Can I automatically trigger an RPA task via the AI agent?

Yes! Use the `trigger_task` tool with the target Robot ID and any required input parameters. Your agent will start the automation job and return the unique Job ID.

How do I check if a robot is currently online and ready?

Use the `list_robots` tool. It will retrieve all configured robots in your ShadowBot account along with their current status (e.g., Online, Busy, Offline).

Can I retrieve the logs for a completed automation job?

Yes! The `get_task_logs` tool allows you to fetch the execution details and any output data for a specific Job ID.

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

Name: ShadowBot
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 ShadowBot 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 ShadowBot "
            "(10 tools)."
        ),
    )

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

Connect your AI agents to ShadowBot (影刀RPA), the leading Robotic Process Automation (RPA) platform for high-performance browser and desktop automation. This MCP provides 10 tools to manage automation robots, orchestrate execution tasks, and monitor the health of your digital workforce programmatically.

Pydantic AI validates every ShadowBot tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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.

What you can do

Robot Orchestration — List and inspect available automation robots and their current operational status
Task Execution — Trigger specific automation tasks and handle job lifecycle management from start to finish
Performance Monitoring — Retrieve granular execution logs and track robot throughput and success rates
Credential Handling — Monitor and manage robotic account assignments and access tokens for secure automation
Global Management — Access organizational project structures and list active automation workflows directly from your agent

The ShadowBot 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 ShadowBot to Pydantic AI via MCP

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

Why Use Pydantic AI with the ShadowBot MCP Server

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

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

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

ShadowBot + Pydantic AI Use Cases

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

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

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

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

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

ShadowBot MCP Tools for Pydantic AI (10)

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

get_robot_details

Get detailed information for a specific robot

get_task_details

Retrieve the status and results of a ShadowBot task

list_apps

List all RPA applications in your ShadowBot account

list_department_members

List members in a specific department

list_departments

Retrieve the organizational department list

list_online_robots

List currently online robots

list_robots

List all robots associated with the account

list_task_logs

Retrieve logs for a specific task

start_task

Remote trigger a ShadowBot RPA application

stop_task

Stop a running ShadowBot task

Example Prompts for ShadowBot in Pydantic AI

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

"List all automation robots in my ShadowBot account and show their status."

"Start task 'process_invoices' on robot ID 'bot_rpa_777'."

"Get the execution logs for Job ID 'job_12345'."

Troubleshooting ShadowBot MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ShadowBot + Pydantic AI FAQ

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