https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning) URL.
Step 03

Save and start

Save the connection and enable Gradient AI (LLM API & Finetuning) in your conversation. Desktop may ask you to restart once.

Cursor · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "gradient-ai-llm-api-finetuning": {
      "url": "https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning)

Open Agent mode in chat and ask: "Using Gradient AI (LLM API & Finetuning), help me...". 19 tools available

VS Code Copilot · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "gradient-ai-llm-api-finetuning": {
      "url": "https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning)

Ask Copilot: "Using Gradient AI (LLM API & Finetuning), help me...". 19 tools available

Windsurf · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "gradient-ai-llm-api-finetuning": {
      "url": "https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning)

Open Cascade and ask: "Using Gradient AI (LLM API & Finetuning), help me...". 19 tools available

Cline · IDE configuration

Official guide ↗

Advanced setup

{
  "mcpServers": {
    "gradient-ai-llm-api-finetuning": {
      "url": "https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning)

Ask Cline: "Using Gradient AI (LLM API & Finetuning), help me...". 19 tools available

Claude Code · Terminal command

Official guide ↗

Advanced setup

claude mcp add gradient-ai-llm-api-finetuning --transport http "https://edge.vinkius.com/vk_preview_KwJfHerXBfsWKXVQH8bGJG4HGUwUfVlRXkPpGPNU/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 Gradient AI (LLM API & Finetuning)

Ask Claude: "Using Gradient AI (LLM API & Finetuning), show me...". 19 tools are ready

Where the request belongs

Work Gradient AI can move forward.

Built around the request

This is for the engineer who's tired of manual data labeling or the data scientist who needs to scale embedding generation without building a custom pipeline.

01

AI Engineer

Iterates on fine-tuning experiments and manages model versions to deploy production-ready LLM features.

02

Data Scientist

Generates high-dimensional embeddings and performs large-scale NLP analysis without local setup.

03

Full Stack Developer

Integrates advanced capabilities like transcription and entity extraction into applications with minimal friction.

Build the capability set

Each Connector adds new actions and data without changing how you work.

Browse Connectors

01 4 capabilities

DeepInfra (Serverless LLM Inference)

Run top-tier LLMs, image generation, and embeddings via DeepInfra's serverless infrastructure directly from your AI agent.

02 7 capabilities

Together AI

Generate code, evaluate embeddings, and deploy open-source LLMs instantly from your local agent via Together AI's infrastructure.

03 7 capabilities

Mistral AI (Frontier LLMs & Embeddings)

Manage AI inference via Mistral. execute chat completions, generate RAG embeddings, and audit frontier models.

04 10 capabilities

OpenAI

Use GPT-4o, DALL-E 3, embeddings, fine-tuning, and moderation as capabilities inside your AI agent workflows.

05 5 capabilities

Cohere (AI Platform)

Power enterprise AI via Cohere. generate text, perform chat completions, reorder documents, and manage embeddings directly from any AI agent.

06 13 capabilities

Eden AI

Access 100+ AI models through a single API. route LLMs, generate embeddings, and execute specialized AI tasks like OCR and translation.

Bring your own AI

Change the model, client or framework. Keep Gradient AI connected.

Before you connect

Questions about Gradient AI.

The practical details behind the request, access and result.

Can I use the Gradient AI MCP to train models on my own data?

Yes, you can use the fine_tune_model capability to train models on your specific datasets. This allows your agent to handle the training process and manage the resulting models directly within your workspace.

How does the Gradient AI MCP handle PDF files?

The Connector includes a capability to extract text and data from PDFs. Your agent can pull the content out so you can then summarize it, ask questions about it, or extract specific entities.

Can I use this Connector to build a RAG system?

Absolutely. You can create and manage RAG collections using the Connector. This makes it easy to set up a knowledge base for your agent to query during conversations.

Does the Gradient AI MCP support audio transcription?

Yes, it can start transcription jobs and retrieve the results. This is great for turning meetings or interviews into searchable text automatically.

How do I manage my fine-tuned models with the Gradient AI MCP?

You can list all your models to see what's available, get specific details on any model, or delete old versions. It gives you full control over your model library.

Can the Gradient AI MCP do sentiment analysis?

Yes, it has a dedicated capability for analyzing the sentiment of documents. Your agent can use this to quickly gauge the tone of customer feedback or reviews.

How can I start training a custom model with my own data?

You can use the fine_tune_model capability. 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 capability 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 capability by specifying a model slug (like 'bge-large') and a list of text inputs. It will return the high-dimensional vectors for your text.

One connection away

Give your agent a direct line to Gradient AI.

Connect Gradient AI once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available