Cognita (RAG Framework) MCP. Query Your Knowledge Base with Chat.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
Cognita (RAG Framework) gives you total control over your knowledge retrieval pipelines. List collections, push data from SQL or cloud storage into a vector store, and run sophisticated AI queries directly against your company's private documentation—all without writing Python scripts.
What your AI agents can do
Get collection
Retrieves specific cloud logging tracing explicit payload IDs for a given collection.
Ingest data
Processes a JSON payload to generate and provision new resource directories, updating the knowledge base.
List collections
Identifies all bounded routing spaces inside the Headless Cognita RAG limit.
List all existing RAG collections to check embedding setups or token limits before starting a query.
Force sync remote files from SQL, Cloud Storage, or APIs into the vector space to keep your knowledge base current.
Run automated questions that query your stored documents and synthesize accurate answers based on the retrieved context.
Perform deep searches to pull raw document chunks, allowing you to verify the exact text segments used by the AI.
Get a list of all LLMs and embedding models registered within your Cognita setup.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Cognita (RAG Framework) 7 Tools
These tools allow you to inspect your collections, ingest new documents, run sophisticated queries, and audit the underlying knowledge base structure.
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 Vinkius019d7576get collection
Retrieves specific cloud logging tracing explicit payload IDs for a given collection.
019d7576ingest data
Processes a JSON payload to generate and provision new resource directories, updating the knowledge base.
019d7576list collections
Identifies all bounded routing spaces inside the Headless Cognita RAG limit.
019d7576list data sources
Performs structural extraction of properties that drive active data buckets, showing external connections.
019d7576list models
Inspects deep internal arrays to show which LLMs and embedding models are available.
019d7576rag query
Identifies precise active arrays by querying the vector store using natural language questions against stored vectors.
019d7576search chunks
Enumerates explicitly attached structured rules, allowing you to search for and view specific document chunks of text.
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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 server provides 7 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Keeping the AI agent informed about corporate knowledge is a nightmare.
Today, if your team updates a policy document or adds a new API guide, someone has to remember to manually upload it into the right folder and then tell the chatbot—often via a ticket—that the source material changed. This process involves multiple clicks, manual confirmations, and usually ends with some documentation getting lost in an email thread.
With this MCP, you just point your agent at the data source (be it SQL or S3). You trigger the sync through `ingest_data`, and the knowledge is automatically available for querying. The chatbot instantly knows about the new policy without anyone having to click a 'publish' button.
Cognita (RAG Framework) MCP provides full data visibility.
Previously, if an answer was wrong, all you could do was ask the AI team for another guess. You couldn't prove *why* it was wrong or which specific section of text influenced the hallucination. The whole process was a black box.
Now that your agent runs on Vinkius, and uses this MCP, every single tool call—from `list_collections` to `rag_query`—generates a cryptographically signed audit trail. You get proof of what data flowed through, eliminating guesswork.
What you can do with this MCP connector
You connect this MCP to any compatible agent to build modular Retrieval Augmented Generation (RAG) workflows using natural language. Instead of relying on fixed API endpoints, you treat your entire knowledge base like a living document that your AI agent can query and synthesize answers from. You can automatically push new files from SQL databases or cloud storage directly into the vector space so your information is always current.
If you need to know what data feeds which part of the system, you can inspect connection details and audit specific chunks of text for verification. The real power shows when you combine this MCP with others; you can build an agent that first checks a document collection, then sends a message about it, then logs the whole thing.
Because every interaction runs on Vinkius, you get full visibility into exactly what data flowed through and which tools were called—nothing happens in the dark.
019d7576-ebd0-70a1-881e-c2faf27c11e5 
How Cognita (RAG Framework) MCP Works
- 1 Subscribe to this MCP, then provide your specific Cognita Base URL and API Key.
- 2 Your agent can now call the tools; for instance, asking the system to list all available data sources or collections.
- 3 The agent executes the request in a secure sandbox, returns the structured data, and you use that information to guide the next step of your workflow.
The bottom line is... you get an AI agent that interacts with your specialized knowledge management system without needing any custom code.
Who Is Cognita (RAG Framework) MCP For?
This MCP is for the data architects and engineers who are tired of manually checking databases, running scripts, or guessing where a piece of information lives. You're the person who needs to prove exactly what knowledge an AI used to answer a customer question.
Debugging RAG queries and chunk retrieval logic by inspecting collections and data sources directly through chat.
Monitoring ingestion pipelines, checking for document chunking inconsistencies across different knowledge collections.
