How to Use the Cadmium Harvester MCP in Pydantic AI

Get type-safe event data in Pydantic AI with this MCP Server.

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Connect Cadmium Harvester MCP to Pydantic AI

Create your Vinkius account to connect Cadmium Harvester 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 Cadmium Harvester with Pydantic AI

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Perplexity

Enforce types in Pydantic AI

Every response from `list_presentations` is validated against your Pydantic models at runtime. If the API structure changes, your agent crashes before it can process bad data. Your agent uses `get_event_details` to pull info into a structured format. This keeps your data clean and predictable throughout the entire agent run.

Validate speaker records with MCP Server

Your agent calls `list_presenters` and `get_presenter_details` to populate your defined speaker schemas. Any schema mismatch triggers an immediate error. This prevents your agent from hallucinating fields. You define the structure, and the tool returns data that fits your model exactly.

Inventory assets using Pydantic AI

Your agent runs `list_presentation_assets` to map out file requirements. It validates these against your internal requirements using the Pydantic toolset. It counts your sessions with `get_presentation_count`. Your agent ensures these integers are handled correctly within your application logic.

Setup guide

Set up Cadmium Harvester 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": {
        "cadmium-harvester-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Cadmium Harvester MCP in Pydantic AI

The server responses are strictly checked against your Pydantic models. If the server returns something unexpected, the agent halts, preventing bad data from moving forward.

Yes, you use the MCPToolset to register the server. Your agent then gains type-safe access to every tool provided by the server.

Absolutely. It supports the required transport methods and integrates into your agent's toolset array without extra configuration.

Your agent catches the validation error or connection failure immediately. You get a clear log entry showing exactly what went wrong.

The server acts as a conduit for your event configuration and speaker data. All communication is authenticated via your unique token and isolated from other users.

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