Supercharge your AI with Cognita. Control every step of your knowledge retrieval workflow.

Q: Can my agent perform semantic RAG queries against my collections?

Yes. The 'ragquery' tool 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.

Q: Can I audit the raw document chunks before LLM generation?

Absolutely. Use the 'searchchunks' tool 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.

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

Connect to your AI in seconds.

Cognita (RAG Framework) lets you manage complex knowledge retrieval systems without writing Python code. Inspect data sources, ingest files from SQL or cloud storage, and run automated questions against your private knowledge base directly from any AI client.

What your AI can do

Rag query

Identifies precise, active arrays by querying the vector store with a user-defined prompt.

List models

Inspects deep internal arrays to enumerate all available LLMs and embedding models registered in the system.

List collections

Identifies and lists all bounded routing spaces available within your Headless Cognita RAG setup.

+ 4 more capabilities included

Audit Knowledge Collections

List and inspect the metadata of different knowledge collections to check embedding configurations and token limits.

Update Data Sources

Force synchronization of remote files from various sources like SQL or Cloud Storage into your vector store.

Query Stored Knowledge

Run automated, deep-dive questions that query the stored vectors and synthesize accurate answers from the retrieved context.

Inspect Raw Data Chunks

Perform targeted searches to pull raw document chunks, allowing you to verify specific text segments for auditing purposes.

Monitor Available Models

List and check the metadata of all LLMs and embedding models registered in your Cognita installation.

Ask an AI about this

Compatible AI Apps

OAuth 2.0 Compatible

Claude

ChatGPT

Cursor

Gemini

VS Code

JetBrains

Vercel

Zendesk

+ any other MCP app

Included with Plan

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AI Agent

Cognita (RAG Framework) 7 Tools

These tools let your agent perform every step of the RAG process: listing resources, ingesting files, querying knowledge, and auditing data structures.

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 Cognita (RAG Framework) on Vinkius

Rag Query

Identifies precise, active arrays by querying the vector store with a user-defined prompt.

List Models

Inspects deep internal arrays to enumerate all available LLMs and embedding models...

List Collections

Identifies and lists all bounded routing spaces available within your Headless...

Get Collection

Retrieves detailed logging and tracing information for a specified knowledge...

List Data Sources

Performs structural extraction to list properties driving active external data...

Ingest Data

Pushes new data or updates into the vector space by creating resources from JSON payloads.

Search Chunks

Enumerates and retrieves explicitly attached structured rules containing raw document chunks for review.

Connect to your AI in seconds. 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.

Claude AI

Open Claude Settings

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

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.

Start a conversation

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

Antigravity

Configure Agent Environment

Open your Antigravity agent's workspace configuration or mcp-servers.json file.

Bind the Endpoint

Add the Vinkius endpoint URL to your agent's MCP connections list:

"mcp_servers": {
  "cognita-rag-framework": {
    "serverUrl": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
  }
}

Provide your secure token in place of [YOUR_TOKEN_HERE] to ensure your agent requests are authenticated.

Execute

Start your Antigravity session. The agent will autonomously discover and utilize the Cognita tools with full Vinkius guardrails applied.

VS Code Copilot

⚡

One-Click Install (Recommended)

In your Vinkius Dashboard, simply click the Add to VS Code button for this server. We'll automatically configure your local workspace.

Or configure manually

Open MCP Settings

Open VS Code, press Ctrl/Cmd + Shift + P, and search for GitHub Copilot: MCP Servers.

Add Server Config

Add the Vinkius endpoint configuration to your mcp-servers.json file:

"cognita-rag-framework": {
  "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}

Ensure you replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.

LangChain

Install Dependencies

Install the LangChain MCP adapters for your environment:

pip install langchain-mcp-adapters

Connect the Server

Use the SSEClient in LangChain to connect to the Vinkius managed endpoint:

from langchain_mcp_adapters.client import SSEClient

# Connect to Vinkius
client = SSEClient(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
tools = client.get_tools()

CrewAI

Define the Tool

Load the Vinkius MCP tools into your CrewAI agents:

from crewai import Agent
from mcp_crewai import MCPTool

# Connect securely to Vinkius
vinkius_tools = MCPTool(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

# Assign to Agent
researcher = Agent(
    role='Data Researcher',
    tools=vinkius_tools.get_all()
)

Execute Task

Run your CrewAI process. The agent will autonomously route tasks to the Vinkius managed server.

