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 Pydantic AI 7 tools — connect in under 2 minutes

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

ai.pydantic.dev/mcp

Built by Vinkius GDPR 7 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Cognita (RAG Framework) through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token — get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Cognita (RAG Framework) "
            "(7 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Cognita (RAG Framework)?"
    )
    print(result.data)

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.

Pydantic AI validates every Cognita (RAG Framework) tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

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

Install Pydantic AI

Run pip install pydantic-ai

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) with type-safe schemas

Why Use Pydantic AI with the Cognita (RAG Framework) MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Cognita (RAG Framework) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Cognita (RAG Framework) connection logic from agent behavior for testable, maintainable code

Cognita (RAG Framework) + Pydantic AI Use Cases

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

Type-safe data pipelines: query Cognita (RAG Framework) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Cognita (RAG Framework) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Cognita (RAG Framework) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Cognita (RAG Framework) responses and write comprehensive agent tests

Cognita (RAG Framework) MCP Tools for Pydantic AI (7)

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

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Cognita (RAG Framework) + Pydantic AI FAQ

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

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer — your Cognita (RAG Framework) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.