How to Use the Unstructured MCP in Pydantic AI

Q: What is the best way to check available data sources when using Unstructured with Pydantic AI?

You call listdatasources. The MCP Server returns a list of configured connectors, and your agent validates this structure against its internal models before proceeding.

Q: Can I monitor job status for Unstructured data with Pydantic AI?

Yes. Use listworkflowjobs. The tool returns a list of jobs, and the resulting job IDs and statuses are all validated by Pydantic models, ensuring you get reliable operational data.

Q: What does Unstructured do with workflow configurations using Pydantic AI?

You use getworkflowdetails to pull configuration. This function retrieves the details, and those resulting fields are validated by the framework, eliminating any guesswork about the required parameters.

Q: How do I list available pipelines for Unstructured with Pydantic AI?

You call listprocessingworkflows. This gives a clean, type-safe list of every end-to-end pipeline, allowing your agent to select the correct path without worrying about schema drift.

Type-safe data transformation for complex inputs with Pydantic AI.

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Setup Unstructured with Pydantic AI

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Listing Data Sources in Pydantic AI

The `list_data_sources` tool gives your agent a list of all configured remote connectors. When using the Pydantic AI framework, this means the API response describing S3 or GCS connections will validate cleanly against expected types. It’s about correctness first. Your agent doesn't have to guess at connection types; it gets a validated list from the MCP Server.

Executing Workflows with Pydantic AI

To run a process, your agent triggers the workflow using `trigger_workflow_execution`. Crucially, the resulting job ID and status are returned in data that Pydantic validates. If the MCP Server sends unexpected structure, the whole thing fails loudly. This prevents silent corruption of critical Unstructured processing steps.

Checking Destinations via MCP Server

The `list_data_destinations` tool confirms where your processed data is going—Vector DBs or SQL. The Pydantic AI client ensures that the list of destinations it receives adheres strictly to defined types. It's a vital check for guaranteeing that downstream consumers get exactly the schema they expect.

Setup guide

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

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

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

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Common questions about Unstructured MCP in Pydantic AI

Your agent uses the MCP Server to manage sources, run workflows via `trigger_workflow_execution`, and then consumes the results. Because of Pydantic validation, your agent knows that any output it receives is correctly structured.

You call `list_data_sources`. The MCP Server returns a list of configured connectors, and your agent validates this structure against its internal models before proceeding.

Yes. Use `list_workflow_jobs`. The tool returns a list of jobs, and the resulting job IDs and statuses are all validated by Pydantic models, ensuring you get reliable operational data.

You use `get_workflow_details` to pull configuration. This function retrieves the details, and those resulting fields are validated by the framework, eliminating any guesswork about the required parameters.

You call `list_processing_workflows`. This gives a clean, type-safe list of every end-to-end pipeline, allowing your agent to select the correct path without worrying about schema drift.

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