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Connect Chainlit 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. Debug and monitor your AI app traffic directly from your chat interface.

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 · 6 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-03

3 capabilities in this set.

Part of 6 available through Chainlit.

  1. 01

    List steps

    This capability pulls the raw programmatic interaction steps, including specific prompts and model generations, from a single thread.

  2. 02

    Get stats

    Use this to fetch project analytics like traffic volume and resource consumption across your native projects.

  3. 03

    Get thread

    This capability retrieves the full payload for a specific conversation to inspect the exact node topology.

Capability set02 / 02

04-06

3 capabilities in this set.

Part of 6 available through Chainlit.

  1. 04

    List feedbacks

    This capability lists user reviews and ratings to help you track conversational accuracy across your deployments.

  2. 05

    List projects

    This capability provides a list of all your globally configured Chainlit Cloud projects.

  3. 06

    List threads

    Use this to find and list all conversational threads within a specific deployed project.

Real work

What teams actually run

01

Production Debugging

An engineer asks the agent to find the specific capability execution that caused a model to hallucinate during a user session.

02

Sentiment Monitoring

A product manager requests a summary of all recent thumbs-down ratings to identify UI or logic issues.

03

Usage Auditing

A developer checks the total token consumption and traffic volume for a specific project to manage costs.

04

Automated QA

A QA tester has the agent scan recent threads to ensure the model is adhering to specific compliance parameters.

Automate a task

Put Chainlit 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

Chainlit 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 Chainlit 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

857ms average. Fast in production.

Chainlit is checked daily against the live service.

Daily averagePeak 1093ms
Oct 3Today
Fastest day
748ms
Slowest day
1093ms
14-day trend
Slowing+18%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 6 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 Chainlit, so you can see the experience inside your AI.

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

Chainlit Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/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 — Chainlit capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "chainlit-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_MmtPPSL0UTrjhdbRcUoAgAc4xQjkgFcCDdx0WVam/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 Chainlit in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Chainlit owners hand off.

This MCP is built for teams managing production AI applications who need to bridge the gap between telemetry and debugging.

  • 01

    AI Developers

    They use the MCP to diagnose model failures by inspecting the exact parameter stack and logic sequences.

  • 02

    Product Teams

    They monitor user sentiment by asking the agent to summarize negative feedback trends.

  • 03

    QA Specialists

    They poll new conversations to check for tone, relevance, and compliance without manual log reading.

FAQ

Questions Chainlit owners ask.

  • 01

    How do I connect my Chainlit projects to this MCP?

    You subscribe via Vinkius and then provide your Chainlit Cloud URL and Project API Key. Once connected, your AI client can immediately start querying your data.

  • 02

    Which AI clients can I use with this MCP?

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

  • 03

    Can I see the specific prompts my model used?

    Yes. By using the list_steps capability, your agent can retrieve the exact prompts and generations used within a specific conversation thread.

  • 04

    Is my data secure?

    Yes. Vinkius hosts the MCP and manages your credentials securely, allowing your agent to interact with your Chainlit Cloud projects safely.

  • 05

    Can I use this to monitor user satisfaction?

    Yes. The list_feedbacks capability allows your agent to pull user thumbs-up/down ratings and textual reviews directly.

  • 06

    Will the AI agent be able to monitor the user interactions and evaluate chat history?

    Yes! The agent can dive into the list_threads and get_thread endpoints to retrieve comprehensive interaction logs from your deployed Chainlit apps. You can essentially command the agent to read past AI chats, summarize usage, or identify edge cases in the user input.

  • 07

    Can it track the individual thought steps and LLM prompt tokens consumed?

    Absolutely. Using the list_steps capability, your agent analyzes the programmatic trace—including specific LLM calls, function blocks, or retrieval events. Thus, identifying hallucinations or latency issues is as easy as typing a prompt.

  • 08

    Is it possible to extract and analyze human feedback scores instantly?

    Yes. The integration provides native capabilities via list_feedbacks to retrieve the explicit thumbs up, down, and textual comments your users left on specific messages, streamlining QA.

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