Quickly auditing the exact knowledge a prototype agent is using during early testing phases without involving engineering resources.
What Changes When You Connect
- Audit your knowledge sources before querying. Use the
list_collectionstool to inspect every RAG collection's embedding configuration and token lengths, preventing bad queries from the start. - Keep data fresh automatically. The
ingest_datatool pushes remote files from SQL or Cloud Storage directly into your vector space so old knowledge doesn't confuse your agent. - Pinpoint answers instantly. Use
rag_queryto ask natural language questions and get synthesized, accurate answers drawn only from the context you provided. - Verify source integrity. If an answer seems off, run
search_chunksto pull raw document chunks and verify the precise text segment that informed the AI's reply. - See what models are available. Run
list_modelsto audit your setup and confirm which LLM endpoints are active before building a new workflow.
Real-World Use Cases
Debugging an incorrect answer
A data scientist receives a query result they suspect is wrong. Instead of trusting the output, they use search_chunks to pull raw text chunks and run list_collections to confirm which specific collection was queried. This pinpoints if the model used outdated or irrelevant source material.
Onboarding new documentation
A technical writer needs the agent to know about a newly built internal API guide stored in S3. They use list_data_sources to verify the bucket is connected, then run ingest_data to push the files into the correct collection.
Checking system readiness
An AI engineer needs to build a new agent but isn't sure what backend models are active. They simply use list_models and confirm all required LLMs are registered before starting development.
Finding the right knowledge source
A product team wants to prototype an agent that answers questions about legal policies. They first use list_collections to find the existing 'legal-kb' collection, then run a targeted rag_query against it.
The Tradeoffs
Assuming data is ready
Running a complex query immediately after building the agent without checking the backend. The agent fails because the necessary source data hasn't been synced.
→
Before querying, always run list_data_sources to verify all external connections are active. If they are, use ingest_data to force a sync into the target collection.
Querying without scope
Running a general query that pulls from every available knowledge source, resulting in vague answers drawn from unrelated documents.
→
Always use list_collections first to identify the specific boundaries (like 'billing-policy' or 'tech-docs') and then target your query with rag_query against just that collection.
Hardcoding data paths
Writing a script that assumes the file path for new documents will always be /data/new. The system breaks when files land in a different directory.
→
Rely on list_data_sources to map existing properties and verify connections, letting your agent dynamically manage the data flow instead of relying on fixed paths.
When It Fits, When It Doesn't
Use this MCP if you need an AI agent that answers questions based on a private, defined body of knowledge. You should use it when your primary workflow involves reading and synthesizing context from structured documents or databases. Don't use this if your goal is simply to process transactions (like sending emails or updating user records) because those require dedicated messaging or CRM MCPs instead. If you only need to list resources without querying them, list_collections handles that job perfectly; don't try to query data you haven't yet ingested.
Common Questions About Cognita (RAG Framework) MCP
How do I check if my knowledge base is up to date using Cognita (RAG Framework) MCP? +
Use the list_data_sources tool first to confirm external connections are active. Then, run ingest_data to push new content from your source into the vector store.
What is the difference between `rag_query` and `search_chunks` in Cognita (RAG Framework) MCP? +
rag_query generates a synthesized, human-readable answer based on context. search_chunks, however, pulls out the raw text segments so you can audit the exact source material.
Do I need to worry about which models are available using list_models in Cognita (RAG Framework) MCP? +
No. Running list_models gives you a complete inventory of every LLM and embedding model registered, so you know exactly what your agent can use.
How do I find out which collections exist in Cognita (RAG Framework) MCP? +
You just call list_collections. This tool gives a clean list of every bounded routing space so you know where to direct your queries.
How do I verify all connected data sources using `list_data_sources`? +
It performs structural extraction of properties driving active Buckets. This command lets you confirm exactly which external services or buckets are currently mapped and available for your AI workflows.
What format should the payload be in when using the `ingest_data` tool? +
The tool requires a highly-available JSON Payload. This structure is necessary to provision new resource directories correctly, ensuring your knowledge base updates without data loss.
How do I check the internal metadata of one specific collection using `get_collection`? +
get_collection retrieves explicit Cloud logging tracing for a given Payload ID. You can audit detailed configuration information to understand how that particular knowledge space is set up.
How does the `search_chunks` tool help me debug my document structure? +
The search_chunks tool enumerates explicitly attached structured rules. This lets you pull active presets and verify exactly how your source documents are segmented into usable chunks.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.