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 CrewAI 7 tools — connect in under 2 minutes

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

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 7 Tools Framework

Connect your CrewAI agents to Cognita (RAG Framework) through the Vinkius — pass the Edge URL in the `mcps` parameter and every Cognita (RAG Framework) tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Cognita (RAG Framework) Specialist",
    goal="Help users interact with Cognita (RAG Framework) effectively",
    backstory=(
        "You are an expert at leveraging Cognita (RAG Framework) tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Cognita (RAG Framework) "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 7 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Cognita (RAG Framework) becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Cognita (RAG Framework) tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

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

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 7 tools from Cognita (RAG Framework)

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

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Cognita (RAG Framework) through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Cognita (RAG Framework) + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Cognita (RAG Framework) for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Cognita (RAG Framework), analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Cognita (RAG Framework) tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Cognita (RAG Framework) against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

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

These 7 tools become available when you connect Cognita (RAG Framework) to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Cognita (RAG Framework) + CrewAI FAQ

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

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.