ShadowBot MCP for AI. Trigger complex RPA jobs or check robot health programmatically.
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ShadowBot MCP connects your AI agent directly to a professional Robotic Process Automation (RPA) platform. It lets you programmatically manage digital workflows: list robots, trigger browser-based tasks, check job status, and monitor performance logs—all via API calls.
Instead of clicking through dashboards, your agent handles the entire execution lifecycle.
What your AI can do
Get robot details
Gets specific details about one automation robot in your account.
Get task details
Checks the current status and final results for a specific RPA task job.
List apps
Retrieves a list of all available RPA applications within your ShadowBot account.
List all available automation robots, check which ones are online right now, or get deep details on a specific bot.
Remotely start a new automated job (triggering the workflow) or force-stop an existing task that's running too long.
Fetch the detailed execution logs for any completed or failed job, confirming exactly what happened during the automation run.
List departments in your company structure or list which specific team members belong to a department.
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ShadowBot MCP Server: 10 Tools for RPA Orchestration
Use these ten tools to manage the complete lifecycle of automated processes, from listing available robots to triggering specific jobs and retrieving detailed logs.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using ShadowBot on VinkiusGet Robot Details
Gets specific details about one automation robot in your account.
Get Task Details
Checks the current status and final results for a specific RPA task job.
List Apps
Retrieves a list of all available RPA applications within your ShadowBot account.
List Departments
Pulls a list of all organizational departments configured in ShadowBot.
List Department Members
Lists the specific members who belong to a selected department.
List Online Robots
Returns an immediate list of robots that are currently active and online.
List Robots
Retrieves a comprehensive list of all automation bots associated with the account.
List Task Logs
Fetches detailed execution logs for a specific, identified task job ID.
Start Task
Remotely triggers and begins running a defined RPA application or workflow.
Stop Task
Immediately halts the execution of any currently running ShadowBot task job.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The ShadowBot integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with ShadowBot, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ShadowBot. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 10 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Debugging automated processes shouldn't require logging into the console.
Right now, if a bot fails—say it runs into an unexpected login page or a bad spreadsheet column—the process stops. You have to open the ShadowBot Console, find the job ID in the logs, click through several tabs, and manually piece together what happened: was it the data that broke it, or did the script itself fail? It's slow, and you lose context.
With this MCP server, your agent handles all that complexity. You simply tell it to check the job status. The agent uses `get_task_details` and `list_task_logs`, giving you a clean, structured output right in the chat. It cuts out the manual investigation entirely.
ShadowBot MCP Server: Control execution with start_task.
Before this server, running a specialized automation job meant filling out forms on a web portal or calling an IT team to initiate the task. You couldn't integrate that trigger into your main workflow; it was always a separate step.
Now, you can embed the execution directly into your agent conversation. By using `start_task`, you treat running a complex bot like sending a simple command. It gives you immediate control and verifiable proof of initiation.
What your AI can actually do with this
ShadowBot MCP connects your AI client directly to ShadowBot, letting your agent run professional Robotic Process Automation (RPA) tasks. You don't gotta click through dashboards; you just call the API, and your agent handles the whole job lifecycle. This server gives your AI agent control over everything—from listing every bot on the network to stopping a task that’s gone rogue.
Checking Robot Status
You can list all available RPA applications using list_apps or pull up a full roster of every automation robot associated with the account via list_robots. Need to know who's running right now? You call list_online_robots for an immediate rundown of active bots. If you gotta dig into one specific bot, use get_robot_details to fetch its deep specs.
It’s all about knowing what’s live and where.
Controlling Task Execution
Want a job done? You start it remotely by triggering a workflow with start_task. If that task runs too long or hits a snag, you don't gotta wait—you use stop_task to halt the execution immediately. After the fact, you track down exactly what happened using get_task_details to check the job’s final status and results.
For the nitty-gritty details, you fetch detailed logs for any completed or failed job ID with list_task_logs. This gives you a perfect audit trail of every step the bot took.
Managing Organizational Data
The server lets your agent manage company structure data too. You can pull up a list of all departments configured in ShadowBot using list_departments, or if you know which department it is, you'll get a specific member roster by calling list_department_members. This means your AI agent doesn't just handle bots; it knows who belongs where.
Overall, this MCP server gives your agent the ability to programmatically manage an entire digital workforce. It lets you list departments and pull up members, check every robot’s status from online counts to specific details, start or stop any task job, and get detailed logs for every single execution.
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Here's how it actually works
The bottom line is: it lets your AI client treat complex RPA workflows like any other API call, giving you full remote control over the digital processes.
First, subscribe to the ShadowBot MCP Server and get your AppKey and AppSecret from the console's API settings.
Second, pass those credentials into your agent's context. The agent then uses a listing tool (e.g., list_robots) to understand the available workforce.
Third, you instruct the agent to perform an action—like triggering a job using start_task or retrieving logs via list_task_logs. The result is passed back to your prompt.
Who is this actually for?
