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.

Cognita (RAG Framework) MCP Server for LangChain 7 tools — connect in under 2 minutes

Name: Cognita (RAG Framework)
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Cognita (RAG Framework) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "cognita-rag-framework": {
            "transport": "streamable_http",
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
        }
    }) as client:
        tools = client.get_tools()
        agent = create_react_agent(
            ChatOpenAI(model="gpt-4o"),
            tools,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using Cognita (RAG Framework), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Cognita (RAG Framework) MCP Server

Connect your Cognita (TrueFoundry) instance to any AI agent and take full control of your modular RAG workflows through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Cognita (RAG Framework) through native MCP adapters. Connect 7 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

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

The Cognita (RAG Framework) MCP Server exposes 7 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Cognita (RAG Framework) to LangChain via MCP

Follow these steps to integrate the Cognita (RAG Framework) MCP Server with LangChain.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 7 tools from Cognita (RAG Framework) via MCP

Why Use LangChain with the Cognita (RAG Framework) MCP Server

LangChain provides unique advantages when paired with Cognita (RAG Framework) through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Cognita (RAG Framework) MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across Cognita (RAG Framework) queries for multi-turn workflows

Cognita (RAG Framework) + LangChain Use Cases

Practical scenarios where LangChain combined with the Cognita (RAG Framework) MCP Server delivers measurable value.

RAG with live data: combine Cognita (RAG Framework) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Cognita (RAG Framework), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Cognita (RAG Framework) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Cognita (RAG Framework) tool call, measure latency, and optimize your agent's performance

Cognita (RAG Framework) MCP Tools for LangChain (7)

These 7 tools become available when you connect Cognita (RAG Framework) to LangChain via MCP:

get_collection

Retrieve explicit Cloud logging tracing explicit Payload IDs

ingest_data

Provision a highly-available JSON Payload generating new Resource directories

list_collections

Identify bounded routing spaces inside the Headless Cognita RAG limit

list_data_sources

Perform structural extraction of properties driving active Buckets

list_models

Inspect deep internal arrays mitigating specific Picture constraints

rag_query

Identify precise active arrays spanning rented Transformation vectors

search_chunks

Enumerate explicitly attached structured rules exporting active Presets

Example Prompts for Cognita (RAG Framework) in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Cognita (RAG Framework) immediately.

"List all RAG collections in Cognita"

"Query collection 'technical-docs' for: 'How do I configure OAuth in our API?'"

"Ingest data from source 'gh-repo-vinkius' into collection 'technical-docs'"

Troubleshooting Cognita (RAG Framework) MCP Server with LangChain

Common issues when connecting Cognita (RAG Framework) to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Cognita (RAG Framework) + LangChain FAQ

Common questions about integrating Cognita (RAG Framework) MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.