Cognita (RAG Framework) MCP, Ready to Go
Manage your RAG pipelines and vector store data with Claude or Cursor using the Cognita MCP for AI agents to handle complex data ingestion.
No credit card required. Experience the power of this integration risk-free.
Manage your RAG pipelines and vector store data with natural conversation.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Cognita (RAG Framework) MCP Server?
Average time for the server to become ready for requests over the last 7 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this MCP on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Cognita RAG Framework 7-Tool Vector Search
Use these tools to sync data, audit collections, and query your vector store directly through your AI agent.
Rag query
Use this to ask a question and get a synthesized answer from your vector store. It pulls relevant context to give you a grounded response.
List models
Use this to see every LLM and embedding model registered in your Cognita instance. It helps you verify which models are ready for production.
List collections
Use this to see all your RAG collections and their specific configurations. It helps you keep track of different knowledge domains.
Get collection
Use this to pull specific logging and payload IDs for a single collection. It is great for deep-diving into specific data sets.
List data sources
Use this to see all the external buckets and APIs mapped to your AI workflows. It confirms your data pipelines are connected.
Ingest data
Use this to trigger a sync to pull new files into your vector space. It handles the heavy lifting of generating new resource directories.
Search chunks
Use this to perform a search to find specific text segments in your vector store. It lets you verify exactly what your agent knows.
One MCP enables access. Vinkius turns MCPs into production-ready infrastructure.
You're looking at one of 5,700+ managed MCPs. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Cognita RAG Framework for Ending Manual Data Syncing
This is for the AI engineer who's tired of manually checking sync logs at 2am or the data scientist who needs to verify chunking logic without writing a single line of Python.
AI Engineer
Testing and debugging RAG retrieval logic to ensure the agent is pulling the right context.
Data Scientist
Monitoring ingestion pipelines and verifying document chunking consistency across different collections.
DevOps Engineer
Checking Cognita model registries to ensure all LLM endpoints are active and reachable.
Product Manager
Auditing the knowledge base content during the prototyping phase to see what the agent actually knows.
Frequently Asked Questions
Can the Cognita MCP sync my SQL data automatically? +
Yes, it can. The MCP allows your agent to trigger data ingestion from various sources, including SQL databases, cloud storage, and remote APIs, to keep your knowledge base updated.
How do I check if my documents are being chunked correctly with Cognita? +
You can use the search tool to pull raw document chunks from your vector store. This lets you see the exact text segments your agent uses to answer questions.
Can I see which LLMs are active in my Cognita instance? +
Yes, you can ask your agent to list the models. This shows you every LLM and embedding model currently registered in your Cognita setup.
Does Cognita work with my existing data sources? +
It works with many common sources like Cloud Storage, SQL, and APIs. You can list your connected sources to verify which ones are mapped into your workflows.
How does Cognita help with RAG debugging? +
It lets you audit your collections and search specific chunks. By seeing the raw data and the model registry in one place, you can quickly find where a retrieval pipeline is failing.
Can I query my knowledge base directly through the Cognita MCP? +
Yes, you can. Your agent can perform RAG queries that search your vector store and synthesize accurate answers based on your stored context.
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.
Your AI, connected to everything.
No credit card required · Free tier available
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