How do I obtain my FieldAware API Key?

You can request or generate your API Key from your FieldAware account administrator or within the **Developer/API Settings** section of your FieldAware portal.

What is the format for the API Key in the settings?

The agent automatically handles the 'Token' prefix. You only need to paste the literal API key provided by FieldAware.

Can I create new jobs through this agent?

Yes! The `create_job` tool allows you to programmatically generate new work orders by providing a customer ID and description.

FieldAware MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: FieldAware
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect FieldAware 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 FieldAware "
            "(12 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in FieldAware?"
    )
    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 FieldAware MCP Server

FieldAware is a comprehensive field service management platform. This MCP server allows your AI agent to interact with your FieldAware account flawlessly.

Pydantic AI validates every FieldAware tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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

Job Orchestration — List all active jobs and fetch detailed metadata for specific work orders natively.
Customer Intelligence — Access customer profiles and contact details to personalize service interactions flawlessly.
Invoice Management — Retrieve and inspect invoices to stay updated on billing and payments synchronously.
Asset Tracking — List managed assets and equipment to ensure your field team has the right context natively.
Quote & Item Access — Query active quotes and your product/service catalog flawlessly through the agent.
Identity Verification — Verify the authorized user and permissions for the current API key flawlessly.

The FieldAware MCP Server exposes 12 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 FieldAware to Pydantic AI via MCP

Follow these steps to integrate the FieldAware 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 12 tools from FieldAware with type-safe schemas

Why Use Pydantic AI with the FieldAware MCP Server

Pydantic AI provides unique advantages when paired with FieldAware 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 FieldAware integration code

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

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

FieldAware + Pydantic AI Use Cases

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

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

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

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

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

FieldAware MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect FieldAware to Pydantic AI via MCP:

create_job

Create a new job

get_customer

Get details for a specific customer

get_invoice

Get details for a specific invoice

get_job

Get details for a specific job

get_whoami

Identify the user associated with the current API key

list_assets

List all assets

list_contacts

List all contacts

list_customers

List all customers

list_invoices

List all invoices

list_items

List all items (products/services)

list_jobs

List all jobs

list_quotes

List all quotes

Example Prompts for FieldAware in Pydantic AI

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

"List all my active jobs in FieldAware."

"Show me the contact info for customer ID 12345."

"Check if there are any unpaid invoices."

Troubleshooting FieldAware MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

FieldAware + Pydantic AI FAQ

Common questions about integrating FieldAware 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 FieldAware MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.