Cognita (RAG Framework) MCP Server
Manage modular RAG via Cognita — list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent.
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What is the Cognita MCP Server?
The Cognita MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Cognita via 7 tools. Manage modular RAG via Cognita — list collections, ingest data sources, and perform AI-driven Q&A directly from any AI agent. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (7)
Tools for your AI Agents to operate Cognita
Ask your AI agent "List all RAG collections in Cognita" and get the answer without opening a single dashboard. With 7 tools connected to real Cognita data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Cognita (RAG Framework) MCP Server capabilities
7 toolsRetrieve explicit Cloud logging tracing explicit Payload IDs
Provision a highly-available JSON Payload generating new Resource directories
Identify bounded routing spaces inside the Headless Cognita RAG limit
Perform structural extraction of properties driving active Buckets
Inspect deep internal arrays mitigating specific Picture constraints
Identify precise active arrays spanning rented Transformation vectors
Enumerate explicitly attached structured rules exporting active Presets
What the Cognita (RAG Framework) MCP Server unlocks
Connect your Cognita (TrueFoundry) instance to any AI agent and take full control of your modular RAG workflows through natural conversation.
What you can do
- Knowledge Collections — List and audit RAG collections to inspect embedding configurations, token lengths, and parser details
- Data Ingestion — Force sync remote files from SQL, Cloud Storage, or APIs into your vector space to update your knowledge base
- RAG Queries — Dispatch automated AI questions that query your vector store and synthesize accurate answers from stored context
- Chunk Auditing — Perform lexical or semantic searches to pull raw document chunks and verify precise text segments
- Model Registry — Enumerate available LLMs and embedding models registered inside your modular Cognita installation
- DataSource Management — List all connected data sources to verify which external data is mapped into your AI workflows
How it works
1. Subscribe to this server
2. Enter your Cognita Base URL and API Key (if required by your TrueFoundry or self-hosted setup)
3. Start managing your RAG pipelines from Claude, Cursor, or any MCP-compatible client
Who is this for?
- AI Engineers — test and debug RAG queries and chunk retrieval logic without writing Python scripts
- Data Scientists — monitor ingestion pipelines and verify document chunking consistency across collections
- Product Teams — quickly audit what knowledge is being fed to AI agents during the prototyping phase
- DevOps Teams — monitor Cognita model registries and ensure that all LLM endpoints are active and reachable
Frequently asked questions about the Cognita (RAG Framework) MCP Server
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.
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Connect Cognita (RAG Framework) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Give your AI agents the power of Cognita MCP Server
Production-grade Cognita (RAG Framework) MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.