How to Use the JobProgress (Leap) MCP in Pydantic AI

Run type-safe, validated field service operations in Pydantic AI using our managed MCP Server.

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Connect JobProgress (Leap) MCP to Pydantic AI

Create your Vinkius account to connect JobProgress (Leap) 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 JobProgress (Leap) with Pydantic AI

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Validate active jobs with Pydantic AI

The `list_jobs` tool outputs structured project data that Pydantic AI validates at runtime. This ensures that every job status, customer ID, and project title matches your strict Python models before your code processes them. If the API returns unexpected data formats, your agent fails immediately instead of corrupting your database. You can safely inspect individual project details using `get_job` knowing the schema is guaranteed.

Enforce strict schemas on this MCP Server

The `list_estimates` tool retrieves pending project costs for instant validation. Pydantic AI checks these financial figures against your business rules, flagging abnormal estimates before they reach your clients. Your agent can also verify contract statuses by reading `list_proposals`. Because the data is strictly typed, your automated workflows can reliably trigger billing systems when a proposal is marked as accepted.

Ensure scheduling accuracy with strict types

The `list_appointments` tool delivers your field calendar directly into validated Python objects. This prevents your scheduling agents from processing malformed dates or missing technician IDs during dispatch. To keep operations organized, the agent tracks team progress using `list_tasks` and `list_workflows`. It maps these tasks against active employees found via `list_users`, ensuring every assignment has a valid owner.

Setup guide

Set up JobProgress (Leap) 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": {
        "jobprogress-leap-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about JobProgress (Leap) MCP in Pydantic AI

Use the MCPToolset class with your hosted server URL. Pass this toolset to your Agent constructor to instantly expose the MCP Server to your model.

The framework raises a validation error at runtime. This prevents your agent from executing decisions based on corrupted or unexpected JSON structures from tools like list_tasks.

Yes, this framework is model-agnostic. You can connect local models to the MCP Server to run secure, offline audits on your list_estimates and list_proposals data.

You should implement retry logic in your Python code or use a middleware proxy. Since the server maps directly to Leap API endpoints, standard rate limits apply when calling list_customers heavily.

Your client names, physical addresses, and contact numbers are never cached or sent to third-party logs. The MCP Server acts as an ephemeral, zero-trust gateway, processing data purely in-memory.

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