How to Use the Cognita (RAG Framework) MCP in Pydantic AI

Q: How do I initialize the Cognita (RAG Framework) toolset in Pydantic AI?

Install the MCP extension and use the MCPToolset class pointing to your Vinkius HTTP URL. Pass this toolset directly to your Pydantic AI agent to instantly expose all MCP tools.

Run type-safe RAG pipelines with Pydantic AI validating every vector and collection payload at runtime.

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Connect Cognita (RAG Framework) MCP to Pydantic AI

Create your Vinkius account to connect Cognita (RAG Framework) to Pydantic AI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Cognita (RAG Framework) with Pydantic AI

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Type-safe vector queries for Pydantic AI

The `rag_query` tool fetches vector arrays and validates them against Pydantic models before your agent can use them. Using this MCP Server with Pydantic AI means every response is strictly validated. If a vector array comes back corrupted, your system catches it instantly. Your agent can confidently run `search_chunks` to pull structured rules, knowing the data matches your exact schema.

Strict schema validation for Cognita ingestion

The `ingest_data` tool ensures your agent validates the JSON payload before provisioning new resource directories. This stops bad data at the door and keeps your RAG pipeline clean. You can also use `list_data_sources` to inspect active buckets. The tool returns structured properties that Pydantic parses into clean, typed Python objects.

Inspect collections without silent errors

The `list_collections` tool identifies routing spaces inside the headless Cognita limits. You get a direct view into your MCP Server architecture without worrying about silent model corruption. If you need to debug, the agent can call `get_collection` to fetch cloud logging traces. This gives you a type-safe way to track payload IDs across your entire RAG architecture.

Setup guide

Set up Cognita (RAG Framework) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "cognita-rag-framework-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Cognita (RAG Framework) tools.",
)

result = await agent.run("List recent Cognita (RAG Framework) transactions")
print(result.output)

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Common questions about Cognita (RAG Framework) MCP in Pydantic AI

Install the MCP extension and use the `MCPToolset` class pointing to your Vinkius HTTP URL. Pass this toolset directly to your Pydantic AI agent to instantly expose all MCP tools.

Pydantic AI will raise a validation error at runtime. This prevents your agent from processing corrupt vector arrays or hallucinating based on bad data.

The MCP Server automatically exports the correct schemas, which Pydantic AI reads and enforces. You do not need to write manual validation models for tools like `rag_query` or `ingest_data`.

Yes, Pydantic AI natively supports async execution. Your agent can run multiple `rag_query` calls in parallel without blocking your main event loop.

All validation happens locally in your Python runtime. The raw vector data and JSON payloads passing through the MCP Server are processed inside an ephemeral, zero-trust V8 sandbox.

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