Mingdao Cloud MCP. Talk to your enterprise database via natural language.
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Mingdao Cloud connects your AI agent to an enterprise hyper-application platform, giving it direct read/write access to complex worksheets and records.
Instead of clicking through dozens of tabs, your agent instantly lists available apps (`list_worksheets`), retrieves schemas (`get_worksheet_schema`), finds specific rows, or updates data across different internal systems.
It’s a way to treat structured business data like it's just another conversational topic for your AI client.
What your AI agents can do
Add row
Adds a brand new record to a specified worksheet.
Delete row
Removes an existing record from a worksheet.
Get app info
Retrieves a high-level summary of the entire connected application.
Retrieves the exact field schema for any worksheet, letting you know what columns are available and their data types.
Performs full Create, Read, Update, and Delete operations on individual records within a specified worksheet.
Identifies all connected worksheets and overall application metadata available for the AI agent to interact with.
Lists existing automated business workflows, allowing you to understand what processes are running in the background.
Retrieves a list of application users and their associated access rights for management purposes.
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Mingdao Cloud MCP Server: 10 Tools for Data Operations
These ten tools give your AI agent full control over reading, writing, auditing, and managing structured data across all connected worksheets and applications.
019d8459add row
Adds a brand new record to a specified worksheet.
019d8459delete row
Removes an existing record from a worksheet.
019d8459get app info
Retrieves a high-level summary of the entire connected application.
019d8459get row details
Pulls all detailed data for a specific record, given its ID.
019d8459get worksheet schema
Returns the field schema (column names and types) for any worksheet in the application.
019d8459list rows
Fetches a list of records from an entire worksheet, usually used to get IDs or summaries.
019d8459list users
Lists all user accounts associated with the application for management purposes.
019d8459list workflows
Retrieves a list of automated business workflows that are currently defined and running.
019d8459list worksheets
Lists every available worksheet (table) within the connected application.
019d8459update row
Modifies the data in an existing record with a new set of values.
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What you can do with this MCP connector
Forget clicking through a dozen tabs just to read some data. Mingdao Cloud connects your AI agent straight into the back end of your enterprise hyper-application platform. You give your agent direct read and write access to complex worksheets and records, letting it treat structured business data like any other conversational topic.
It’s an operations layer that bypasses the UI entirely.
To get started, you can ask the system what applications are connected by calling list_worksheets, which instantly lists every available worksheet or table. If you need a high-level overview of the whole setup, use get_app_info to pull a summary of the entire connected application's metadata.
When you need to know how the data is organized, you don't have to guess. The agent uses get_worksheet_schema to return the exact field schema for any specific worksheet, telling you precisely what columns exist and what type of data they hold. This lets your AI client understand your structure instantly.
To read records, you can first run list_rows against an entire sheet; this fetches a summary list of all records, perfect for grabbing IDs or quick overviews. If you know the ID of a specific row, calling get_row_details pulls every piece of data associated with that single record. For managing user access, you can call list_users to get a full roster of accounts and their access rights across the application.
When it comes time to modify data—the Create, Read, Update, Delete cycle—your agent has full control. If you need new information entered, running add_row lets you drop a brand new record into a specified worksheet. Need to change something? You use update_row to modify the values in an existing record without touching anything else.
And if data gets stale or obsolete, delete_row removes that specific record from the sheet.
Beyond just managing tables, your agent can audit the underlying business logic. You call list_workflows to get a list of all automated workflows—the processes running in the background that keep things moving. This gives you visibility into what kind of operations are happening without manual intervention. Essentially, this server transforms structured data access from a painful UI exercise into a direct conversation for your AI agent.
How Mingdao Cloud MCP Works
- 1 You first subscribe to the Mingdao Cloud server, inputting your App Key and Secret credentials.
