How to Use the HCSS MCP in Pydantic AI

Q: How do I connect the HCSS MCP Server to Pydantic AI?

Initialize the MCPToolset with your Vinkius server URL and pass it to your Agent constructor. The deprecated MCPServerHTTP class should not be used.

Run type-safe HCSS queries with Pydantic AI to ensure your field data matches your database schemas perfectly.

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Connect HCSS MCP to Pydantic AI

Create your Vinkius account to connect HCSS 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 HCSS with Pydantic AI

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Validate field timecards with strict runtime checks.

Operational labor data is fetched for your Pydantic AI agents by the `list_timecards` and `list_employees` tools. The framework validates every record against your defined Python types, throwing immediate errors if fields are missing. This prevents dirty data from corrupting your analysis. If a timecard contains an invalid employee ID or mismatched format, the run fails loudly before any logic executes.

Run type-safe fleet tracking with Pydantic AI.

Precise operational metrics are provided via this MCP Server using the `get_equipment_meters` and `list_equipment` tools. Your agent uses these tools to track machine wear and schedule maintenance based on exact odometer readings. Because Pydantic AI enforces strict data validation, you never have to worry about null values or malformed GPS strings breaking your code. You get clean, predictable metrics for your entire fleet.

Match bid items to active project estimates.

HeavyBid data is exposed directly to your validation pipelines through the `get_bid_items` and `list_estimates` tools. Your agent pulls active estimates and verifies that each bid item matches your corporate cost structure. This keeps your bidding process accurate and compliant. The agent checks that every cost code aligns with the active project scope without silent failures.

Setup guide

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

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

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

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Common questions about HCSS MCP in Pydantic AI

Initialize the `MCPToolset` with your Vinkius server URL and pass it to your `Agent` constructor. The deprecated `MCPServerHTTP` class should not be used.

Yes. Every cost code returned by the server is parsed and validated against your schema at runtime, catching missing fields instantly.

Yes. The framework is model-agnostic, meaning you can run your HCSS agent with local models or commercial APIs.

Pydantic AI raises a validation error immediately. This stops the agent from executing further actions based on bad location data.

The server runs in a secure, isolated V8 sandbox on Vinkius. Your sensitive bidding estimates and employee rosters are parsed locally, ensuring no raw data leaks to external servers.

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