How do I obtain my Fieldly API credentials?

First, contact support@fieldly.com to enable the Public API. Once activated, you can find your Client ID and Client Secret in the **Integration Settings** of your Fieldly Admin panel.

How long is the access token valid?

Fieldly access tokens are valid for one hour. The agent handles token renewal automatically so you don't have to worry about manual updates.

Can I create new construction tasks via chat?

Yes! The `create_work_item` tool allows you to programmatically generate new construction tasks and work items directly within your AI-powered workflow flawlessy.

Fieldly MCP Server for Pydantic AI 11 tools — connect in under 2 minutes

Name: Fieldly
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 11 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Fieldly through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Fieldly "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Fieldly?"
    )
    print(result.data)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Fieldly MCP Server

Fieldly is a specialized project management platform for the construction industry. This MCP server allows your AI agent to interact with your Fieldly account flawlessly.

Pydantic AI validates every Fieldly tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

Key Features

Work Item Orchestration — List all construction tasks and work items, and fetch detailed metadata natively.
Booking Intelligence — Retrieve and inspect scheduling bookings to stay updated on team allocation flawlessly.
Invoice Management — Access billing data and individual invoices to track project financials flawlessly.
Article Access — Query your catalog of articles, materials, and service items natively.
Customer CRM — Access customer profiles and contact details to manage business relationships flawlessly.
Identity Verification — Verify the authorized application and user profile through the agent flawlessly.

The Fieldly MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Fieldly to Pydantic AI via MCP

Follow these steps to integrate the Fieldly MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Fieldly with type-safe schemas

Why Use Pydantic AI with the Fieldly MCP Server

Pydantic AI provides unique advantages when paired with Fieldly through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Fieldly integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Fieldly connection logic from agent behavior for testable, maintainable code

Fieldly + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Fieldly MCP Server delivers measurable value.

Type-safe data pipelines: query Fieldly with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Fieldly tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Fieldly and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Fieldly responses and write comprehensive agent tests

Fieldly MCP Tools for Pydantic AI (11)

These 11 tools become available when you connect Fieldly to Pydantic AI via MCP:

create_work_item

Create a new work item

get_booking

Get details for a specific booking

get_invoice

Get details for a specific invoice

get_me

Get details for the authorized application/user

get_work_item

Get details for a specific work item

list_articles

List all inventory and service articles

list_bookings

List all scheduling bookings

list_customers

List all customers

list_invoices

List all invoices

list_users

List all users in the system

list_work_items

List all work items (jobs/tasks)

Example Prompts for Fieldly in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Fieldly immediately.

"List all active work items in Fieldly."

"Show me the team bookings for tomorrow."

"Check for any unpaid construction invoices."

Troubleshooting Fieldly MCP Server with Pydantic AI

Common issues when connecting Fieldly to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Fieldly + Pydantic AI FAQ

Common questions about integrating Fieldly MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Fieldly MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.