Cognita (RAG Framework) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cognita (RAG Framework) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Cognita (RAG Framework). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Cognita (RAG Framework)?"
)
print(response)
asyncio.run(main())
* 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.
LlamaIndex agents combine Cognita (RAG Framework) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Cognita (RAG Framework) MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 7 tools from Cognita (RAG Framework)
Why Use LlamaIndex with the Cognita (RAG Framework) MCP Server
LlamaIndex provides unique advantages when paired with Cognita (RAG Framework) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cognita (RAG Framework) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cognita (RAG Framework) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cognita (RAG Framework), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cognita (RAG Framework) tools were called, what data was returned, and how it influenced the final answer
Cognita (RAG Framework) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cognita (RAG Framework) MCP Server delivers measurable value.
Hybrid search: combine Cognita (RAG Framework) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cognita (RAG Framework) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cognita (RAG Framework) for fresh data
Analytical workflows: chain Cognita (RAG Framework) queries with LlamaIndex's data connectors to build multi-source analytical reports
Cognita (RAG Framework) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Cognita (RAG Framework) to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Cognita (RAG Framework) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCognita (RAG Framework) + LlamaIndex FAQ
Common questions about integrating Cognita (RAG Framework) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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.
Connect Cognita (RAG Framework) to LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.