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ClaudeChatGPTPerplexityGeminiMicrosoft CopilotRaycastMeta AIGrokZ.aiQwenKimi
DeepSeekMistralCursorVS CodeWindsurfJetBrainsClineLovableVercel AI SDKLangChain

Use Cognita with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Manage modular RAG via Cognita. list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.

Included with plan

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

Complete set · 7 capabilities

The complete Cognita capability set.

These are the exact actions your AI can choose when you ask it to work with Cognita.

Capability set01 / 02

01-04

4 capabilities in this set.

Part of 7 available through Cognita.

  1. 01

    Ingest data

    Provision a highly-available JSON Payload generating new Resource directories

  2. 02

    Rag query

    Identify precise active arrays spanning rented Transformation vectors

  3. 03

    List data sources

    Perform structural extraction of properties driving active Buckets

  4. 04

    List models

    Inspect deep internal arrays mitigating specific Picture constraints

Capability set02 / 02

05-07

3 capabilities in this set.

Part of 7 available through Cognita.

  1. 05

    Search chunks

    Enumerate explicitly attached structured rules exporting active Presets

  2. 06

    Get collection

    Retrieve explicit Cloud logging tracing explicit Payload IDs

  3. 07

    List collections

    Identify bounded routing spaces inside the Headless Cognita RAG limit

Observed, not estimated

841ms average. Fast in production.

Cognita is checked daily against the live service.

Daily averagePeak 1035ms
Aug 20Today
Fastest day
662ms
Slowest day
1035ms
14-day trend
Slowing+19%

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

FAQ

Questions Cognita owners ask.

  • 01

    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.

  • 02

    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.

  • 03

    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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