Vinkius

LibreChat MCP. Control your self-hosted agents directly from any client.

LibreChat MCP connects your self-hosted AI instances to any agent client. It lets you manage custom agents, list all available models, and generate chat completions using an OpenAI-compatible interface for your private LLM setups.

LibreChat MCP is compatible with Claude Claude
LibreChat MCP is compatible with ChatGPT ChatGPT
LibreChat MCP is compatible with Cursor Cursor
LibreChat MCP is compatible with Gemini Gemini
LibreChat MCP is compatible with Windsurf Windsurf
LibreChat MCP is compatible with VS Code VS Code
LibreChat MCP is compatible with JetBrains JetBrains
LibreChat MCP is compatible with Vercel Vercel
See Vinkius in Action

Give Claude and any AI agent real-world access

List Available Models

You can ask the MCP for a full list of every agent and model configured in your LibreChat instance.

Run Chat Completions

The MCP sends messages to specific agents, generating responses that mimic standard chat completion APIs.

Generate Structured Responses

You prompt the agent for output and receive a highly structured response using the Open Responses API format.

Manage Authentication

The MCP allows you to log in directly with your email and password, obtaining required access tokens without needing static API keys.

Waiting for input…

AI Agent
LibreChat

What AI agents can do with LibreChat: 4 Tools for Agent Operations

These tools let you manage authentication, list available agents, send chat prompts, and force structured data output directly from your self-hosted LibreChat environment.

Make your AI actually useful.

Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.

Start using LibreChat MCP

Chat Completions

Creates a chat completion using the Agents API, allowing you to send prompts and get model responses.

List Models

Retrieves a list of all available models and agents configured in your LibreChat...

Login

Authenticates you with your credentials, retrieving necessary access and refresh...

Open Responses

Generates a structured AI output using the Open Responses API specification.

Security and governance baked right in.

Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.

LibreChat MCP is compatible with Claude

Claude AI

1

Open Claude Settings

Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.

2

Add Custom Connector

Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:

https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp

Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. For OAuth-protected servers, expand Advanced settings to add credentials.

3

Start a conversation

Open a new chat. The LibreChat integration is available immediately — no restart needed.

Choose How to Get Started

Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.

Build Your Own

Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.

  • Import from OpenAPI, Swagger, or YAML specs
  • Create Agent Skills with progressive disclosure
  • Deploy to edge with MCPFusion framework
  • Built in DLP, auth, and compliance on each call
  • Real time usage dashboard and cost metering
  • Publish to catalog or keep private
Start building

Make Your AI Do More

Start with LibreChat, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.

  • Use this MCP plus 5,200+ others, all in one place
  • Add new capabilities to your AI anytime you want
  • Connections are secured and governed automatically
  • Track usage and costs across all your servers
  • Works with Claude, ChatGPT, Cursor, and more
  • New servers added to the catalog weekly
LibreChat MCP server cover

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LibreChat. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

VINKIUS CLOUD

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on each call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

The pain of model sprawl

Today, getting your agent client to talk to different private models is a nightmare. You have to jump between five different dashboards, copy-pasting keys and unique endpoints every time you want to test something new or run a report.

With this MCP, you plug it in once. Your agent client talks directly to the LibreChat MCP. You simply list your agents using `list_models`, then send all your prompts through one unified API call, regardless of which underlying model actually answers.

LibreChat MCP: Accessing Every Model

You eliminate the manual steps of finding agent IDs, managing disparate keys, and writing wrapper code for every unique LLM endpoint you use.

The result is simple: your agents feel like they live in one place. You get instant, programmatic access to your entire private model catalog.

What LibreChat MCP does for your AI

Connecting your own LibreChat environment through this MCP gives your AI client direct control over your private language model ecosystem. Instead of relying on a single provider's features, you can treat your self-hosted agents like any other tool right inside Claude or Cursor. You first use the login tool to authenticate with your credentials and grab necessary access tokens.

Once authenticated, you can list everything available using list_models. After that, you send requests via chat_completions to run chats against specific agents. Need structured data? The open_responses tool handles that too. This makes building complex agent workflows simple, letting your AI client talk directly to the models you set up yourself.

It’s a solid way to centralize access to multiple private LLMs through one secure interface on Vinkius.

Built · Hosted · Managed by Vinkius LibreChat MCP - Manage Private LLMs & Agents
Server ID 019e38b7-ad28-7040-9c37-38d0f1a714b2
Vinkius Inspector
Compliance Grade A+
Score 100/100
Vinkius Inspector Badge — Score 100/100

Frequently asked questions about LibreChat MCP

How do I get started with the LibreChat MCP? +

You start by subscribing to the MCP and running the login tool using your credentials. This authenticates you and gives your agent client the necessary tokens for subsequent calls.

Can I use chat_completions with agents that aren't OpenAI-compatible? +

Yes, that is the point of this MCP. It provides an OpenAI-compatible interface layer over your self-hosted LibreChat environment, making diverse models appear uniform to your client.

Is there a way to check which agents are active in my LibreChat instance? +

Use the list_models tool. It provides an immediate overview of all available agents and model configurations, saving you from guessing IDs.

What if I need the output data to be perfectly formatted JSON? +

For guaranteed structure, use the open_responses tool. This API is specifically designed to force your agent's response into a predictable format that downstream systems can read.

Does this MCP require me to modify my core AI client code? +

No. You connect the LibreChat MCP through your existing compatible client (Claude, Cursor, etc.). The tool handles the complex routing and API translation for you.