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Connect Cognita RAG Framework 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. Manage knowledge bases and data ingestion through natural conversation.

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 · 7 capabilities

We ship only the actions that matter.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Cognita.

  1. 01

    Rag query

    Executes a RAG query to pull specific information from active transformation vectors.

  2. 02

    List collections

    Returns a list of all available RAG collections in your Cognita instance.

  3. 03

    List data sources

    Retrieves a list of all connected data sources and their structural properties.

  4. 04

    Get collection

    Fetches detailed metadata and payload information for a specific collection.

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Cognita.

  1. 05

    Ingest data

    Triggers a data ingestion process to create new resource directories from JSON payloads.

  2. 06

    Search chunks

    Searches through document chunks using specific rules and presets.

  3. 07

    List models

    Shows the LLMs and embedding models currently registered in your Cognita setup.

Real work

What teams actually run

01

RAG Debugging

An engineer uses the agent to search specific chunks to see why a query returned the wrong context.

02

Knowledge Updates

A developer triggers a sync from a GitHub repo to update a technical documentation collection.

03

Pipeline Monitoring

A data scientist checks the model registry to ensure the correct embedding model is being used.

04

Data Auditing

A product manager verifies which external data sources are currently mapped to a specific RAG collection.

Automate a task

Put Cognita 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

Cognita 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 Cognita 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

885ms average. Fast in production.

Cognita is checked daily against the live service.

Daily averagePeak 1293ms
Oct 3Today
Fastest day
680ms
Slowest day
1293ms
14-day trend
Stable-3%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 7 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 Cognita, so you can see the experience inside your AI.

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

Cognita Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/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 — Cognita capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "cognita-rag-framework-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_SHxG8KJRFQw2m3lX2GfeLS7zRF34WrsRFB0j95Nv/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 Cognita in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Cognita owners hand off.

This MCP is built for technical teams managing production or prototype RAG pipelines.

  • 01

    AI Engineers

    They use the agent to test and debug retrieval logic without writing custom scripts.

  • 02

    Data Scientists

    They monitor ingestion pipelines and check chunking consistency.

  • 03

    Product Teams

    They audit the knowledge being fed to agents during the prototyping phase.

  • 04

    DevOps Teams

    They verify that LLM endpoints in the model registry are active and reachable.

FAQ

Questions Cognita owners ask.

  • 01

    What is the Cognita MCP used for?

    It allows you to manage modular RAG workflows, including data ingestion, collection management, and querying, through any MCP-compatible AI client.

  • 02

    Which AI clients can I use with this MCP?

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

  • 03

    Can I trigger data syncs through my AI agent?

    Yes, you can use the ingest_data capability to force sync remote files from SQL, Cloud Storage, or APIs into your vector space.

  • 04

    How do I verify the data being used by my agent?

    You can use the search_chunks capability to pull raw document segments or list_collections to audit your RAG setups.

  • 05

    Do I need to host the MCP myself?

    No, Vinkius hosts and manages the MCP for you. You only need to provide your Cognita credentials to connect.

  • 06

    Can my agent perform semantic RAG queries against my collections?

    Yes. The 'rag_query' capability allows you to ask questions in natural language. The agent queries your vector store via Cognita and uses an LLM to synthesize a final answer based explicitly on the retrieved context.

  • 07

    How can I trigger a data ingestion pipeline through the agent?

    Provide the collection name and the data source FQN (Fully Qualified Name). The 'ingest_data' capability will command the Cognita backend to start a sync, updating your RAG vector space with the latest remote documents.

  • 08

    Can I audit the raw document chunks before LLM generation?

    Absolutely. Use the 'search_chunks' capability to perform vector searches that return raw text segments and metadata without LLM synthesis. This is the perfect way to verify that your retrieval logic is pulling the correct data boundaries.

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