How to Use the GroundX MCP in Pydantic AI

Q: How do I set up the GroundX MCP Server in Pydantic AI?

You initialize MCPToolset with your Vinkius HTTP endpoint URL. Pass this toolset directly to your Agent constructor to let the agent auto-discover tools like searchdocuments and ingestdocuments.

Q: Can Pydantic AI handle asynchronous document crawling with GroundX?

Yes, your agent triggers ingestwebsite asynchronously. It then monitors the progress by calling getingeststatus within an async loop, ensuring your application remains responsive during large crawls.

Q: How do I check my GroundX account limits using Pydantic AI?

Your agent calls getcustomerinfo, which returns a validated payload containing your account details. This allows your code to programmatically verify limits before initiating large batch ingestion tasks.

Type-safe GroundX RAG search with runtime validation for your Pydantic AI agents.

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Connect GroundX MCP to Pydantic AI

Create your Vinkius account to connect GroundX 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 GroundX with Pydantic AI

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Type-Safe Document Ingestion

GroundX integrates with Pydantic AI using `ingest_documents` to upload local files or URLs. The framework validates every ingestion response payload at runtime, ensuring your agent never processes corrupt status data. You track the upload progress using `get_ingest_status`. If the API returns an unexpected status schema, Pydantic AI raises a validation error immediately, preventing silent failures in your production pipeline.

Strict Search Validation for Pydantic AI

This MCP Server exposes `search_content` to run semantic queries across your indexed documents. The framework guarantees that the search results match your defined Pydantic models before they reach your LLM. Your agent uses `search_documents` to locate specific files with strict metadata filtering. This prevents hallucinated fields from entering your prompt context, keeping your agent's outputs predictable and accurate.

Verified Bucket and Group Management

GroundX manages storage structures using `create_bucket` and `create_group` directly through type-safe tools. Your Pydantic AI agent verifies these structures using `list_buckets` and `list_groups` to ensure correct routing. The framework connects to these tools using the unified `MCPToolset` over HTTP. Every bucket configuration returned by `list_workflows` is verified against strict schemas, keeping your data organization clean.

Setup guide

Set up GroundX 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": {
        "groundx-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent GroundX transactions")
print(result.output)

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Common questions about GroundX MCP in Pydantic AI

The framework validates the response against internal schemas for `search_content`. If GroundX returns a payload that doesn't match, Pydantic AI raises a validation error immediately rather than passing bad data to the LLM.

You initialize `MCPToolset` with your Vinkius HTTP endpoint URL. Pass this toolset directly to your Agent constructor to let the agent auto-discover tools like `search_documents` and `ingest_documents`.

Yes, your agent triggers `ingest_website` asynchronously. It then monitors the progress by calling `get_ingest_status` within an async loop, ensuring your application remains responsive during large crawls.

Your agent calls `get_customer_info`, which returns a validated payload containing your account details. This allows your code to programmatically verify limits before initiating large batch ingestion tasks.

All PDFs, crawled websites, and search indexes are managed inside isolated containers. The Vinkius MCP Server sandbox enforces strict zero-trust execution, ensuring your Pydantic AI agent interacts with the API through a single, highly secure transport layer.

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