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Connect Forefront to ChatGPT, Claude and Gemini

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Run completions and fine-tuning jobs through your AI agent.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 10 capabilities

We ship only the actions that matter.

Capability set01 / 03

01-04

4 capabilities in this set.

Part of 10 available through Forefront.

  1. 01

    List pipelines

    This capability returns a full list of your current pipelines.

  2. 02

    Create chat completion

    This capability generates a model response using chat-ml formatted messages.

  3. 03

    Create completion

    Use this to get a response from a single prompt string.

  4. 04

    Create fine tune

    This capability initiates a custom fine-tuning job on base models.

Capability set02 / 03

05-07

3 capabilities in this set.

Part of 10 available through Forefront.

  1. 05

    Get pipeline count

    This capability checks the total count of a pipeline selection.

  2. 06

    Get pipeline samples

    Use this to pull data samples from a specific pipeline.

  3. 07

    Create pipeline dataset

    This capability builds a new dataset based on a specific pipeline selection.

Capability set03 / 03

08-10

3 capabilities in this set.

Part of 10 available through Forefront.

  1. 08

    Add pipeline data

    This capability adds new data samples to an existing pipeline.

  2. 09

    Create pipeline

    Use this to start a new pipeline for collecting LLM outputs.

  3. 10

    Get pipeline

    This capability retrieves a specific pipeline object using its ID.

Real work

What teams actually run

01

Rapid Prototyping

Test different model prompts and completions directly in your coding environment.

02

Model Optimization

Start a fine-tuning job when a base model fails to meet specific requirements.

03

Data Logging

Set up pipelines to automatically collect and organize model responses for later analysis.

04

Dataset Building

Turn collected pipeline data into structured datasets for future training.

Automate a task

Put Forefront to work on one thing.

Step-by-step automations your AI runs through this Connector: the workflow, the prompts to start with, and the exact capabilities behind each move.

One connector, every AI

Forefront works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put Forefront to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

980ms average. Fast in production.

Forefront is checked daily against the live service.

Daily averagePeak 1509ms
Oct 3Today
Fastest day
785ms
Slowest day
1509ms
14-day trend
Slowing+12%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 10 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of Forefront, so you can see the experience inside your AI.

It does not authenticate your account with Forefront. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

Forefront Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_9p3sy2z5j8vwdMtOomVYymmFfYsP4YghbaGI6v8C/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — Forefront capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "forefront-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_9p3sy2z5j8vwdMtOomVYymmFfYsP4YghbaGI6v8C/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up Forefront in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Forefront owners hand off.

This MCP is built for developers and AI engineers who need to bridge the gap between model training and active inference.

  • 01

    AI Engineers

    They hand off fine-tuning tasks and pipeline management to their agent.

  • 02

    LLM Developers

    They use it to test completions and organize model output data.

  • 03

    Data Scientists

    They manage datasets and training jobs through a conversational interface.

FAQ

Questions Forefront owners ask.

  • 01

    Which AI clients can I use with this MCP?

    You can use this MCP with any compatible client, including Claude, Cursor, and Windsurf.

  • 02

    Do I need to provide my own API key?

    Yes, you will need to enter your Forefront API Key to authenticate the connection.

  • 03

    Can I run fine-tuning jobs through my agent?

    Yes, you can use the create_fine_tune capability to start custom training jobs on base models.

  • 04

    How do I collect model outputs?

    You can use the pipeline capabilities to create a pipeline and add data samples to it.

  • 05

    Is the hosting managed?

    Yes, Vinkius hosts and manages the MCP so it is ready to use immediately after connection.

  • 06

    How can I generate a model response using a chat conversation?

    You can use the create_chat_completion capability. Provide the model name and an array of messages in chat-ml format to receive the generated response.

  • 07

    Can I start a custom fine-tuning job directly from my agent?

    Yes! Use the create_fine_tune capability by specifying the name of your fine-tuned model, the baseModel, and the trainingDataset ID to begin training.

  • 08

    How do I collect LLM outputs using pipelines?

    First, create a pipeline using create_pipeline. Once created, you can use add_pipeline_data to log messages, user IDs, and custom metadata directly into that pipeline.

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