- 2 Your AI client then calls a function (like
list_worksheetsorget_app_info) to discover what data is available in your enterprise system. - 3 Finally, you issue commands like 'Add a new record for John Doe' which the agent translates into specific tool calls (
add_row, providing all necessary parameters).
The bottom line is that it turns complex, multi-layered internal web applications into simple functions your AI client can call.
Who Is Mingdao Cloud MCP For?
This is for the Operations Analyst who spends half their day manually checking data consistency across different sheets and systems. It's for the System Integrator who needs to audit schemas before writing any automation code, or the Department Manager who just wants an AI layer that lets them query project status without having to learn a new dashboard interface.
Uses list_worksheets and then list_rows to pull current data for reconciliation, or uses update_row when they spot an error in a record.
Calls get_worksheet_schema first. This lets them validate the precise field names and required types before building any automated data piping logic.
Uses natural language to request information, allowing the agent to call tools like list_workflows or get_row_details on demand.
What Changes When You Connect
- Stop manual data checks. By using
list_rowsand thenget_row_details, your agent can pull specific records from a worksheet, eliminating the need to manually cross-reference multiple tabs in a spreadsheet just to find one piece of information. - Avoid guesswork on data structure. Before writing any code or asking for an update, call
get_worksheet_schema. This tells you the exact field names and types (like 'date' vs. 'string'), guaranteeing your subsequent calls usingadd_roworupdate_rowwon't fail. - Understand what's running in the background. Need to know why a record changed? Use
list_workflowsto see all defined automation logic, giving you visibility into how the data moves without needing admin access to the underlying workflow editor. - Manage users and permissions easily. The
list_userstool lets your agent pull an overview of who is in the system. This saves time for IT admins who otherwise have to navigate separate user management modules just to check credentials or roles. - Streamline record maintenance. Instead of logging into five different systems, you use
update_row. You give the AI client the ID and the new data point, and it handles making sure that single record is current across all necessary sheets.
Real-World Use Cases
Need to audit a specific project status.
A manager needs to know if Project Alpha has been assigned. Instead of asking an admin, they prompt their agent: 'What is the status of Project Alpha?' The agent uses list_worksheets to find the 'Projects' table, and then calls get_row_details using the project ID. It returns a single answer, skipping all the manual dashboard navigation.
Data entry needs to happen quickly.
A sales rep closes a deal and needs to log it immediately. They tell their agent: 'Add a new lead for Acme Corp with $50k in revenue.' The agent uses add_row, correctly mapping the text prompt into structured data fields, ensuring the record is created accurately without human error.
A piece of data needs fixing.
The finance team finds that a row's revenue amount was mistyped. They ask their agent to fix it. The agent first uses list_rows to confirm the ID, then calls update_row, making sure only the 'Revenue' field changes while leaving all other data intact.
Discovering system limitations.
A developer needs to know if a new column is possible. They ask their agent: 'What fields are available in the Inventory sheet?' The agent doesn't guess; it runs get_worksheet_schema and hands back the official list of field names, preventing them from building faulty code.
The Tradeoffs
Assuming data structure is stable
A developer writes a script that calls get_row_details based on hardcoded column names. When the underlying sheet changes, the code breaks entirely because it can't find 'Client Name'.
→
Always start by calling get_worksheet_schema. This verifies the current structure before you write any read or write logic. If the schema changes, your agent flags it immediately.
Trying to update data without ID confirmation
The user tells the AI: 'Update the row for John Doe.' The system doesn't know which John Doe record you mean, leading to ambiguous or incorrect updates.
→
First, use list_rows to narrow down the records and get the correct unique ID. Then, pass that specific ID when calling update_row. This keeps the transaction locked onto the right data.
Treating everything as a simple list
The user thinks they can just 'get all related info' without knowing if the information is in a separate application. They get incomplete results.
→
Always start with list_worksheets to see every available data source. This ensures your agent knows whether it needs to check the 'Users' sheet or the 'Orders' sheet for the full context.