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 every call
Real time usage dashboard and cost metering
Publish to catalog or keep private

Start building

Make Your AI Do More

Start with Cognita (RAG Framework), then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.

Use this MCP plus 5,000+ others, all in one place
Add new capabilities to your AI anytime you want
Every connection is secured and compliant automatically
Track usage and costs across all your servers
Works with Claude, ChatGPT, Cursor, and more
New servers added to the catalog every week

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cognita. 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 INFRASTRUCTURE

Cloud Hosted

Managed infra

V8 Isolated

Sandboxed per request

Zero-Trust Proxy

No stored credentials

DLP Enforced

Policy on every call

GDPR Compliant

EU data residency

Token Compression

~60% cost reduction

Your data is protected. See how we built it.

Works with Claude, ChatGPT, Cursor, and more

The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.

This connection provides 7 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.

Debugging knowledge retrieval is a manual nightmare today.

Right now, if an agent gives you a wrong answer or uses outdated information, your workflow breaks. You're forced to jump between different tools: checking the API documentation for data sources, logging into the vector database console to see what collections exist, and then maybe writing custom scripts just to audit the chunking logic. It’s slow, it requires specialized coding knowledge, and you spend half your time just verifying that the inputs are correct.

With this MCP, you bring all those checks—the source mapping, the collection listing, the data ingestion—into a single chat interface. Your agent handles the debugging steps for you. You tell it to audit the system's state, and it gives you the answer. It’s pure control.

Cognita (RAG Framework) Gives You Full Control Over Data Flow

You no longer have to write Python code just to check if a new file got successfully pushed or if the correct LLMs are registered. The `list_models` tool handles that enumeration, and `ingest_data` manages the transfer, making it all conversational.

The difference is control. You move from being dependent on siloed GUIs and scripts to orchestrating your entire knowledge graph directly through a simple prompt.

Support 24/7 support@vinkius.com ↗

Security Vinkius Trust Center ↗

SLA Service Level Agreement ↗

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What your AI can actually do with this

Building a good AI agent means more than just asking it questions; you have to control the entire flow of information. This MCP lets you take full command of that process. You can inspect exactly what data is in your system, see which external sources are connected, and audit the structure of your knowledge collections.

Need to update the base? Use this MCP's tools to push fresh files from SQL or cloud storage into your vector space. Once the data is clean and ready, you can run targeted questions against that stored context and get synthesized answers without leaving your chat window. If you're working with multiple AI clients, Vinkius hosts this MCP, giving your agent access to all its retrieval tools in one place.

Built · Hosted · Managed by Vinkius Cognita RAG Framework - Manage Knowledge Retrieval

Server ID 019d7576-ebd0-70a1-881e-c2faf27c11e5

Vinkius Inspector

Compliance Grade A+

Score 100/100

Report View Report ↗

Here's how it actually works

The bottom line is that you manage complex RAG pipelines right through your conversational interface.

Subscribe to this MCP, then enter your Cognita Base URL and API Key into your preferred AI client.

Use the tools within your agent to list available data sources and collections, verifying everything is mapped correctly.

Call ingest_data to push new files or updated content from external systems into the vector space, making it ready for queries.

What Changes When You Connect

You don't have to write code just to test a query. Use rag_query to ask questions directly against your live data and get answers synthesized by the agent.

Debugging context is easy. If you suspect stale information, run list_data_sources first to verify which external buckets are mapped into the AI workflow.

Need to see what's in a specific knowledge base? Use get_collection or list_collections to audit metadata for token lengths and parser details before asking questions.

Data updates shouldn't require a DevOps ticket. Triggering an ingestion run with ingest_data pushes fresh files from SQL or Cloud Storage instantly.

You can check the whole stack at once. Running list_models ensures that every required LLM and embedding model endpoint is active and reachable.

See it in action

01 01

Troubleshooting a bad answer

An agent gives an incorrect answer, and you can't tell why. Instead of guessing, run search_chunks to manually pull the raw document chunks used in the response. Then use list_collections to confirm which knowledge base that data came from. This pinpoints if the issue is the source or the query.

02 02

Onboarding a new manual

A team writes a huge, complex technical guide stored in an API bucket. You need the agent to know it immediately. Use list_data_sources to confirm the connection exists, then use ingest_data to push the files into your knowledge base.

03 03

Validating model capabilities

The AI team needs to switch from Model A to Model B for cost reasons. Before switching, they run list_models to check which other LLMs are registered and available within the Cognita setup.