This server targets Ops Engineers who spend too much time manually checking dashboard status. It's for Business Analysts who need hard evidence of process execution before reporting. If your job involves running, monitoring, or troubleshooting automated browser-based tasks in an enterprise environment, you need this.
You use the tools to programmatically trigger complex bots and monitor their execution state, treating the entire workflow as a callable function.
Your job is keeping the digital workforce healthy. You run status checks using list_online_robots or analyze failures by calling get_task_details.
You need audit trails. Instead of screenshots, you call list_task_logs to pull the exact data and success metrics for reporting purposes.
What Changes When You Connect
Get instant visibility into your digital workforce. Use list_online_robots to see which bots are running right now, eliminating the need to navigate through a separate monitoring dashboard.
Full job control from chat. Instead of waiting for an operator, your agent can execute critical workflows instantly by calling start_task with minimal input.
Pinpoint failures fast. If a task fails, don't guess what went wrong. Call list_task_logs to grab the exact sequence of events and error messages immediately.
Manage credentials securely without leaving your agent environment. You can monitor account assignments using internal tools, keeping sensitive access tokens managed programmatically.
Understand system scope instantly. Use list_apps or list_departments to map out what resources exist in the organization before you write a single line of code.
Kill runaway processes safely. If a bot gets stuck in a loop, don't restart the whole thing—call stop_task and contain the failure immediately.
See it in action
The Daily Status Check
An Operations Manager needs to know if their critical data processing bots are active. They ask their agent, 'Which robots are online?' The agent calls list_online_robots and returns a clean list of active units and their status.
Debugging a Failure
A Business Analyst finds an error in yesterday’s reports. Instead of waiting for IT, they tell the agent to 'Show me the logs for job ID X.' The agent calls list_task_logs and provides the full log output instantly, allowing immediate root cause analysis.
On-Demand Execution
A specific payroll report needs running right after month-end close. Instead of submitting a ticket, the agent receives the prompt 'Run the invoice processing task.' It calls start_task, initiating the job and providing a Job ID for tracking.
Scope Mapping
A new team is being onboarded. A manager asks the agent to list all teams involved in the 'Finance' department. The agent uses list_departments followed by list_department_members, providing a complete roster for immediate review.
The honest tradeoffs
Assuming real-time data
The developer just asks the agent to 'tell me if the bot is running.' This vague prompt doesn't provide enough context or tools for a definite answer.
Always specify your intent. To check status, call list_online_robots. If you need specific details on a known job ID, use get_task_details.
Over-relying on manual listing
The user manually lists all applications and then tries to guess the correct IDs needed for triggering a task.
Use list_apps first. This returns the approved application list, helping you confirm the exact name or ID required before calling start_task.
Ignoring job lifecycle
The user triggers a task and assumes it's finished. They then ask for logs immediately without checking status.
Always check the state first. Run get_task_details on the Job ID to confirm the completion status before attempting to retrieve logs with list_task_logs.
When It Fits, When It Doesn't
Use this server if your core business process relies on automating browser interactions or managing a defined set of digital robots. You need programmatic control over task execution—specifically, triggering and monitoring jobs using tools like start_task, stop_task, or checking logs with list_task_logs.
Don't use this if your primary need is simple data querying (like fetching a single record by ID) or basic message passing. For those cases, look for dedicated database connectors or messaging tools. This server manages actions and processes, not just static data retrieval.
Questions you might have
How do I check if my robot is currently working with get_robot_details? +
Use get_robot_details to pull specific, deep information on a single bot. If you just want to know what's active right now, it’s better to use the list_online_robots tool first.
What is the difference between list_task_logs and get_task_details? +
get_task_details gives you the high-level status (running, completed, failed). list_task_logs provides the granular log output—the actual step-by-step record of what happened during execution.
Can I trigger a task without knowing its ID? How do I start_task? +
You don't need the Job ID to start. The start_task tool accepts parameters defining the application and workflow you want to run, initiating the process remotely.
If a task is running too long, how do I stop it? Does 'stop_task' work? +
Yes. If a bot gets stuck or hits an infinite loop, call stop_task. This sends a signal to the orchestrator that immediately halts the job.
How do I see all my available bots? Should I use list_robots or list_apps? +
list_robots shows every bot instance connected to your account. list_apps lists the master applications that contain those robots, giving you a higher-level view of capability.
Before I run a task using `start_task`, how do I check available organizational scopes with `list_departments`? +
It gives you the full list of departments. This is critical because many automation workflows require an active department ID to function correctly. You need this scope information before triggering any tasks.
If a task fails unexpectedly, how do I use `get_task_details` to find out why? +
It provides the immediate status and error code summary of a failed job. This is faster than diving into raw logs because it isolates the failure point right away.
How do I verify which specific robotic account tokens are active when using `get_robot_details`? +
By calling this tool, you get detailed info on a robot's current credentials and status. This lets you confirm if the assigned access token is valid before attempting complex tasks.
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.
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