When It Fits, When It Doesn't
Use this server if your core problem is manipulating structured, relational business data (worksheets and records) that lives within a large, complex enterprise application. You need an agent to act as a universal API wrapper over existing backend logic—that's the sweet spot.
Don't use it if you only need general text processing or knowledge retrieval from unstructured documents; for those tasks, look at RAG-based search tools. Also, don't use it if your data changes constantly and lacks a defined schema; this server relies on the underlying structure being stable enough to read via get_worksheet_schema. If you only need simple user lists without any row interaction, dedicated directory APIs might be simpler, but Mingdao Cloud provides the full lifecycle management (read, write, audit) needed for true operational automation.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mingdao Cloud. 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 server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Finding a specific record shouldn't take 20 clicks across different dashboards.
Right now, finding one piece of data—say, the final revenue amount for Q3—means navigating from the CRM dashboard to the Sales Tracker sheet, then filtering by date range, and finally clicking into a separate 'Summary' tab. It’s tedious, manual work that guarantees human error.
With Mingdao Cloud, your agent handles it all. You just ask: 'What was the Q3 revenue for Project X?' The agent runs `list_worksheets` to find the right source, then uses `get_row_details` on the correct record ID. It gives you the number instantly.
The Mingdao Cloud MCP Server: Control data flow with explicit tools.
You're no longer limited to just reading data. You can tell your agent exactly what needs changing by calling `update_row`. Say, 'Change the status of this order from Pending to Shipped.' The agent doesn't guess; it executes that specific tool call against the correct record.
This gives you full transactional control over records and workflows. It lets you manage data entry (`add_row`) or clean up old entries (`delete_row`) without needing a developer to write boilerplate code every time.
Common Questions About Mingdao Cloud MCP
How do I find out what columns are in the worksheets using get_worksheet_schema? +
You call get_worksheet_schema and pass it the name of the worksheet. The tool returns a structured list that details every column name, its data type (like string or integer), and whether it's required for new records.
Does add_row require me to know all field names? +
Yes. When calling add_row, you must provide values for every required field. Before you do that, run get_worksheet_schema first to ensure you have the exact, current list of column headers.
Can I check if a user has permission using list_users? +
Yes. The list_users tool retrieves all active application users. While it shows who is in the system, you can use that data to determine which accounts need access or management attention.
How do I know what kind of workflows exist? Use list_workflows. +
Running list_workflows gives you a name and description for every automated workflow defined in the system. This lets you audit business logic without ever touching the underlying configuration UI.
When I use list_rows, how does it handle worksheets with thousands of records? +
The tool handles pagination automatically. It doesn't return every record at once; your AI client manages chunking behind the scenes. You process the data in manageable batches so you don't run into API limits.
If I use delete_row, is there a way to confirm or undo the deletion? +
No, deletion via this server is immediate and permanent. The tool requires a specific row ID for execution, so you must be 100% sure of the record before calling it.
How do I make sure that update_row modifies the correct record? +
You absolutely need to provide a unique identifier (like a primary key or row ID). The server won't guess which record you mean, so always use list_rows first to grab the exact ID.
What kind of high-level information does get_app_info give me? +
This tool gives a summary of your entire connected application. It's like checking the system's health dashboard, letting you know the overall capabilities and structure before diving into specific sheets.
How do I find my Mingdao App Key and Sign? +
In your Mingdao application, click the [Application Name] in the top navigation, then go to [API Developer Docs] → [Application Authorization]. You will find your App Key and Sign (App Secret) there.
What is a 'Worksheet' in Mingdao Cloud? +
A Worksheet is the primary data storage unit in Mingdao Cloud, similar to a database table or a sheet in Airtable. You can manage rows within these worksheets using the provided tools.
How do I format the 'controls' data for adding a row? +
The controls parameter should be a JSON array of objects, where each object has a controlId and a value. For example: [{"controlId": "field_1", "value": "Hello"}].
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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