How do I get Insightful API credentials?

Log in to your Insightful dashboard as an Admin, navigate to Settings > API Tokens, and create a new token. Note that the token is only displayed once.

What is the productivity report?

The get_productivity tool provides aggregated data on active, productive, and unproductive time for your employees and teams.

Can I see real-time activity?

Yes, the list_activity tool provides access to recent activity logs recorded in your organization.

Insightful MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Insightful
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Insightful through the 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 Insightful "
            "(10 tools)."
        ),
    )

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

Empower your AI agents to analyze and manage workforce productivity with Insightful (formerly Workpuls). This MCP server allows you to list employees and teams, track project progress, monitor real-time activity, and retrieve productivity and attendance reports directly through the Insightful API. Ideal for automating team management and performance monitoring.

Pydantic AI validates every Insightful tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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.

The Insightful MCP Server exposes 10 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 Insightful to Pydantic AI via MCP

Follow these steps to integrate the Insightful 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 10 tools from Insightful with type-safe schemas

Why Use Pydantic AI with the Insightful MCP Server

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

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

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

Insightful + Pydantic AI Use Cases

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

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

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

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

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

Insightful MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Insightful to Pydantic AI via MCP:

get_attendance

Retrieves aggregated attendance and time tracking reports

get_employee

Retrieves details for a specific employee

get_productivity

Retrieves aggregated productivity reports

list_activity

Lists recent employee activity logs

list_employees

Lists all employees in the organization

list_locations

Lists all registered organization locations

list_projects

Lists all active and past projects

list_tasks

Lists all tasks across projects

list_teams

Lists all organization teams

list_webhooks

Lists all configured webhooks

Example Prompts for Insightful in Pydantic AI

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

"List all employees in the 'Engineering' team."

"Show me the productivity report for last week."

"Check recent activity logs for project ID '789'."

Troubleshooting Insightful MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Insightful + Pydantic AI FAQ

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