Can the AI add new resources, approve timesheets, or assign projects?

No, this connector is constructed strictly logically explicitly purely as a read-only data extraction protocol naturally seamlessly securely flawlessly organically natively seamlessly explicitly efficiently. It pulls records, reporting scopes organically completely accurately carefully tightly mapped optimally.

What data can the AI retrieve from Runn?

The integration can list projects, people, roles, teams, milestones, and clients. It provides a comprehensive read-only view of your resource planning data.

Can I see project budgets and utilization rates?

Yes. By querying project details, the AI can surface budget allocations, scheduled hours, and utilization metrics for each project in your Runn workspace.

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

Name: Runn
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 Runn 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 Runn "
            "(12 tools)."
        ),
    )

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

asyncio.run(main())

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* 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 Runn MCP Server

Integrate your conversational AI natively with Runn, the premier real-time resource planning and forecasting platform. This integration enables your assistant to pull essential project metadata, capacity bottlenecks, people configurations, team allocations, and timesheet metrics directly into your sessions.

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

What you can do

Analyze Projects & Resources — Extract ongoing engagement details, milestones, and client associations by querying lists natively (list_projects, list_clients). Request detailed readouts of individual operational scopes (get_project).
Audit Roles & Assignments — Find exactly who is assigned to what phase, mapping active allocations accurately (list_assignments, list_phases). Consult team members' details (list_people, get_person) or review globally defined roles (list_roles).
Review Budgets & Actuals — Safely extract reported operational logs (list_actuals) to compare planned work versus billed hours. Account for non-working days naturally via the holidays lists (list_holidays).

The Runn 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 Runn to Pydantic AI via MCP

Follow these steps to integrate the Runn 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 Runn with type-safe schemas

Why Use Pydantic AI with the Runn MCP Server

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

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

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

Runn + Pydantic AI Use Cases

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

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

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

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

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

Runn MCP Tools for Pydantic AI (12)

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

get_person

Retrieves details for a specific person

get_project

Retrieves details for a specific project

list_actuals

Lists actual hours logged (timesheet data)

list_assignments

Lists all resource assignments across projects

list_clients

Lists all clients in the organization

list_holidays

Lists public holidays and non-working days

list_milestones

Lists milestones for a specific project

list_people

Lists all people and resources in Runn

list_phases

Lists project phases for a specific project

list_projects

Lists all projects managed in Runn

list_roles

Lists all defined roles/positions

list_teams

Lists all teams in the workspace

Example Prompts for Runn in Pydantic AI

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

"List all active projects mapped."

"Which team is assigned to the Alpha project next week?"

"What are the upcoming milestones for the Beta project?"

Troubleshooting Runn MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Runn + Pydantic AI FAQ

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