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 CrewAI 10 tools — connect in under 2 minutes

Name: ShadowBot
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to ShadowBot through the Vinkius — pass the Edge URL in the `mcps` parameter and every ShadowBot tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="ShadowBot Specialist",
    goal="Help users interact with ShadowBot effectively",
    backstory=(
        "You are an expert at leveraging ShadowBot tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in ShadowBot "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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 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.

When paired with CrewAI, ShadowBot becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call ShadowBot tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the ShadowBot MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 10 tools from ShadowBot

Why Use CrewAI with the ShadowBot MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ShadowBot through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

ShadowBot + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries ShadowBot for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries ShadowBot, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain ShadowBot tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries ShadowBot against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

ShadowBot MCP Tools for CrewAI (10)

These 10 tools become available when you connect ShadowBot to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

ShadowBot + CrewAI FAQ

Common questions about integrating ShadowBot MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.