04 04

Auditing data sprawl

The product team is unsure if all necessary documentation has been fed into the agent. They start by calling list_collections to map out every existing knowledge silo, ensuring nothing crucial was missed before launch.

The honest tradeoffs

Running a query blind

Anti-pattern

Just running rag_query when you haven't checked the data source connection. The agent might pull context from an old or irrelevant collection, giving a misleading answer.

The Fix

Always start by calling list_data_sources. This confirms the external connections are live and mapped before querying anything with rag_query.

Assuming data is fresh

Anti-pattern

The source API updated critical documents, but you haven't told the system. The agent continues to use old information, leading to major errors.

The Fix

Manually trigger an update using ingest_data. This forces synchronization and ensures your vector space contains the latest files.

Ignoring collection metadata

Anti-pattern

You run a query but don't know if the underlying documents are properly segmented or structured. The answer is vague, hard to trust.

The Fix

Run get_collection first. This inspects embedding configurations and token lengths, telling you if the data was prepared correctly for retrieval.

When It Fits, When It Doesn't

Use this MCP if your core requirement is controlling a complex, multi-stage knowledge pipeline (i.e., 'I need to ingest X, check Y, then query Z'). You need it when debugging or building systems that rely on accurate context retrieval, not just simple chat. Don't use this if you only need basic search functionality; for that, a standard document index is enough. If your primary pain point is simply getting an answer based on one static PDF, the complexity of ingest_data and collection management might be overkill. This MCP shines when data flow itself is part of the problem.

Questions you might have

Can my agent perform semantic RAG queries against my collections? +

Yes. The 'rag_query' tool 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.

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' tool will command the Cognita backend to start a sync, updating your RAG vector space with the latest remote documents.

Can I audit the raw document chunks before LLM generation? +

Absolutely. Use the 'search_chunks' tool 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.

When I use the `list_data_sources` tool, how do I verify which external systems are connected to Cognita? +

It lists all active data sources and properties. You can audit exactly which buckets or APIs feed into your knowledge base, ensuring you only rely on approved data.

After running `list_models`, how do I check which LLMs and embedding models Cognita recognizes? +

The tool enumerates every registered model endpoint. This lets you verify compatibility or switch to a different vector size without writing any code.

If my RAG query fails, how do I use `get_collection` to retrieve payload IDs for debugging? +

It retrieves explicit cloud logging tracing and specific Payload IDs. This lets you pinpoint exactly where the data processing broke down in your knowledge pipeline.

When I run a complex `rag_query`, does Cognita tell me what context it used to answer? +

Yes, it synthesizes accurate answers while detailing the source context. You see precisely how many chunks were pulled from your vector store, confirming depth of knowledge.

If I use `list_collections`, what specific metadata can I audit for each knowledge collection? +

It provides configuration details like embedding setups and token lengths. This confirms that every collection is properly sized and structured before you start querying it.

Connect to your AI in seconds.

Rag query

List models

List collections

Cognita (RAG Framework) 7 Tools

Make your AI actually useful.

Rag Query

List Models

List Collections

Get Collection

List Data Sources

Ingest Data

Search Chunks

Connect to your AI in seconds. Security and governance baked right in.

Claude AI

Open Claude Settings

Add Custom Connector

Start a conversation

Claude Code

Open your terminal

Add the MCP Server

Start coding

Cursor

One-Click Install (Recommended)

Open Cursor Settings

Add New Server

Use in Composer

Antigravity

Configure Agent Environment

Bind the Endpoint

Execute

VS Code Copilot

One-Click Install (Recommended)

Open MCP Settings

Add Server Config

Windsurf

One-Click Install (Recommended)

Open Windsurf Settings

Add Server Endpoint

LangChain

Install Dependencies

Connect the Server

CrewAI

Define the Tool

Execute Task

Choose How to Get Started

Build Your Own

Make Your AI Do More

Works with Claude, ChatGPT, Cursor, and more

Debugging knowledge retrieval is a manual nightmare today.

Cognita (RAG Framework) Gives You Full Control Over Data Flow

What your AI can actually do with this

Here's how it actually works

Who is this actually for?

What Changes When You Connect

See it in action

Troubleshooting a bad answer

Onboarding a new manual

Validating model capabilities

Auditing data sprawl

The honest tradeoffs

Running a query blind

Assuming data is fresh

Ignoring collection metadata

When It Fits, When It Doesn't

Questions you might have